tag:theconversation.com,2011:/us/topics/high-frequency-trading-1611/articlesHigh frequency trading – The Conversation2023-05-18T13:09:46Ztag:theconversation.com,2011:article/2014362023-05-18T13:09:46Z2023-05-18T13:09:46ZChatGPT-powered Wall Street: The benefits and perils of using artificial intelligence to trade stocks and other financial instruments<figure><img src="https://images.theconversation.com/files/526640/original/file-20230516-23-zv2vps.jpg?ixlib=rb-1.1.0&rect=121%2C80%2C4372%2C2910&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Markets are increasingly driven by decisions made by AI. </span> <span class="attribution"><a class="source" href="https://www.gettyimages.com/detail/photo/robot-analyze-stock-royalty-free-image/930658900?phrase=ai%20trading">PhonlamaiPhoto/iStock via Getty Images</a></span></figcaption></figure><p>Artificial Intelligence-powered tools, such as ChatGPT, have the potential to revolutionize the efficiency, effectiveness and speed of the work humans do. </p>
<p>And this is true in financial markets as much as in sectors like <a href="https://www.healthcareittoday.com/2023/05/01/chatgpt-may-not-be-ready-to-revolutionize-the-healthcare-industry-quite-yet-but-theres-promise-for-the-future/">health care</a>, <a href="https://www.technologyreview.com/2023/03/25/1070275/chatgpt-revolutionize-economy-decide-what-looks-like/">manufacturing</a> and pretty much every other aspect of our lives.</p>
<p><a href="https://scholar.google.com/citations?hl=en&user=ktowyKoAAAAJ">I’ve been researching financial markets</a> and algorithmic trading for 14 years. While AI offers lots of benefits, the <a href="https://blog.kryll.io/the-impact-of-artificial-intelligence-on-algorithmic-trading-and-financial-markets">growing use of these technologies</a> in financial markets also points to potential perils. A look at Wall Street’s past efforts to speed up trading by embracing computers and AI offers important lessons on the implications of using them for decision-making. </p>
<h2>Program trading fuels Black Monday</h2>
<p>In the early 1980s, <a href="https://www.cftc.gov/About/HistoryoftheCFTC/history_1980s.html">fueled by advancements in technology</a> and financial innovations such as derivatives, institutional investors began using computer programs to execute trades based on predefined rules and algorithms. This helped them complete large trades quickly and efficiently.</p>
<p>Back then, these algorithms were relatively simple and were primarily used for <a href="https://www.investopedia.com/terms/i/indexarbitrage.asp#:%7E:text=What%20Is%20Index%20Arbitrage%3F,where%20the%20price%20discrepancy%20originates.">so-called index arbitrage</a>, which involves trying to profit from discrepancies between the price of a stock index – like the S&P 500 – and that of the stocks it’s composed of.</p>
<p>As technology advanced and more data became available, this kind of program trading became increasingly sophisticated, with algorithms able to analyze complex market data and execute trades based on a wide range of factors. These program traders continued to grow in number on the largey unregulated trading freeways – on which over a <a href="http://www.nasdaqtrader.com/Trader.aspx?id=MarketShare">trillion dollars worth of assets</a> change hands every day – causing <a href="https://www.nytimes.com/2012/10/19/business/a-computer-lesson-from-1987-still-unlearned-by-wall-street.html">market volatility to increase dramatically</a>.</p>
<p>Eventually this resulted in the <a href="https://www.investopedia.com/ask/answers/042115/what-caused-black-monday-stock-market-crash-1987.asp">massive stock market crash</a> in 1987 known as Black Monday. The Dow Jones Industrial Average suffered what was at the time the biggest percentage drop in its history, and the pain spread throughout the globe.</p>
<p>In response, regulatory authorities <a href="https://www.federalreservehistory.org/essays/stock-market-crash-of-1987">implemented a number of measures to restrict</a> the use of program trading, including circuit breakers that halt trading when there are significant market swings and other limits. But despite these measures, program trading continued to grow in popularity in the years following the crash. </p>
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<a href="https://images.theconversation.com/files/526623/original/file-20230516-25-uk0ohe.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="a bunch of black and white newspaper front pages are layered on top of each other with words like panic and crash and wall street" src="https://images.theconversation.com/files/526623/original/file-20230516-25-uk0ohe.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/526623/original/file-20230516-25-uk0ohe.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=951&fit=crop&dpr=1 600w, https://images.theconversation.com/files/526623/original/file-20230516-25-uk0ohe.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=951&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/526623/original/file-20230516-25-uk0ohe.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=951&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/526623/original/file-20230516-25-uk0ohe.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=1195&fit=crop&dpr=1 754w, https://images.theconversation.com/files/526623/original/file-20230516-25-uk0ohe.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=1195&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/526623/original/file-20230516-25-uk0ohe.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=1195&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
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<span class="caption">This is how papers across the country headlined the stock market plunge on Black Monday, Oct. 19, 1987.</span>
<span class="attribution"><a class="source" href="https://newsroom.ap.org/detail/StockMarketCrashof1987/b84c9c508f1741a6a09f1f7531d96872/photo?Query=black%20monday%20wall%20street&mediaType=photo&sortBy=arrivaldatetime:asc&dateRange=Anytime&totalCount=726&currentItemNo=3">AP Photo</a></span>
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<h2>HFT: Program trading on steroids</h2>
<p>Fast forward 15 years, to 2002, when the New York Stock Exchange introduced a fully automated trading system. As a result, program traders gave way to more sophisticated automations with much more advanced technology: <a href="https://www.investopedia.com/terms/h/high-frequency-trading.asp">High-frequency trading</a>. </p>
<p>HFT uses computer programs to analyze market data and execute trades at extremely high speeds. Unlike program traders that bought and sold baskets of securities over time to take advantage of an arbitrage opportunity – a difference in price of similar securities that can be exploited for profit – high-frequency traders use powerful computers and high-speed networks to analyze market data and execute trades at lightning-fast speeds. High-frequency traders <a href="https://smartasset.com/investing/high-frequency-trading#:%7E:text=High%2Dfrequency%20traders%20can%20conduct,than%20any%20human%20possibly%20could">can conduct trades in approximately one 64-millionth of a second</a>, compared with the several seconds it took traders in the 1980s.</p>
<p>These trades are typically very short term in nature and may involve buying and selling the same security multiple times in a matter of nanoseconds. AI algorithms analyze large amounts of data in real time and identify patterns and trends that are not immediately apparent to human traders. This helps traders <a href="https://www.investopedia.com/articles/active-trading/092114/strategies-and-secrets-high-frequency-trading-hft-firms.asp">make better decisions</a> and execute trades at a faster pace than would be possible manually. </p>
<p>Another important application of AI in HFT <a href="https://www.tradersmagazine.com/am/improving-high-frequency-trading/">is natural language processing</a>, which involves analyzing and interpreting human language data such as news articles and social media posts. By analyzing this data, traders can gain valuable insights into market sentiment and adjust their trading strategies accordingly.</p>
<h2>Benefits of AI trading</h2>
<p>These AI-based, high-frequency traders operate very differently than people do.</p>
<p>The human brain is slow, inaccurate and forgetful. It is incapable of quick, high-precision, floating-point arithmetic needed for analyzing huge volumes of data for identifying trade signals. Computers are millions of times faster, with essentially infallible memory, perfect attention and limitless capability for analyzing large volumes of data in split milliseconds. </p>
<p>And, so, just like most technologies, HFT provides several benefits to stock markets.</p>
<p>These traders typically buy and sell assets at prices very close to the market price, which means they don’t charge investors high fees. This <a href="https://www.theatlantic.com/business/archive/2014/04/everything-you-need-to-know-about-high-frequency-trading/360411/">helps ensure that there are always buyers and sellers</a> in the market, which in turn helps to stabilize prices and reduce the potential for sudden price swings.</p>
<p>High-frequency trading can also help to reduce the impact of market inefficiencies by quickly identifying and exploiting mispricing in the market. For example, HFT algorithms can detect when a particular stock is undervalued or overvalued and execute trades to take advantage of these discrepancies. By doing so, this kind of trading can help to correct market inefficiencies and ensure that assets are priced more accurately. </p>
<figure class="align-center ">
<img alt="a crowd of people move around a large room with big screens all over the place" src="https://images.theconversation.com/files/526642/original/file-20230516-17-ykwk78.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/526642/original/file-20230516-17-ykwk78.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=402&fit=crop&dpr=1 600w, https://images.theconversation.com/files/526642/original/file-20230516-17-ykwk78.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=402&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/526642/original/file-20230516-17-ykwk78.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=402&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/526642/original/file-20230516-17-ykwk78.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=505&fit=crop&dpr=1 754w, https://images.theconversation.com/files/526642/original/file-20230516-17-ykwk78.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=505&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/526642/original/file-20230516-17-ykwk78.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=505&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">Stock exchanges used to be packed with traders buying and selling securities, as in this scene from 1983. Today’s trading floors are increasingly empty as AI-powered computers handle more and more of the work.</span>
<span class="attribution"><a class="source" href="https://newsroom.ap.org/detail/NYSEOptimism/970783fc76fc423b95d384444c6f0ed4/photo?Query=nyse%20trading&mediaType=photo&sortBy=arrivaldatetime:asc&dateRange=Anytime&totalCount=427&currentItemNo=4">AP Photo/Richard Drew</a></span>
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<h2>The downsides</h2>
<p>But speed and efficiency can also cause harm. </p>
<p>HFT algorithms can react so quickly to news events and other market signals that they can cause sudden spikes or drops in asset prices. </p>
<p>Additionally, HFT financial firms are able to use their speed and technology to gain an unfair advantage over other traders, <a href="http://dx.doi.org/10.2139/ssrn.2816202">further distorting market signals</a>. The volatility created by these extremely sophisticated AI-powered trading beasts led to the so-called flash crash in May 2010, when <a href="https://corporatefinanceinstitute.com/resources/equities/2010-flash-crash/">stocks plunged</a> and then recovered in a matter of minutes – erasing and then restoring about $1 trillion in market value.</p>
<p>Since then, volatile markets have become the new normal. In 2016 research, two co-authors and I found that <a href="https://fred.stlouisfed.org/series/VIXCLS">volatility</a> – a measure of how rapidly and unpredictably prices move up and down – <a href="http://dx.doi.org/10.2139/ssrn.2816202">increased significantly after the introduction of HFT</a>. </p>
<p>The speed and efficiency with which high-frequency traders analyze the data mean that even a small change in market conditions can trigger a large number of trades, leading to sudden price swings and increased volatility. </p>
<p>In addition, <a href="https://ssrn.com/abstract=3774567">research I published</a> with several other colleagues in 2021 shows that most high-frequency traders use similar algorithms, which increases the risk of market failure. That’s because as the number of these traders increases in the marketplace, the similarity in these algorithms can lead to similar trading decisions. </p>
<p>This means that all of the high-frequency traders might trade on the same side of the market if their algorithms release similar trading signals. That is, they all might try to sell in case of negative news or buy in case of positive news. If there is no one to take the other side of the trade, markets can fail.</p>
<h2>Enter ChatGPT</h2>
<p>That brings us to a new world of ChatGPT-powered trading algorithms and similar programs. They could take the problem of too many traders on the same side of a deal and make it even worse. </p>
<p>In general, humans, left to their own devices, will tend to make a diverse range of decisions. But if everyone’s deriving their decisions from a similar artificial intelligence, this can limit the diversity of opinion. </p>
<p>Consider an extreme, nonfinancial situation in which everyone depends on ChatGPT to decide on the best computer to buy. <a href="https://doi.org/10.1016/j.digbus.2021.100018">Consumers are already very prone</a> to herding behavior, in which they tend to buy the same products and models. For example, reviews on Yelp, Amazon and so on motivate consumers to pick among a few top choices.</p>
<p>Since decisions made by the generative AI-powered chatbot <a href="https://www.nytimes.com/2023/03/28/technology/ai-chatbots-chatgpt-bing-bard-llm.html">are based on past training data</a>, there would be a similarity in the decisions suggested by the chatbot. It is highly likely that ChatGPT would suggest the same brand and model to everyone. This might take herding to a whole new level and could lead to shortages in certain products and service as well as severe price spikes. </p>
<p>This becomes more problematic when the AI making the decisions is informed by biased and incorrect information. AI algorithms <a href="https://www.allerin.com/blog/ai-can-reinforce-existing-human-biases">can reinforce existing biases</a> when systems are trained on biased, old or limited data sets. And ChatGPT and similar tools <a href="https://www.fastcompany.com/90833017/openai-chatgpt-accuracy-gpt-4">have been criticized</a> for making factual errors. </p>
<p>In addition, since market crashes are relatively rare, there isn’t much data on them. Since generative AIs depend on data training to learn, their lack of knowledge about them could make them more likely to happen. </p>
<p>For now, at least, it seems most banks won’t be allowing their employees to take advantage of ChatGPT and similar tools. Citigroup, Bank of America, Goldman Sachs and several other lenders <a href="https://www.bloomberg.com/news/articles/2023-02-24/citigroup-goldman-sachs-join-chatgpt-crackdown-fn-reports?utm_source=google&utm_medium=bd&cmpId=google&sref=Hjm5biAW">have already banned their use</a> on trading-room floors, citing privacy concerns.</p>
<p>But I strongly believe banks will eventually embrace generative AI, once they resolve concerns they have with it. The potential gains are too significant to pass up – and there’s a risk of being left behind by rivals.</p>
<p>But the risks to financial markets, the global economy and everyone are also great, so I hope they tread carefully.</p><img src="https://counter.theconversation.com/content/201436/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Pawan Jain does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Wall Street’s history of embracing high-speed algorithmic trading suggests ChatGPT will pose similar – if bigger – risks to financial markets.Pawan Jain, Assistant Professor of Finance, West Virginia UniversityLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1093622019-01-07T16:17:13Z2019-01-07T16:17:13ZFlash crashes: if reforms aren’t ramped up, the next one could spell global disaster<figure><img src="https://images.theconversation.com/files/252648/original/file-20190107-32130-15gq14l.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Flight club. </span> <span class="attribution"><a class="source" href="https://www.shutterstock.com/image-photo/businessman-currency-financial-crisis-concepts-226762159?src=uynuAS5mYZks6yR9PRhKxw-2-88">rawpixel.com</a></span></figcaption></figure><p>In California, people fear the “big one” – an earthquake of such magnitude that it could wipe the state off the map. They look nervously at the intense seismic tremors from previous earthquakes and fear it is only a matter of time. The financial markets have an equivalent to these tremors: flash crashes are temporary market spikes that are a feature of modern automated trading. So far, they have passed quickly and normal business has resumed. Yet that may not be the pattern in future. The worry is that one day soon, a flash crash could bring the global economy to its knees. </p>
<p>Rewind to January 2, when Apple <a href="https://www.apple.com/newsroom/2019/01/letter-from-tim-cook-to-apple-investors/">issued</a> a profit warning, largely thanks to softer demand for Apple devices in China. The Australian dollar, used by traders as a proxy for the Chinese economy, suddenly tumbled 3.5%. Something similar happened with the Japanese yen in the opposite direction. By the end of the Asian trading session, these shifts had rebounded. Yet market watchers were in no doubt: another “flash crash” had just struck. </p>
<h2>Flashes big and small</h2>
<p>The first flash crash that made headlines infamously took place around 2.30pm to 3.00pm Eastern Standard Time on May 6, 2010. The Dow Jones Industrial Average suddenly tanked 10%, causing spectacular upheaval in the US futures and spot markets. A <a href="https://www.sec.gov/news/studies/2010/marketevents-report.pdf">subsequent official report</a> blamed automated trading, often known as algorithmic trading, for starving the market of willing buyers. </p>
<p>What saved the day was the triggering of a circuit breaker on the Chicago Mercantile Exchange, the world’s largest futures market. This stopped the market for just five seconds, but it was enough time for automated traders to discern that prices were artificially low. They duly sent manual purchases which cumulatively helped the markets to recover by driving up prices again. </p>
<p>Flash crashes have since become a more regular occurrence. There have been thousands of mini flash crashes, moving a market by a relatively small amount, but also more major incidents. The highlights are listed in the following table, including the crash of January 2, 2019. This doesn’t include the flash crash of December 5, 2018, which saw a <a href="https://heisenbergreport.com/2018/12/05/whats-behind-the-flash-crash-in-sp-futures/">sudden plunge</a> in S&P 500 E-mini futures, the most traded futures contract in the world. In just three minutes after the day’s opening, these futures plunged 2.5%, only to rebound thanks to another circuit breaker. </p>
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<a href="https://images.theconversation.com/files/252499/original/file-20190104-32121-265vpc.png?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/252499/original/file-20190104-32121-265vpc.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/252499/original/file-20190104-32121-265vpc.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=289&fit=crop&dpr=1 600w, https://images.theconversation.com/files/252499/original/file-20190104-32121-265vpc.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=289&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/252499/original/file-20190104-32121-265vpc.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=289&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/252499/original/file-20190104-32121-265vpc.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=363&fit=crop&dpr=1 754w, https://images.theconversation.com/files/252499/original/file-20190104-32121-265vpc.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=363&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/252499/original/file-20190104-32121-265vpc.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=363&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
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<p>So what is going on? Since around the turn of the century, financial firms have increasingly relied on algorithmic trading. It enables them to take advantage of the superhuman abilities of computers to process huge volumes of data at high speeds. Different operators use different strategies and time horizons, ranging from long-term investments by pension funds and insurance companies to short-term buying and selling by banks and brokers. </p>
<h2>Problem predators</h2>
<p>Most algorithmic trading is perfectly legitimate – indeed, it makes markets more efficient by increasing trading activity. It becomes problematic <a href="https://mechanicalforex.com/2011/01/predatory-trading-the-illegal-way-to-profit-from-someones-edge.html">where it becomes</a> predatory – manipulating other traders by giving a false impression of market demand. </p>
<p>Most predatory strategies involve posting then cancelling orders to buy or sell a security at a better price. Let’s say that a trading algorithm wants to sell 400 Microsoft shares at US$10 per share. The market order book, which records buyer demand, shows outstanding bids for only 50 shares at that price. This could be because, say, most trading is currently taking place at US$9.98. </p>
<p>To try and remedy this, our trading algorithm places a dummy bid for 450 Microsoft shares at US$10 each. Other would-be buyers are lured to place orders at the same price. This increases the number of share orders from, say, 500 to 900. </p>
<p>All this is happening in microseconds, so that none of this share demand has yet found a seller. Our algorithm knows exactly when its 450 order will be satisfied, and cancels just before. Instead, it instantaneously sells 400 shares at US$10 each to the buyers it attracted to the market. This is called spoofing. It was considered legitimate in the days of manual trading, but automated trading speeds have made it too effective. The same goes for other predatory strategies <a href="https://www.sciencedirect.com/science/article/pii/S027553191530026X?via%3Dihub">such as</a> algorithm sniffing, quote stuffing, latency arbitrage and marking the close. Yet with <a href="https://www.businessinsider.com/how-high-frequency-trading-has-changed-the-stock-market-2017-3?r=US&IR=T">more than</a> half of US shares trading automated, for example, the worry is that there is still much predatory behaviour going on. </p>
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<a href="https://images.theconversation.com/files/252678/original/file-20190107-32130-1b75pk1.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/252678/original/file-20190107-32130-1b75pk1.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/252678/original/file-20190107-32130-1b75pk1.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=450&fit=crop&dpr=1 600w, https://images.theconversation.com/files/252678/original/file-20190107-32130-1b75pk1.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=450&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/252678/original/file-20190107-32130-1b75pk1.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=450&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/252678/original/file-20190107-32130-1b75pk1.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=566&fit=crop&dpr=1 754w, https://images.theconversation.com/files/252678/original/file-20190107-32130-1b75pk1.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=566&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/252678/original/file-20190107-32130-1b75pk1.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=566&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
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<span class="caption">Blood in the water?</span>
<span class="attribution"><a class="source" href="https://www.shutterstock.com/image-photo/great-white-shark-smiling-383911420?src=CFqOO2wn2osAeWf6VuebqA-1-35">Ramon Carretero</a></span>
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</figure>
<p>The reason these <a href="https://www.wsj.com/articles/german-bundesbank-high-frequency-trading-can-worsen-flash-crashes-1477306280">can cause</a> or exacerbate flash crashes is that they can encourage herd behaviour – a rash of panic selling, say. This can prompt a particularly sharp price swing at a time when traders are staying out of the market because prices are too volatile – potentially spreading to other markets due to global interconnectedness, as traders begin to think that other prices must be wrong as well. </p>
<p>So far, flash crashes have coincided with a relatively calm bull market. But many <a href="https://edition.cnn.com/2018/11/19/investing/stocks-bear-market-morgan-stanley/index.html">now believe</a> the tide has turned. The FTSE100, for example, <a href="https://www.theguardian.com/business/2018/dec/31/ftse-100-tumbles-by-125-in-2018-its-biggest-fall-in-a-decade">registered</a> its biggest fall in a decade in 2018. In a more depressed market, where there’s inevitably <a href="https://www.macrotrends.net/2603/vix-volatility-index-historical-chart">more volatility</a> and traders are more downbeat, the worry is that flash crashes are more likely to get out of hand – possibly causing contagion around the world. </p>
<h2>How we should respond</h2>
<p>Circuit breakers <a href="https://www.bloomberg.com/quicktake/circuit-breakers">have become</a> much more widespread since 2010, but they don’t stop flash crashes. They merely pause trading – and if traders are feeling downbeat anyway, they may simply carry on panic selling (or buying) when the market reopens. Circuit breakers are also less helpful with securities traded in more than one market, such as currencies. </p>
<p>One alternative answer is more controlled exchanges which are friendlier to manual traders, such as the <a href="https://iextrading.com">Investor Exchange (IEX)</a> in the US. The IEX, established in 2012, offers simultaneous market access to all participants by imposing a 350 microseconds delay on trades. After only a few years of trading, the IEX <a href="https://www.businessinsider.com/theres-been-an-executive-shakeup-at-upstart-stock-exchange-iex-as-it-struggles-to-attract-a-listing-2018-5?r=UK&IR=T">accounts for</a> about 2% of US securities trading. Faced with this new competitor, the New York Stock Exchange <a href="https://www.businessinsider.com/nyse-is-slowing-down-trading-for-a-key-market-2017-1?r=US&IR=T">recently introduced</a> a similar delay, but only for shares of small and mid-sized companies. </p>
<p>Elsewhere, the Tokyo Stock Exchange <a href="https://uk-mobile-reuters-com.cdn.ampproject.org/c/s/uk.mobile.reuters.com/article/amp/idUKKCN18F0TU">has implemented</a> a system of trading checks to discourage manipulative order cancellations. The Italian exchange <a href="https://www.modernmarketsinitiative.org/archive/2018/11/13/this-is-a-test-post">introduced</a> a 0.02% levy on order cancellations beyond a particular threshold. France and Finland <a href="https://www.bnymellon.com/emea/en/_locale-assets/pdf/our-thinking/ftt-globalperspective-brochure-03-2018.pdf">have launched</a> similar systems. </p>
<p>Such interventions <a href="https://www.marketwatch.com/story/flash-crash-rules-made-knight-keep-bad-trades-2012-08-07">definitely</a> reduce the risk of flash crashes. But even put together, only a relatively small proportion of the securities trade has been affected. The system as a whole remains gravely at risk. </p>
<p>With monetary policy tightening around the world; a trade war between the US and China; and stocks still generally expensive, it’s not surprising sentiment has been weakening. Having had two significant flash crashes in less than a month, the Bank of England’s <a href="https://www.reuters.com/article/britain-boe-flashcrash/update-1-bank-of-englands-salmon-says-brace-for-further-flash-crashes-idUSL5N1FE6IF">recent warnings</a> to “brace for future crashes” seem timely. Unless market regulators do more to mitigate these risks, there could be big trouble ahead.</p><img src="https://counter.theconversation.com/content/109362/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Jean-Philippe Serbera does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Automated predatory trading has the potential to bring the world economy to its knees. So why is reform so leisurely?Jean-Philippe Serbera, Senior Lecturer, Sheffield Hallam UniversityLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/734172017-05-03T01:14:23Z2017-05-03T01:14:23ZWhy Dodd-Frank – or its repeal – won’t save us from the next crippling Wall Street crash<p>Republicans <a href="http://www.latimes.com/business/la-fi-dodd-frank-20170504-story.html">appear poised to roll back</a> Wall Street regulations passed after the 2008 financial crisis. Democrats <a href="http://www.cnbc.com/2017/02/07/if-trump-repeals-dodd-frank-it-would-be-a-monumental-mistake-bart-chilton-commentary.html">argue doing so</a> would be a “monumental mistake.” </p>
<p>It’s been framed as a typical fight over regulation. <a href="http://www.latimes.com/business/la-fi-dodd-frank-demoocrats-20170206-story.html">Democrats want more</a> to protect taxpayers and investors from the next crisis; Republicans want less because it <a href="https://www.nytimes.com/2017/02/03/business/dealbook/trump-congress-financial-regulations.html">stifles economic growth</a>. </p>
<p>So who’s right? </p>
<p>Based on our combined 35 years of experience with securities markets and the research we’ve done for our new book, “<a href="https://www.amazon.com/When-Levees-Break-Re-visioning-Regulation/dp/0739196049">When the Levees Break: Re-visioning Regulation of the Securities Markets</a>,” we think both sides are wrong. The issue isn’t about more or less regulation but about the need for a streamlined system that supports 21st-century investing. </p>
<p>If we had our way, the whole system of financial regulation would be burned to the ground and replaced with something entirely different. </p>
<h2>Of bonds and banks</h2>
<p>Before we go any further, let’s clarify what we’re talking about. When we think of financial markets, we tend to jumble securities markets like stocks, bonds and commodities with conventional bank lending such as checking accounts and lines of credit. </p>
<p>The <a href="http://www.cftc.gov/LawRegulation/DoddFrankAct/index.htm">Dodd-Frank Act</a>, for example, was ostensibly focused on regulation of securities markets, but the rules that got the most attention were those that affect the “too big to fail” banks. That those banks straddled both worlds made the market crash life-threatening. </p>
<p>But securities trading, and in particularly derivatives, were at the root of the 2008 financial crisis. For our purposes, when we talk about financial regulation, our focus is on the securities markets. </p>
<h2>How did we get here?</h2>
<p>The financial markets meltdown in the fall of 2008 devastated our economy, but it still <a href="http://online.wsj.com/mdc/public/page/2_3024-djia_alltime.html">pales in comparison</a> with the stock market rout that preceded the Great Depression in October 1929. The Dow Jones Industrial Average <a href="https://finance.yahoo.com/quote/%5EDJI/history?period1=475822800&period2=1493697600&interval=1d&filter=history&frequency=1d">fell</a> 23 percent from Oct. 28 to Oct. 29 that year, compared with a two-day slide of at most half that throughout the 2008 crisis. </p>
<p>After the 1929 crash, lawmakers reacted by passing laws aimed at ensuring investor protection. Two groundbreaking pieces of legislation, passed in 1933 and 1934, <a href="https://www.sec.gov/about/laws/sa33.pdf">required companies</a> to submit quarterly and annual reports and <a href="https://www.sec.gov/about/laws/sea34.pdf">established the Securities and Exchange Commission</a>. These laws form the cornerstone of modern securities markets regulation. </p>
<p>But they were only the beginning. As markets expanded and changed, Congress continued to craft new laws that added more agencies to oversee Wall Street activities. As a result, we have more than two dozen agencies, self-regulatory organizations and exchanges (including the <a href="https://www.cftc.gov">Commodities & Futures Trading Commission</a>, the Treasury and the <a href="https://www.dol.gov/">Departments of Labor</a> and <a href="https://www.justice.gov">Justice</a>), not to mention state securities agencies, all with overlapping regulatory jurisdictions. </p>
<p>Moreover, the laws have been reactionary – rather than visionary – resulting in competing concerns and duplicative audit and enforcement procedures. Not surprisingly, there is largely no coordination or communication between them. </p>
<p>Meanwhile, the SEC – as primary regulator – is bogged down with too many directives, many of which are under- or unfunded. For decades, whenever Congress passed a bill to “regulate” big changes in the markets – from market crashes to “advancements” such as mutual funds and investment advisors – the SEC has been required to add oversight of these new practices to their existing responsibilities. Dodd-Frank, for example, expanded the SEC’s role and called for additional internal audits of existing practices but – like past market-related legislation – failed to include funding for those activities.</p>
<p>Amid all the regulation, investor protection seems to have gotten lost. </p>
<h2>Enter Dodd-Frank</h2>
<p>The severity of the 2008 crash and its economic impact (including investment company failures and unprecedented government bailouts) goaded Congress into action. </p>
<p>In 2010 Democratic lawmakers passed the <a href="https://www.sec.gov/about/laws/wallstreetreform-cpa.pdf">Dodd-Frank Act</a>, <a href="https://corpgov.law.harvard.edu/2010/11/20/the-financial-panic-of-2008-and-financial-regulatory-reform/">the most extensive revision of securities regulation</a> since the 1930s, with the hope that more regulation would prevent another crisis. </p>
<p>Republicans have argued for its repeal ever since, claiming <a href="http://financialservices.house.gov/dodd-frank/">the law</a> and the regulations designed to implement it (<a href="https://www.davispolk.com/Dodd-Frank-Rulemaking-Progress-Report/">many of which are behind schedule</a>) inhibit prosperity. </p>
<p>Both parties are missing the point. The current system of financial regulation is built on how stocks were traded in the 1930s – when computers and algorithmic trading had yet to be a glimmer in a <a href="https://www.merriam-webster.com/dictionary/quant">quant’s</a> eye. To paraphrase the <a href="https://www.youtube.com/watch?v=bAJ3-mbP1pY">Oldsmobile commercial</a>, it’s not your father’s stock market anymore.</p>
<h2>My, how markets have changed</h2>
<p>Financial markets have undergone a fundamental transformation over the past 80 years. </p>
<p>First of all, there are the investors themselves. The mom and pop investor that the SEC was created to protect has by and large been replaced by institutional investors, including quantitative analysts or <a href="http://www.nytimes.com/2010/02/21/business/21shelf.html">“quants”</a> that use complex algorithmic formulas to predict the best trading strategies. In fact, algorithmic trading makes up the <a href="https://www.wired.com/2010/12/ff_ai_flashtrading">majority</a> of volume in today’s markets.</p>
<p>Then there’s the issue of disclosure. Since the dawn of federal securities regulation, lawmakers and regulators have relied on <a href="http://heinonline.org/HOL/Page?handle=hein.journals/wvb118&div=6&g_sent=1&collection=journals">disclosure</a> to protect investors. Public companies are required to disclose volumes of information, from <a href="https://www.sec.gov/news/pressrelease/2015-160.html">financial information</a> to dealings with <a href="https://www.sec.gov/divisions/corpfin/cfannouncements/itr-act2012.htm">Iran</a> and even their <a href="https://www.sec.gov/rules/final/33-8177.htm">Code of Ethics</a>. As a result, <a href="https://www.transactionadvisors.com/insights/considering-ipo-costs-going-and-being-public-may-surprise-you">a company can spend</a> <a href="https://www.quora.com/How-much-time-does-a-US-company-typically-spend-on-SEC-filing">over a million dollars each year</a> complying with disclosure regulations that few people actually read. Yet every time there’s a new disaster, Congress piles on the disclosure requirements, as happened with Dodd-Frank. </p>
<p>But for all the hundreds of pages of disclosure, at no time in the past 80 years has there been a mandate to review the actual securities products issued by public companies and investment banks. There are no “safety” standards for stocks, like there are for cars or toasters. The products that brought down the house in 2008 – mortgage-backed securities and products derived from them – continue to be offered to the public, including new ones backed by credit card debt and <a href="https://www.theatlantic.com/business/archive/2013/03/dont-panic-wall-sts-going-crazy-for-student-loans-but-this-is-no-bubble/273682/">student loans</a>.</p>
<p>Finally, the SEC and other regulators are unequipped to keep up with the breathtaking changes in technology, let alone anticipate potential advances and challenges. To understand why, one must only consider the breadth of organizations that have fallen victim to hackers, from <a href="https://www.bloomberg.com/news/articles/2014-03-13/target-missed-warnings-in-epic-hack-of-credit-card-data">Target</a> and <a href="https://www.nytimes.com/2017/03/15/technology/yahoo-hack-indictment.html?_r=0">Yahoo</a> to the <a href="http://www.politico.com/story/2013/06/computer-hacking-veterans-affairs-department-092227">Veterans Administration,</a> and the <a href="http://www.reuters.com/article/us-usa-fed-cyber-idUSKCN0YN4AM">Federal Reserve itself</a>.</p>
<p>Unfortunately, however, Congress <a href="https://cup.columbia.edu/book/how-they-got-away-with-it/9780231156912">does not fund the SEC</a> in a way that would allow it to pay for the skills or systems it needs to keep up with technological and other market advances. Following Dodd-Frank, for example, the SEC’s budget was actually reduced, even as its responsibilities multiplied.</p>
<p>In sum, what we have is a regulatory system that fails in its mission to protect investors. The structure used to oversee current investment practices, corporate disclosures, product development and technological advances is based on the market failures of 1929. It’s a bit like trying to surf the internet using a typewriter. </p>
<h2>Preparing for the next crash</h2>
<p>The next “big” crash will likely be bigger than the last one. So how do we prepare for it? </p>
<p>Dodd-Frank is largely an extension of the patchwork structure and won’t protect us in the future. Yet the Republican answer, to repeal it and let markets self-regulate, won’t stop the proliferation of products that nearly brought the house down in 2008. After the next crash, institutions will not be too big to fail, they’ll be too big to save.</p>
<p>The answer, in our view, is <a href="https://revisioninginvesting.com/">a complete rethinking of how we regulate investing</a>. As the White House moves to dismantle Dodd-Frank, this is the perfect time to do exactly that. Let’s get rid of what doesn’t work – which is pretty much everything – and replace it with a system that does. </p>
<p>What we envision is a contemporary, 21st-century holistic structure built on proactive, thoughtful and streamlined laws that takes into account markets that are technology-driven and move in nanoseconds. </p>
<p>Think of it this way: Our regulatory structure is like the dike that keeps springing leaks – the makeshift plugs we’ve used are so ineffective that the dike isn’t leaking – it’s crumbling. We need to build a new dike, using all available technology, before the next tidal wave hits. </p>
<p>We don’t claim to have all the answers. But we want to get the conversation started. We invite you to join in.</p><img src="https://counter.theconversation.com/content/73417/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>The authors do not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Instead, we need to burn the entire system of financial regulation to the ground and replace it with something that supports investing the way it’s done today.Jena Martin, Professor of Law, West Virginia UniversityKaren Kunz, Associate Professor of Public Administration, West Virginia UniversityLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/684772016-11-28T19:15:51Z2016-11-28T19:15:51ZExplainer: the good, the bad, and the ugly of algorithmic trading<figure><img src="https://images.theconversation.com/files/146716/original/image-20161121-4531-4p4ng4.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Data and algorithms are an integral part of modern trading.</span> <span class="attribution"><span class="source">Shutterstock</span></span></figcaption></figure><p>Algorithms are taking a lot of flak from those in financial circles. They’ve been blamed for a recent <a href="https://www.bloomberg.com/news/articles/2016-10-06/pound-plunges-6-1-percent-in-biggest-drop-since-brexit-result">flash crash in the British pound</a> and <a href="https://www.theguardian.com/business/2015/apr/22/2010-flash-crash-new-york-stock-exchange-unfolded">the greatest fall in the Dow in decades</a>. They’ve been <a href="https://www.ft.com/content/e579ec78-bf50-11e3-b924-00144feabdc0">called a cancer</a> and <a href="http://www.bloomberg.com/news/articles/2014-04-04/is-high-frequency-trading-insider-trading">linked to insider trading</a>.</p>
<p>Government agencies are <a href="http://www.cftc.gov/PressRoom/Events/opaevent_cftcstaff110416">taking notice</a> and are investigating ways to regulate algorithms. But the story is not simple, and telling the “good” algorithms from the “bad” isn’t either. Before we start regulating we need a clearer picture of what’s going on.</p>
<h2>The ins and outs of trading algorithms</h2>
<p>Taken in the widest sense, algorithms are responsible for the vast majority of activity on modern stock markets. Apart from the “mum and dad” investors, whose transactions account for about <a href="http://download.asic.gov.au/media/1343084/rep-215.pdf">15 to 20%</a> of Australian share trades, almost every trade on the stock markets is initiated or managed by an algorithm. </p>
<p>There are many different types of algorithms at play, with different intentions and impacts. </p>
<p>Institutional investors such as super funds and insurance companies rely on <a href="http://www.nasdaqomx.com/digitalAssets/95/95590_execution-algos-member-presentation-october-2014.pdf">execution algorithms</a> to transact their orders. These slice up a large order into many small pieces, gradually and strategically submitting them to the market. The intention is to minimise transaction costs and to receive a good price – if a large order were submitted in one go it might adversely <em>move</em> the entire market. </p>
<p>Human market makers used to provide quotes to buy or sell a given stock and were responsible for maintaining an orderly market. They have been <a href="http://www.bloomberg.com/news/articles/2016-01-26/high-speed-firms-now-oversee-almost-all-stocks-at-nyse-floor">replaced by algorithms</a> that automatically post and adjust quotes in response to changing market conditions. </p>
<p>Algorithms drove the human market makers out of business by being smarter and faster. Most market-making algorithms, however, don’t have an obligation to maintain an orderly market. When the market gets shaky, algorithms can (and do) pull out, which is where the potential for “<a href="http://www.investopedia.com/terms/f/flash-crash.asp">flash crashes</a>” starts to appear – a sudden drop and then recovery of a securities market.</p>
<p>Further concerns about algorithmic trading are focused on another kind – proprietary trading algorithms. Hedge funds, investment banks and trading firms use these to profit from momentary price differentials, by trading on statistical patterns or exploiting speed advantages.</p>
<p>Rather than merely optimising a buy or sell decision of a human trader to minimise transaction costs, proprietary algorithms themselves are responsible for the choice of what to buy or sell, seeking to profit from their decisions. These algorithms have the potential to trigger flash crashes.</p>
<h2>Fast vs. slow algorithms</h2>
<p>Proprietary algorithmic traders are often further divided, between “slow” and “fast” (the latter also <a href="http://www.investopedia.com/articles/active-trading/042414/youd-better-know-your-highfrequency-trading-terminology.asp">referred to as “high-frequency” or “low-latency”</a>). </p>
<p>Many traditional portfolio managers use mathematical models to inform their trading. Nowadays such strategies are often implemented using algorithms, drawing on large datasets. Although these algorithms are often faster than human portfolio managers, they are “slow” in comparison to other algorithmic traders.</p>
<p>High-frequency algorithmic trading (HFT) is on the other end of the spectrum, where speed is fundamental to the strategy. These algorithms operate at the microsecond scale, making decisions and racing each other to the market using an array of different strategies. Winning this race can be highly profitable – fast traders can exploit slower traders that are yet to receive, digest or act on new information.<br>
<a href="http://www.cnbc.com/2014/04/02/high-frequency-trading-benefits-investors-advocate.html">Proponents of HFT</a> argue that they increase efficiency and liquidity because market prices are faster to reflect new information and fast market makers are better at managing risks. <a href="https://www.ft.com/content/6d27495e-cbc2-11e3-a934-00144feabdc0">Many institutional investors</a>, on the other hand, argue that HFTs are predatory and parasitic in nature. According to these detractors, HFTs actually reduce the effective liquidity of the stock market and increase transaction costs, profiting at the expense of institutional investors such as superannuation funds. </p>
<h2>The effects of algorithms are complicated</h2>
<p>A <a href="https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=2813870">recent study</a> by Talis Putnins from UTS and Joseph Barbara from the Australian Securities and Exchange Commission (ASIC) investigated some of these concerns. Using ASIC’s unique regulatory data to analyse institutional investor transaction costs and quantify the impacts of proprietary algorithmic traders on these, the study found considerable diversity across algorithmic traders. </p>
<p>While some algorithms are harmful to institutional investors, causing higher transaction costs, others have the opposite effect. Algorithms that are harmful, as a group, increase the cost of executing large institutional orders by around 0.1%. This ends up costing around A$437 million per year for all large institutional orders in the S&P/ASX 200 stocks. </p>
<p>But these effects are offset by a group of traders that significantly decrease those costs by approximately the same amount. The beneficial algorithms provide liquidity to institutional investors by taking the other side of their trades. </p>
<p>They do so not out of the goodness of their little algorithmic hearts, but rather because they earn a “fee” for this service (for example, the difference between the prices at which they buy and sell). What makes these algorithms beneficial to institutions, is that “fee” they charge is lower than the “fee” institutions would face if these algorithmic traders were not present and instead had to trade with less competitive or less efficient liquidity providers, such as humans. The ability for algorithms to provide liquidity more cheaply comes from the use of technology, as well as increased competition. </p>
<p>What distinguishes the algorithms is that the beneficial ones trade against institutional investors (serving as their <a href="http://www.investopedia.com/terms/c/counterparty.asp">counterparties</a>), whereas the harmful ones trade with the institutions, competing with them to buy or sell. In doing so, the beneficial algorithms reduce the market impact of institutional trading. This allows institutions to get into or out of positions at more favourable prices.</p>
<p>The study also found that high-frequency algorithms are not more likely to harm institutional investors than slower algorithms. This suggests institutional investor concerns about HFT may be misdirected. </p>
<h2>We shouldn’t stamp out the ‘good’ algorithms</h2>
<p>ASIC is now using the tools developed in the <a href="https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=2813870">Putnins and Barbara study</a> to detect harmful algorithms in its surveillance activities. These are identified by looking for statistical patterns in the trading activity of individual algorithmic traders and the variation in institutional transaction costs. The result is an estimated “toxicity” score for every algorithmic trader, with the highest-scoring traders attracting the spotlight. </p>
<p>So, we know the affect of algorithms is complicated and we can start to tell the harmful apart from the beneficial. Regulators need to be mindful of this diversity and avoid blanket regulations that impact all algorithmic traders, including the good guys. Instead, they should opt for more targeted measures and sharper surveillance tools that place true misconduct in the cross-hairs.</p><img src="https://counter.theconversation.com/content/68477/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Talis Putnins receives funding from the Centre for International Finance and Regulation (CIFR) and the Australian Research Council (ARC).</span></em></p><p class="fine-print"><em><span>Marco Navone does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Government agencies are investigating how to start regulating trading algorithms. But algorithms are ubiquitous and we need to make sure we don’t stamp out good ones.Marco Navone, Senior Lecturer in Finance, University of Technology SydneyTalis Putnins, Professor of Finance, University of Technology SydneyLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/619492016-08-22T20:14:25Z2016-08-22T20:14:25ZQuantamentals, signal surfing and fast walkers: mapping the rise of weird data<figure><img src="https://images.theconversation.com/files/134513/original/image-20160817-3597-1a1msae.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">An increasingly diverse array of geospatial, network and time-series data is being used to generate new perspectives and insights. Here we see air traffic in England and Wales visualised over satellite images.</span> <span class="attribution"><a class="source" href="https://www.flickr.com/photos/natspressoffice/13085091395/">https://www.flickr.com/photos/natspressoffice/</a>, <a class="license" href="http://creativecommons.org/licenses/by-sa/4.0/">CC BY-SA</a></span></figcaption></figure><p>One of the lesser understood aspects of what you can do with massive stockpiles of data is the ability to use data that would traditionally have been overlooked or in some cases even considered rubbish. </p>
<p>This whole new category of data is known as “exhaust” data – data generated as a byproduct of some other process. </p>
<p>Much financial market data is a result of two parties agreeing on a price for the sale of an asset. The record of the price of the sale at that instant becomes a form of exhaust data. Not that long ago, this kind of data wasn’t of much interest, except to economic historians and regulators. </p>
<p>A massive moment-by-moment archive of prices of shares and other securities sales prices is now key to many major banks and hedge funds as a “training ground” for their machine-learning algorithms. Their trading engines “learn” from that history and this learning now powers much of the world’s trading. </p>
<p>Traditional transactions such as house price sales history or share trading archives are one form of time-series data, but many other less conventional measures are being collected and traded too. </p>
<p>There are also other categories of unconventional data that are not time-series-based. For example, network data outlines relationships and other signals from social networks, geospatial data lends itself to mapping, and survey data concerns itself with people’s viewpoints. Time series or longitudinal data is, however, the most common form and the easiest to integrate with other time-series data. </p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/134515/original/image-20160817-3602-yncphj.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/134515/original/image-20160817-3602-yncphj.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=433&fit=crop&dpr=1 600w, https://images.theconversation.com/files/134515/original/image-20160817-3602-yncphj.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=433&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/134515/original/image-20160817-3602-yncphj.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=433&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/134515/original/image-20160817-3602-yncphj.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=544&fit=crop&dpr=1 754w, https://images.theconversation.com/files/134515/original/image-20160817-3602-yncphj.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=544&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/134515/original/image-20160817-3602-yncphj.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=544&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">Location data from mobile phones means many companies now have people-movement data.</span>
<span class="attribution"><span class="source">https://www.flickr.com/photos/bouldair</span></span>
</figcaption>
</figure>
<p>Consistent Longitudinal Unconventional Exhaust Data or CLUE data sets, as I’m calling them, are many, varied and growing. They include: </p>
<ul>
<li> foot traffic data
</li><li> consumer spending data
</li><li> satellite imaging data
</li><li> biometrics
</li><li> ecommerce parcel flow data
</li><li> technology usage data
</li><li> employee satisfaction data.
</li></ul>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/134506/original/image-20160817-3578-i7gp6h.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/134506/original/image-20160817-3578-i7gp6h.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=208&fit=crop&dpr=1 600w, https://images.theconversation.com/files/134506/original/image-20160817-3578-i7gp6h.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=208&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/134506/original/image-20160817-3578-i7gp6h.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=208&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/134506/original/image-20160817-3578-i7gp6h.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=261&fit=crop&dpr=1 754w, https://images.theconversation.com/files/134506/original/image-20160817-3578-i7gp6h.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=261&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/134506/original/image-20160817-3578-i7gp6h.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=261&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">Visualisation of footfall data from the past nine years at Glasgow’s Tramway arts venue. Kyle Macquarrie.</span>
<span class="attribution"><span class="source">https://www.flickr.com/photos/53111802@N05/</span></span>
</figcaption>
</figure>
<p>Say, for example, you are interested in the seasonal profitability of supermarkets over time. Foot traffic data may not be the cause of profitability, as more store visitors doesn’t necessarily correlate directly to profit or even sales. But it may be statistically related to volume of sales and so may be one useful clue, just as body temperature is a good clue or one signal to a person’s overall well-being. And when combined with massive amounts of other signals using data analytics techniques, this can provide valuable new insights. </p>
<h2>Rise of ‘Quantamental’ investment funds</h2>
<p>Leading hedge fund Blackrock, for example, is <a href="http://www.afr.com/brand/chanticleer/big-data-is-the-new-frontier-for-active-equity-managers-20160603-gpb8vo">using satellite images of China</a> taken every five minutes to better understand industrial activity and to give it an independent reading on reported data. </p>
<p>Traditionally, there have been two main types of actors in the financial world – traders (including <a href="https://www.technologyreview.com/s/602135/high-frequency-trading-is-nearing-the-ultimate-speed-limit/">high-frequency traders</a>), who look to make money from massive volumes on many small transactions, and investors, who look to make money from a smaller number of larger bets over a longer time. Investors tend to care more about the underlying assets involved. In the case of company stocks, that usually means trying to understand the underlying or fundamental value of the company and future prospects based on its sales, costs, assets and liabilities and so on. </p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/133955/original/image-20160812-16333-j0jvms.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/133955/original/image-20160812-16333-j0jvms.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/133955/original/image-20160812-16333-j0jvms.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=381&fit=crop&dpr=1 600w, https://images.theconversation.com/files/133955/original/image-20160812-16333-j0jvms.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=381&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/133955/original/image-20160812-16333-j0jvms.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=381&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/133955/original/image-20160812-16333-j0jvms.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=479&fit=crop&dpr=1 754w, https://images.theconversation.com/files/133955/original/image-20160812-16333-j0jvms.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=479&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/133955/original/image-20160812-16333-j0jvms.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=479&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Aerial photography from drones and new low-cost satellites are one key new source of unconventional data.</span>
<span class="attribution"><span class="source">https://www.flickr.com/photos/thisisinbalitimur/</span></span>
</figcaption>
</figure>
<p>A new type of fund is emerging that combines the speed and computational power of computer-based Quants with the fundamental analysis used by investors: Quantamental. These funds use advanced machine learning combined with a huge variety of conventional and unconventional data sources to predict the fundamental value of assets and mismatches in the market.</p>
<p>Some of these new style of funds, including <a href="https://www.twosigma.com/">TwoSigma</a> in New York and <a href="https://www.wintoncapital.com/">Winton Capital</a> in London, have been spectacularly successful. Winton was founded by David Harding, a physics graduate from Cambridge University in 1997. After less than two decades it ranks in the [top ten hedge funds worldwide](<a href="http://www.institutionalinvestorsalpha.com/profile/3287866/4689/Hedge-Fund-100-Firm-Profiles.html">http://www.relbanks.com/rankings/top-hedge-funds</a> with US$33 billion in assets under advice and more than 400 people – many with PhDs in physics, maths and computer science. Not far behind and with US$30 billion in assets, TwoSigma also glistens with top tech talent.</p>
<p>New ones are emerging too, including <a href="http://taaffeitecm.com/">Taaffeite Capital Management</a> run by computational biology and University of Melbourne alumnus Professor Desmond Lun. Understanding the complex data dynamics of many areas of natural science, including biology and ecology, are turning out to be excellent training for understanding financial market dynamics. </p>
<h2>Weird data for all</h2>
<p>But it’s not only the world’s top hedge funds that can or are using alternative data. A number of startups are on a mission to democratise access to new sources. Michael Babineau, co-founder and CEO of Bay Area startup <a href="https://secondmeasure.com/">Second Measure</a>, aims to offer a Bloomberg-terminal-like approach to consumer purchase data. This will transform massive amounts of inscrutable text in card statements into more structured data, thus making it accessible and useful to a wide business and investor audience.</p>
<p>Others companies, like <a href="https://mattermark.com/">Mattermark</a> in San Francisco and <a href="https://www.cbinsights.com/">CB Insights</a> in New York, are intelligence services that provide fascinating and valuable data insights into company “signals”. These can be indicators and potential predictors of success — especially in the high-stakes game of technology venture capital investment.</p>
<p>Akin to <a href="http://www.holovaty.com/">Adrian Holovaty’s</a> pioneering work a decade ago mapping crime and many other statistics in Chicago online, <a href="https://www.microburbs.com.au/">Microburbs</a> in Sydney provides a granular array of detailed data points on residential locations around Australia. It allows potential residents and investors to compare schooling, restaurants and many other amenities in very specific neighbourhoods within suburbs.</p>
<p><a href="http://wefeel.csiro.au/">We Feel</a>, designed by CSIRO researcher <a href="http://people.csiro.au/P/C/Cecile-Paris.aspx">Dr Cecile Paris</a>, is an extraordinary data project that explores whether social media – specifically Twitter – can provide an accurate, real-time signal of the world’s emotional state. </p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/134518/original/image-20160817-3592-wopak1.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/134518/original/image-20160817-3592-wopak1.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=175&fit=crop&dpr=1 600w, https://images.theconversation.com/files/134518/original/image-20160817-3592-wopak1.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=175&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/134518/original/image-20160817-3592-wopak1.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=175&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/134518/original/image-20160817-3592-wopak1.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=219&fit=crop&dpr=1 754w, https://images.theconversation.com/files/134518/original/image-20160817-3592-wopak1.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=219&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/134518/original/image-20160817-3592-wopak1.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=219&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">WeFeel is a research tool that creates ‘signals’ data about the emotional mood of people around the world via their tweets.</span>
<span class="attribution"><span class="source">CSIRO</span></span>
</figcaption>
</figure>
<h2>Weird small data has its benefits</h2>
<p>More than simply pop-economics, Freakonomics (2005) showed how unusual yet good-quality data sources can be valuable in creating insights. Assiduous record-keeping of the accounts of an honesty system cookie jar in an office place revealed that people stole most during certain holidays (perhaps due to increased financial and mental stress at these times); access to drug gangster book-keeping accounts explained why many drug dealers live with their grandparents (they are too poor to move out); and massive public school records from Chicago showed parental attention to be a key factor in students’ academic success. </p>
<p>Many of the examples in Freakonomics were based on small quirky data samples. However, as many academics are aware, studies with small samples can present several problems. There’s the question of sampling — whether it’s large enough to represent a robust sample and whether it’s a random selection of the population the study aims to understand. </p>
<p>Then there’s the problem of errors. While one could expect errors to be smaller with smaller sample sizes, <a href="http://www.economist.com/news/science-and-technology/21700620-surprisingly-simple-test-check-research-papers-errors-come-again">a recent meta-study of academic psychology papers</a> found half the papers tested showed significant data inconsistencies and errors. In a small number of cases this may be due to authors fudging the results, whereas others may be due to transcription or other simple mistakes.</p>
<h2>Weird data is getting easier to find</h2>
<p>More and more large-scale unconventional data collections are becoming readily available. There are three blast furnaces driving its proliferation:
</p><ul>
<li>the interaction furnace: our own growing interactions with the web and web services (ecommerce, web mail, social media) etc
</li><li>the transaction furnace: the increasingly online ledger of commerce
</li><li>the automation furnace: an explosion of web-connected sensors.
</li></ul><p></p>
<p>While large data collections can’t help with avoiding fabrication, they can sometimes help with sample size and representation issues. When combined with machine learning they can:
</p><ul>
<li>provide accurate insights from incomplete, noisy and even partially erroneous data
</li><li>offer associations, patterns and connections — blindly with no a priori assumptions
</li><li>help eliminate bias — by invoking multiple perspectives.
</li></ul><p></p>
<h2>What can we expect from more clues?</h2>
<p>We may see unexpected results and be surprised about the degree to which many factors such as social and personal information are highly predictable using unexpected data signals. Michael Kosinski and his colleagues showed the predictive power of social media data in the analysis they published in <a href="http://www.pnas.org/content/110/15/5802.full.pdf">PNAS in 2013</a>. They demonstrated that highly personal traits such as religion, politics and even whether your parents were together when you were 21 were highly predictable using Facebook likes alone. </p>
<p>We will see a plethora of applications emerge that take advantage of processing unconventional data sources. One rich area is biometrics. Australian tech startup <a href="https://www.braingauge.com.au">Brain Gauge</a> has shown that people’s voices can be uses as signal for cognitive load and used for real-time detection of stress levels and reduced absenteeism in call-centre staff, for example. </p>
<p>We can also expect to see a lot more meta-analysis of communities, populations and industries. Increasingly ambitious studies are now possible that combine and link massive, often disparate data sets together to yield new insights into economics, law, health and many other areas of research. One example is the recent meta-study published in the Journal of the American Medical Association that combined nine other studies and found that <a href="http://jama.jamanetwork.com/article.aspx?articleid=644554">walking speed in older adults is indeed a predictor of longevity</a>. </p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/134507/original/image-20160817-3571-1v5idzp.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/134507/original/image-20160817-3571-1v5idzp.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/134507/original/image-20160817-3571-1v5idzp.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/134507/original/image-20160817-3571-1v5idzp.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/134507/original/image-20160817-3571-1v5idzp.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=502&fit=crop&dpr=1 754w, https://images.theconversation.com/files/134507/original/image-20160817-3571-1v5idzp.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=502&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/134507/original/image-20160817-3571-1v5idzp.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=502&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">Walking speed or gait has been confirmed as a good albeit unconventional data signal that is a predictor of longevity.</span>
<span class="attribution"><span class="source">https://www.flickr.com/photos/34536315@N04/</span></span>
</figcaption>
</figure><img src="https://counter.theconversation.com/content/61949/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Paul X. McCarthy is co-founder and CEO of League of Scholars a specialist data analytics based global executive search and recruitment firm for researchers and academics. </span></em></p>People in all manner of professions from economists and real estate agents to stockbrokers and doctors are beginning to recognise the huge potential and power of unconventional data.Paul X. McCarthy, Adjunct Professor, UNSW SydneyLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/610552016-07-05T01:57:53Z2016-07-05T01:57:53ZCan slower financial traders find a haven in a world of high-speed algorithms?<p>It sounds like a scene from “Jurassic World”: fast, agile predators pursue their slower, less nimble prey, as the latter flee for safer pastures. Yet this ecology framework turns out to be an apt analogy for today’s financial markets, in which ultra-fast traders vie for profits against less speedy counterparts. </p>
<p>In fact, the algorithmic traders (known variously as algos, bots and AIs) proliferating in financial markets may well be viewed as an invasive species that has upended the prevailing order in their shared habitat. A <a href="http://www.nature.com/articles/srep02627">2013 article</a> asserts that the financial world has become a “techno-social” system in which human traders are shunted aside, unable to keep up with the bots interacting in a “new machine ecology beyond human response time.” </p>
<p>And in a rapidly evolving world of autonomous traders, past experience may not provide reliable assurance of safety and predictability. The hallmark of a <a href="http://money.cnn.com/2014/05/06/investing/flash-crash-anniversary/">flash crash</a> is lack of an apparent triggering event, generating uncertainty that can further <a href="https://theconversation.com/flash-crash-jitters-what-to-know-about-high-speed-trading-before-the-next-market-disaster-strikes-37446">destabilize markets</a>. </p>
<p>Is the <a href="https://theconversation.com/wall-st-might-not-be-ready-for-a-war-on-high-frequency-trading-61150">regime of algorithmic traders</a> making the financial world more dangerous? How can market innovation and regulations shape this habitat for better or worse? For policy makers, the pressing question is: how can we operate our markets so that they remain stable and efficient amid fundamental technological changes?</p>
<p>In my research on artificial intelligence and strategic reasoning, I’ve been exploring answers to these questions by modeling how the world of trading works.</p>
<h2>‘Latency’ arms race</h2>
<p>What makes this world especially different and unpredictable is the unprecedented speed at which trading bots can respond to information. </p>
<p>A slight edge translates into profit because of the way exchanges match orders. When new information arrives, the first trader to react is able to make money off of slower rivals, while any relative delay or latency of even a fraction of a millisecond can mean no trade and no profit. </p>
<p>This leads inevitably to a latency arms race in which the designers of trading algorithms adopt any available method to shave milliseconds or even microseconds – one millionth of a second – from response time. </p>
<p>Most exchanges and trading forums have catered to the high-frequency traders, providing premium access options and interface features that preserve or enhance the advantage of speed. </p>
<p>An exception is the <a href="https://www.iextrading.com">alternative trading system IEX</a>, featured in <a href="http://www.vanityfair.com/news/2015/03/michael-lewis-flash-boys-one-year-later">Michael Lewis’s Flash Boys</a> and backed by institutional investors, which introduced a 350 microsecond delay on order submission to shield against high-speed bots. On June 17, the <a href="https://www.sec.gov/news/pressrelease/2016-123.html">Securities and Exchange Commission (SEC) approved IEX’s application</a> to operate as a public exchange – rather than only as a private trading platform – against strong opposition by high-frequency traders and competing exchanges. </p>
<h2>Ending the latency race</h2>
<p>But there is another way to neutralize small speed advantages: change the way markets time the matching of buy and sell orders. </p>
<p>Today’s typical market works by matching orders to buy and sell a stock or other asset on a continuous basis. For example, when a trader submits a request to buy a share of Apple at a specific price, the exchange matches it immediately if there is an offer from someone else to sell at the same price or less. This immediacy is what allows a trader able to react more swiftly to new information (say news about the latest iPhone) to profit off of slower rivals. </p>
<p>In a frequent call market, on the other hand, orders to buy and sell are matched at fixed intervals (such as once every second). So our Apple buyer with knowledge of the release of a big improvement in the iPhone wouldn’t be able to get a jump on rivals because her order wouldn’t transact immediately, giving time for others to “catch up.” </p>
<p>By ensuring that speed no longer categorically prevails, the incentive for shaving milliseconds and microseconds is virtually eliminated. Orders within the interval compete instead based on price, leading to a more efficient overall set of trades.</p>
<p>Regulators have taken notice. New York Attorney General Eric Schneiderman <a href="http://www.ag.ny.gov/press-release/ag-schneiderman-calls-new-efforts-eliminate-unfair-advantages-provided-trading-venues">has publicly endorsed</a> the frequent call market – also known as a frequent batch auction – to even the playing field. And SEC Chair Mary Jo White said it could help counter problems with algorithmic trading. </p>
<p>At present, however, no stock exchange operates as a full-fledged frequent call market. One major hurdle to adoption is perception: the view that faster is always better. </p>
<p>Another problem <a href="http://blogs.cfainstitute.org/marketintegrity/2014/11/10/are-frequent-batch-auctions-a-solution-to-hft-latency-arbitrage">that some have raised</a> is that it would only be viable if all exchanges adopted the method simultaneously because otherwise traders would always pick the venue offering the most immediacy. </p>
<p>But is this true? Given the option of trading on either a continuous market or a frequent call market, which one would investors prefer? Or, in the terms of our ecology metaphor, would they flock to the new habitat operating in discrete time intervals or stay in the traditional continuous domains? </p>
<h2>Predator and prey</h2>
<p>To answer this question, in <a href="http://web.eecs.umich.edu/srg/?page_id=1666">research</a> conducted at the University of Michigan, Elaine Wah and I developed a model with two markets, one continuous and the other a frequent call market. </p>
<p>In this model, traders are either fast (think high-frequency) or slow (such as institutional and retail investors). Each trader can choose to buy and sell in one of the two markets and so will prefer to pick the one that offers the highest expected trading gains, taking all others’ behavior as given. </p>
<p>If all the agents are in one market, no individual can benefit by going to the other, as there is nobody to trade with. We therefore focused on market attraction, measured in terms of the prevalence of conditions that would make one trader want to switch. </p>
<p>Our results show that fast traders prefer the continuous market, where they can make the most money, but only when the slow traders are also there. In other words, the predators need their prey in order to be profitable, which means they have a pronounced tendency to follow the slow traders to whichever market they go. </p>
<p>Slow traders, on the other hand, can evade their pursuers by fleeing to the market with fewer fast traders. If the fast traders are prevalent in both markets, then slower ones tend to seek refuge in the frequent call market, which offers some protection from faster traders with better information, as well as generally higher trading gains. </p>
<p>A <a href="http://trust.sce.ntu.edu.sg/aamas16/pdfs/p50.pdf">recent paper</a> by Zhuoshu Li and Sanmay Das from Washington University also found, under quite different assumptions, a tendency for the frequent call market to attract traders away from continuous markets. </p>
<h2>Lessons for exchanges</h2>
<p>What both of these studies suggest is that we may not need a top-down mandate to transform financial markets from continuous to discrete-time trading. Simply making the option available in one or two exchanges may capture the population, as the haven for slow traders can attract both the prey and the predators in pursuit. </p>
<p>High-frequency traders have been relentless in their pursuit of lower latencies and faster access to market-moving information, but ultimately it’s the continuous markets that deserve blame for allowing this predator-prey dynamic to take shape. </p>
<p>Neutralizing the advantage of tiny speed improvements with something like a frequent call market offers a clear-cut solution. The introduction of such a market will provide an attractive haven for investors, and widespread adoption could eventually send the latency arms race the way of the dinosaurs.</p><img src="https://counter.theconversation.com/content/61055/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Michael Wellman receives funding to study the implications of algorithmic trading from the US National Science Foundation and the Future of Life Institute. </span></em></p>New research shines light on whether creating such a haven as a new type of exchange that slows trading down a bit could attract enough traders to be effective.Michael Wellman, Professor of Computer Science & Engineering, University of MichiganLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/611502016-06-22T02:13:27Z2016-06-22T02:13:27ZWall St might not be ready for a war on high-frequency trading<p>Stock markets globally have seen increased fragmentation in recent times as investors seek <a href="http://www.sciencedirect.com/science/article/pii/S0304405X11000390">lower transaction costs</a>, faster execution time of trades and fairer prices. </p>
<p>Now a startup backed by US entrepreneur Eric Ries and some Silicon Valley supporters have initiated talks with the Securities and Exchange Commission (SEC) for a new type of trading venue, dubbed the Long-Term Stock Exchange (LTSE). But could the plan create more problems than it solves by taking aim at high-frequency traders?</p>
<p>The talks come as the SEC last week <a href="https://www.sec.gov/rules/other/2016/34-78101.pdf">approved</a> another new exchange, the <a href="http://www.theverge.com/2016/6/17/11957258/iex-sec-approves-stock-exchange">IEX (Investors’ Exchange)</a>, which aims to create a more level playing field for investors by slowing down high-frequency trading by 350 microseconds.</p>
<p>Whilst the IEX focuses on protecting investors, the LTSE is motivated by younger firms seeking an investor-base who are in it for the long-haul. It’s also designed to shield such startups from short-term volatility in prices due to earnings numbers and fast-traders, better-known as high-frequency traders.</p>
<p>High-frequency trading is considered bad by some as it can reduce <a href="http://www.cfapubs.org/doi/full/10.2469/faj.v69.n2.6">market stability</a> (as seen during the 2010 Flash Crash) and front-running strategies, which increases trading costs for other investors.</p>
<p>Key features of the LTSE include: using the length of time that an investor has held shares to determine shareholder’s voting rights, linking managers’ pay to long-term performance (similar in nature to issuing stock-options as a form of manager compensation) and more detailed disclosure regarding capital expenditure.</p>
<h2>A difficult road</h2>
<p>On the surface the LTSE may seem like an attractive prospect, but a number of challenges stand in the way before such an exchange can become a reality, in particular the role of high-frequency trading and order-protection rules.</p>
<p>An important aim of the LTSE is for listed stocks to have sufficient liquidity (i.e. ease with which you can buy or sell assets). Other aims include fairer prices, as well as allowing stocks to co-list on other US exchanges, all while shielding stock prices from high-frequency traders.</p>
<p>There is ongoing debate regarding whether high-frequency traders are good or bad for markets. But high-frequency traders reportedly account for more than 50% of total trading in US markets. This means they are a key player in supplying liquidity and their quoting activity is a primary mechanism through which <a href="http://papers.ssrn.com/sol3/Papers.cfm?abstract_id=2655927">prices reflect information</a>. The exclusion of HFTs from the LTSE could have serious implications for <a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.2010.01624.x/full">liquidity</a>.</p>
<p>Finance theory teaches us about the law of one price – that is, the same asset should sell for the same price at all locations. The lack of price discovery and trading activity from high-frequency traders may result in unfair prices for LTSE listed-stocks when compared to their cross-listed counterparts. </p>
<p>If LTSE prices are inefficient, order protection rules introduced <a href="https://www.sec.gov/rules/final/34-51808.pdf">by the SEC</a> in 2007 will re-route trades to alternative exchanges to ensure investors get the best price. The potential for unfair prices and lack of liquidity could outweigh the short-term volatility induced by high-frequency traders that supporters of the LTSE wanted to eliminate in the first place.</p>
<p>Then there’s the question of which firms will be brave enough to first list, how much it will cost to list, and whether there is enough demand for the LTSE to survive.</p>
<h2>A long-term proposition</h2>
<p>While trading is beginning to take place away from traditional exchanges around the world, for example in Canada and Europe, there’s still a lack of connection between different trading venues. This will make it difficult for the <a href="http://www.sciencedirect.com/science/article/pii/S0304405X11000390">advantages of US market fragmentation</a> to materialise in other markets, such as from the introduction of a LTSE.</p>
<p>Ries <a href="http://blogs.wsj.com/venturecapital/2011/10/20/lean-startup-leader-advances-to-his-more-wacky-ideas/">first proposed the LTSE concept in 2011</a>. Given the multitude of challenges that exist it could be many more years before we see a LTSE. One only has to look at the red tape and controversy surrounding the <a href="http://www.nytimes.com/2016/06/18/business/dealbook/iex-group-gains-approval-for-stock-exchange.html?_r=0">IEX Group</a> to see the speed humps to innovation.</p><img src="https://counter.theconversation.com/content/61150/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Vinay Patel does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Younger firms are voicing their support for a new type of long-term stock exchange, seeking investors in it for the long-haul.Vinay Patel, Lecturer, Finance, University of Technology SydneyLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/535602016-01-27T19:10:39Z2016-01-27T19:10:39ZEx machina: are computers to blame for market jitters?<figure><img src="https://images.theconversation.com/files/109291/original/image-20160126-19633-11qy0ax.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Is computerised High-Frequency Trading to blame for share market volatility?</span> <span class="attribution"><span class="source">Shutterstock</span></span></figcaption></figure><p>Recent turbulence in the share markets has caused some experts to <a href="http://www.bloomberg.com/gadfly/articles/2016-01-15/robots-are-eating-your-retirement-in-volatile-stock-market">point the finger</a> of culpability at computerised High-Frequency Trading (HFT). There are few complaints about HFT when computers push share markets up, but in the ebbing tide of today’s markets, it’s blamed both for exaggerating the share market dive as well as for the heightened volatility.</p>
<p>The logic behind the fears is this: algorithms and software do not muse about global economic events; they merely chase mechanical patterns that they are programmed to find, such as movements in trend or momentum. They do not make decisions based on real-world eventualities, such as political events.</p>
<p>Can the algorithms express a view on <a href="https://theconversation.com/how-a-chinese-slowdown-will-hit-global-growth-46655">Chinese consumer confidence</a>? The economic impacts of Middle-Eastern <a href="https://theconversation.com/why-saudi-arabia-is-having-such-trouble-with-its-syria-policy-47309">sectarian conflicts</a>? These real world factors aren’t taken into account in the programming of algorithms.</p>
<p>Yet the computers hold substantial sway and can execute a barrage of trades that create unprecedented <a href="http://www.cfapubs.org/doi/pdf/10.2469/cfm.v22.n2.3%22%22">volatility</a> at a rate that human reactions simply cannot match.</p>
<p>What is truly problematic is that the algorithms are not cognisant of when to stop or change a trade and thus can continue to pile money and exaggerate a trade well beyond what the market would consider a correct response. The computers do not have the “<a href="http://www.forbes.com/sites/richardfinger/2013/09/30/high-frequency-trading-is-it-a-dark-force-against-ordinary-human-traders-and-investors/#5b00ecfd51a6">affirmative obligation</a>” to keep the markets orderly.</p>
<p>In fact, this sort of financial competition has been described as “a new world of <a href="http://www.forbes.com/sites/richardfinger/2013/09/30/high-frequency-trading-is-it-a-dark-force-against-ordinary-human-traders-and-investors/#5b00ecfd51a6">a war between machines</a>”.</p>
<p>Research has explained that stock prices <a href="http://papers.ssrn.com/sol3/Papers.cfm?abstract_id=1691679">tend to overreact</a> to news when HFT activity is at a high volume, and that this can have “harmful effects” for capital markets.
Additionally, financial experts have found that HFT “<a href="http://jfin-swufe.springeropen.com/articles/10.1186/s40854-015-0003-8">exacerbates the adverse impacts</a> of trading-related mistakes”, while also leading to “extremely higher market volatility and surprises about suddenly-diminished liquidity”, which in turn “<a href="https://www.fas.org/sgp/crs/misc/R43608.pdf">raises concerns</a> about the stability and health of the financial markets for regulators.”</p>
<p>Officials at the <a href="https://theconversation.com/could-high-frequency-trading-lead-to-our-own-flash-crash-9572%22%22">Australian Securities and Investment Commission</a> have described the possible impact of HFT as “sometimes manipulative or illegal”, but “often predatory”.</p>
<p>In Australia HFT has made significant inroads into the market. In 2015 it accounted for nearly <a href="http://asic.gov.au/about-asic/media-centre/find-a-media-release/2015-releases/15-311mr-asic-publishes-results-of-new-reviews-of-high-frequency-trading-and-dark-liquidity/">one-third</a> of all equity market trades, a level similar to Canada, the European Union, and Japan.</p>
<p>ASIC estimates that HFTs in Australia are collectively earning an <a href="http://download.asic.gov.au/media/3444836/rep452-published-26-october-2015.pdf">not inconsequential</a> $100 million to $180 million annually.</p>
<p>Securities regulators have tolerated HFT so far, but as we may be entering a <a href="https://theconversation.com/market-volatility-is-here-to-stay-but-high-frequency-trading-not-all-bad-46615">“new normal”</a> of higher volatility and with algorithms helping exert a downward pressure on the markets, the regulators may find themselves revisiting the HFT issue.</p>
<p>Australian financial traders may also be put in jeopardy by the sheer magnitude of large foreign-funded HFT players. In recent times, the incursion of HFT into other asset classes such as interest rates futures <a href="http://www.smh.com.au/business/markets/algorithmic-traders-invade-42-trillion-bond-futures-market-20150326-1m8xi4.html">has shown</a> that local traders are being forced out by the computing power of internationally-funded “flash boys”. </p>
<p>Nonetheless, the track record of Australian regulators has been very positive and they <a href="https://theconversation.com/making-sense-of-asics-new-rules-on-dark-liquidity-and-high-frequency-trading-10968">have been proactive</a> about creating mechanisms such as “kill switches” to mitigate potential losses.</p>
<p>From a theoretical standpoint, the proponents of HFT have argued that it provides the most up-to-date information and thus facilitates <a href="https://theconversation.com/flash-crash-jitters-what-to-know-about-high-speed-trading-before-the-next-market-disaster-strikes-37446">price discovery</a>. However, if the algorithms are merely exaggerating sentiments by moving large sums at instantaneous speeds – then they are <a href="http://papers.ssrn.com/sol3/Papers.cfm?abstract_id=1691679">not facilitating price discovery</a> but in fact preventing that goal from being achieved.</p>
<p>The movie, <a href="http://www.nytimes.com/2015/12/11/movies/review-in-the-big-short-economic-collapse-for-fun-and-profit.html?referrer=google_kp&_r=0">The Big Short</a>, based on the book by Michael Lewis, (who also <a href="http://www.nytimes.com/2014/04/06/magazine/flash-boys-michael-lewis.html">wrote about “flash boys”)</a> has infused narratives of the financial world with a “human element”. They have put faces to the names we read about in financial scandals. </p>
<p>However, if HFT grows in size and share markets continue to perform negatively, it may be that the computerised antagonists of finance’s future, the <em>diaboli ex machina</em>, may have no face at all.</p><img src="https://counter.theconversation.com/content/53560/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Usman W. Chohan does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>Computerised High-Frequency Trading (HFC) has been blamed for recent volatility in the share market, does this represent the new normal?Usman W. Chohan, Economist, UNSW SydneyLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/466152015-09-16T20:18:40Z2015-09-16T20:18:40ZMarket volatility is here to stay, but high-frequency trading not all bad<p>The volatility on global equity markets in August was at its highest since 2011. On Black Monday (August 24, 2015), the Dow Jones Industrial Average fell by more than 1,000 points and the S&P500 index plummeted 5.3% in the first four minutes after the opening. During the first 30 minutes, more than two billion shares were traded and, over the morning, the market quickly recovered about half of what was lost during the first four minutes. </p>
<p>The CBOE Volatility Index (VIX) also known as the fear index peaked that day at 40.74. During less stressful times in the market, VIX values are usually below 20. Values greater than 30 are generally associated with high levels of volatility. For example, during the global financial crisis, the index reached an intraday high of 89.53 on October 24, 2008. </p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/94956/original/image-20150916-12018-1tv2c4s.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/94956/original/image-20150916-12018-1tv2c4s.png?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=398&fit=crop&dpr=1 600w, https://images.theconversation.com/files/94956/original/image-20150916-12018-1tv2c4s.png?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=398&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/94956/original/image-20150916-12018-1tv2c4s.png?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=398&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/94956/original/image-20150916-12018-1tv2c4s.png?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=500&fit=crop&dpr=1 754w, https://images.theconversation.com/files/94956/original/image-20150916-12018-1tv2c4s.png?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=500&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/94956/original/image-20150916-12018-1tv2c4s.png?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=500&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">Chicago Board Options Exchange SPX Volatility Index 2014-15.</span>
<span class="attribution"><a class="source" href="http://www.bloomberg.com/quote/VIX:IND">Bloomberg Business</a></span>
</figcaption>
</figure>
<p>The speed of adjustments in the market during the last few weeks have seen many market commentators question whether the higher level of volatility is the “new normal”. For instance, the <a href="http://www.cnbc.com/2015/08/28/trichet-market-volatility-becoming-the-new-norm.html">former European Central Bank President Jean-Claude Trichet</a> suggests that “we have to live now with much higher, high-frequency level volatility”. </p>
<h2>Things change</h2>
<p>What has changed and who are the market participants that are contributing to the high-frequency volatility that we are observing? </p>
<p>The <a href="http://www.theaustralian.com.au/business/financial-services/high-frequency-trading-a-volatile-problem-mike-smith-ian-narev/story-fn91wd6x-1227500321817">chiefs of banking giants Commonwealth Bank and ANZ</a> have laid the blame on high-frequency traders. ANZ chief Mike Smith argues HFT is a problem because it’s moving the market “very, very dramatically both ways”. </p>
<p>High frequency traders use computers and complex algorithms to move in and out of stocks very quickly. These movements are typically milliseconds apart, involving the trading of very large volumes of shares. Some market commentators believe HFT has intensified the recent volatility by causing the market to react rapidly to news that may not be significant. In response to the market swings that we are currently experiencing, some argue that the reactions observed are much more volatile than what is expected. </p>
<h2>HFT and market quality</h2>
<p><a>Doug Cifu</a>, the co-founder of one of the largest electronic market making firms in the world and biggest high-frequency trading firm, Virtu, has defended the role of HFT. Virtu trades about 11,000 financial instruments in 225 markets across 35 countries. Cifu argues that HFT does not cause volatility but absorbs volatility as they participate in the market as a market maker. Market makers help the trading process by acting as the counterparty when others want to trade, and earn a fee in the process. </p>
<p>High-frequency trading firms have argued they provide liquidity to investors and make trading cheaper by reducing spreads between bids and offers across the markets. </p>
<p>My colleagues and I at the University of Western Australia Business School and University of Nagasaki studied the <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2274631">effects of HFT</a> on liquidity on the Tokyo Stock Exchange. We found evidence to support the argument that trading by high-frequency trading firms improves market quality during normal market conditions. This is consistent with <a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.2010.01624.x/abstract">prior research</a> conducted using data from the New York Stock Exchange.</p>
<p>However, we found HFT does not improve market quality during periods associated with high levels of market uncertainty. This is particularly worrisome because high frequency traders appear to consume liquidity when liquidity is needed the most. </p>
<h2>Actions by regulators</h2>
<p>Market operators and regulators have considered different strategies to increase market stability. Some have implemented circuit breakers to halt trading when the market moves by certain percentages, while others have considered imposing transaction taxes on high-frequency traders. </p>
<p>In response to the latest market swings, the <a href="http://atimes.com/2015/09/china-clamps-down-on-algorithmic-trading/">China Financial Futures Exchange (CFFE)</a> took a more drastic response by suspending 164 investors who were found to have high daily trading frequency. According to the China Securities Regulatory Commission (CSRC), the trading by these investors is believed to amplify market fluctuations. </p>
<p>In the US, it is <a href="http://www.marketwatch.com/story/leon-cooperman-says-blame-the-machines-for-market-volatility-2015-09-08">estimated</a> that about three-quarters of daily trading is by HFT and ETFs using “slice and dice” type strategies. In an Australian Securities and Investments Commission <a href="http://download.asic.gov.au/media/1344182/rep331-published-18-March-2013.pdf">report</a> released in 2013, HFT is found to account for 27% of total turnover in S&P/ASX200 securities. </p>
<p>These traders are unlikely to go away. It’s now important for us to get a good understanding of what is the new normal. This is what will help regulators in their tough task of monitoring and ensuring market stability.</p><img src="https://counter.theconversation.com/content/46615/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Marvin Wee receives funding from ACCA/IEAER/IIRC and the KPMG Foundation. </span></em></p>Yes, we’re in a ‘new normal’, so the best thing to do is improve understanding of high-frequency trading.Marvin Wee, Associate Professor, Accounting and Finance, The University of Western AustraliaLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/374462015-02-26T14:09:42Z2015-02-26T14:09:42ZFlash Crash jitters: what to know about high-speed trading before the next market disaster strikes<p>Ask people on the street what mental image they associate with the words “stock exchange,” and you’ll likely hear about a large imposing building in the middle of New York or Chicago. Inside the building there is a huge space crowded with traders in multicolored jackets screaming and gesticulating to each other. </p>
<p>Until ten years ago, that would have been a pretty accurate description of a stock exchange. Today, however, almost all trading is done by algorithms firing digital commands traveling near the speed of light to rows upon rows of computer servers sitting in nondescript suburban warehouses.</p>
<p>The transition from human to electronic trading came with the promise of using faster and cheaper technology to drastically lower the costs of trading shares and to make it much easier to determine the most up-to-date prices for all market participants (commonly known as <a href="http://www.investopedia.com/terms/p/pricediscovery.asp">price discovery</a>).</p>
<figure>
<iframe width="440" height="260" src="https://www.youtube.com/embed/ezJzN_iBgO0?wmode=transparent&start=0" frameborder="0" allowfullscreen=""></iframe>
</figure>
<p>Certainly, for investors who want to buy or sell one hundred shares or a couple of futures contracts, the promise of automation seems to have been realized. They can now trade at lower transaction costs, connect to more buyers or sellers and take advantage of prices that can be discovered around the clock. </p>
<p>But with all that speed, automation and complexity comes the risk that a string of problematic ones and zeros could cause a market meltdown, even if only a temporary one. As both computing power and communication speed continue to grow, the intensity of these disruptive events will only increase as well, making it essential to diagnose the root causes and craft safeguards that prevent or mitigate them. </p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/72676/original/image-20150221-21904-1ywj5p8.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/72676/original/image-20150221-21904-1ywj5p8.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/72676/original/image-20150221-21904-1ywj5p8.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=311&fit=crop&dpr=1 600w, https://images.theconversation.com/files/72676/original/image-20150221-21904-1ywj5p8.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=311&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/72676/original/image-20150221-21904-1ywj5p8.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=311&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/72676/original/image-20150221-21904-1ywj5p8.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=391&fit=crop&dpr=1 754w, https://images.theconversation.com/files/72676/original/image-20150221-21904-1ywj5p8.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=391&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/72676/original/image-20150221-21904-1ywj5p8.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=391&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">The good old days.</span>
<span class="attribution"><span class="source">Shutterstock</span></span>
</figcaption>
</figure>
<h2>Enter the Flash Crash</h2>
<p>By far the biggest such incident occurred on May 6, 2010, when markets for stocks and derivatives collapsed and rebounded with extraordinary velocity. The Dow Jones Industrial Average declined about 1,000 points, losing 9% of its value in a matter of minutes, the biggest same-day drop in its history, then suddenly recovered its losses just as quickly. </p>
<p>Because these dramatic events unfolded so fast and with so much fury, what happened that day has become known as the “<a href="http://money.cnn.com/2014/05/06/investing/flash-crash-anniversary/">Flash Crash</a>.” The crash was akin to an accident at a nuclear power plant: a massive release of energy over a short period of time, followed by blackouts across the whole power grid. </p>
<p>In the aftermath of the Flash Crash, the public became fascinated with the blend of high-powered technology and hyperactive market activity known as high frequency trading (HFT). To many investors and market commentators, high frequency trading has become the root cause of the unfairness and fragility of automated markets.</p>
<h2>What caused it</h2>
<p>Within hours of the Flash Crash, my colleagues and I began conducting an empirical analysis of trading several days before May 6, 2010 and during the day itself.</p>
<p>We found that the Flash Crash was triggered by a <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1686004">massive automated sell program</a> in the stock index futures market. </p>
<p>We also established that high frequency traders - algorithms that trade very quickly but do not accumulate large positions - did not cause the Flash Crash. They did, however, contribute to extraordinary market volatility experienced that day. We also showed how HFT can contribute to flash-crash-type events by exploiting short-lived imbalances in market conditions.</p>
<p>So, technology enables trading strategies that can lead to flash-crash type events. But perhaps with time, markets themselves will self-correct and become more resilient.</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/73105/original/image-20150225-1780-14zu6n3.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/73105/original/image-20150225-1780-14zu6n3.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/73105/original/image-20150225-1780-14zu6n3.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=420&fit=crop&dpr=1 600w, https://images.theconversation.com/files/73105/original/image-20150225-1780-14zu6n3.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=420&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/73105/original/image-20150225-1780-14zu6n3.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=420&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/73105/original/image-20150225-1780-14zu6n3.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=528&fit=crop&dpr=1 754w, https://images.theconversation.com/files/73105/original/image-20150225-1780-14zu6n3.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=528&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/73105/original/image-20150225-1780-14zu6n3.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=528&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">The real hub of today’s trading floor looks more like this.</span>
<span class="attribution"><span class="source">Shutterstock</span></span>
</figcaption>
</figure>
<h2>Can competition solve the market fragility problem</h2>
<p>One well established way to achieve market resiliency is through greater competition. If there are more and more participants using HFT, then soon enough they will start competing for providing services rather than looking for ways to take advantage of slower traders. </p>
<p>My colleagues and I wanted to find out if this is actually happening. We carefully looked into the inner workings of the HFT industry over two years. We found that it was dominated by an oligopoly of fast and aggressive traders who somehow persistently manage to earn high and persistent returns while taking little risk. <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2433118">link text</a> </p>
<p>How did this environment persist? For some reason, competitive market forces were unable to break up the oligopoly, and the benefits of automated markets were not being fully realized by all market participants. Instead of competing to provide the best execution to customers, incumbent high frequency traders seemed to be engaged in a winner-takes-all arms race for small reductions in latency or the amount of time it takes for a trading platform to respond to a command.</p>
<h2>Why latency matters</h2>
<p>We decided to study latency in much more detail. Latency or the gap between the issuance of a command and its execution is present in all sufficiently complex mechanical or automated systems. What we wanted to look at was automated trading platforms – where a one-millisecond delay can translate into millions of dollars.</p>
<p>In a <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2546567">recently completed study</a>, we measured the latency of a sophisticated automated trading platform and found that the amount of time it takes for a given trade request to process can vary wildly from one command to the next.</p>
<p>Sometimes, an exchange takes a few milliseconds to respond to a command to post or cancel an order. At other times it may take several seconds. Perhaps that’s where the advantage of high frequency traders comes from. If they can predict latency, then they can effectively predict what other market participants will do. </p>
<p>To visualize this, imagine one of those slow motion action sequences in which an action figure quickly disables a large crowd of adversaries. By being able to move faster than the adversaries and anticipate their moves, the action figure wins every battle. </p>
<p>How can market participants react to the presence of such action figures?</p>
<p>Well, as in the movies, they all advance or retreat together as soon as they can move. This can mean that some trading algorithms overreact or underreact to changes in market conditions. Effects of this sort, if any, should show up in prices, especially in volatility, a measure of how jumpy prices are. </p>
<p>So, we examined the relationship between trading platform latency and the volatility of asset prices. We found that latency, and especially the uncertainty about latency called jitter, can predict the volatility of asset prices. That is, the greater and more uncertain the delay, the more volatile the asset, which, of course, is great for high frequency traders, who make more money when prices are moving about more. </p>
<h2>What to do about it</h2>
<p>Following the Flash Crash of 2010, government regulators around the world came up with a variety of measures to address the issues inherent in automated trading. Most of these measures in one way or another propose to adjust latency - to “slow things down” or to remove the “speed advantage” of HFT. </p>
<p>However, in dealing with automated markets, we must use science to craft responses that address the root causes of violent market incidents without eliminating longer term advantages of technological innovation. If applied without a solid understanding of the effects of latency on the price discovery process, these knee-jerk government proposals could possibly result in extra costs and risks to the very participants they are designed to protect.</p>
<p>Instead of hastily crafted regulations, we recommend three measures. </p>
<p>First, introduce latency transparency. Trading platforms should begin to report characteristics of the time gap between trades being requested and executed to market participants on an ongoing basis so that any valuable information contained in latency can be discovered directly along with asset prices. The markets will then do what they do best – quickly incorporate information about latency into their algorithmic trading decisions and, thus, market prices.</p>
<p>Second, introduce derivatives – which are contracts whose value derive from the prices of either a real asset such as a wheat harvest or a financial asset such as a government bond – to trade latency risk. If volatility can be traded, why not latency? That would help manage the risks associated with latency by allowing an investor to pay a price to shift them to a third party just like it is being done now with wheat futures or interest swaps. </p>
<p>Third, design more pre-trade safeguards that briefly pause trading for everyone if markets start moving too quickly. In fact, that’s exactly what happened on May 6, 2010, in the stock index futures market. A five-second trading pause built deep into the trading platform forced all algorithms to reset their clocks leading to the restoration of order in the market. But by the time this the trading pause kicked in, the chain reaction had already began. If we can design these pre-trade pauses to kick in well before prices move down 1,000 points, we will all be better off. </p>
<h2>Out with the old, study the new</h2>
<p>Overall, we as scientists need to measure, study and share with the public what’s really going on in fast automated markets. We need to set aside our old notions of how trading used to be done a mere decade ago and come up with a solid evidence-based understanding of how price discovery really works at extremely small scales. </p>
<p>This knowledge is critical for designing appropriate safeguards that would protect against the massive short-lived releases of energy such as the Flash Crash, while allowing all market participants to benefit from the positive aspects of automation over the long term. I believe that getting a handle on latency and its jitter is a way to get us there.</p><img src="https://counter.theconversation.com/content/37446/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Andrei Kirilenko does not work for, consult, own shares in or receive funding from any company or organization that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>The 2010 Flash Crash wiped 1,000 points off the DJIA in an instant. An MIT professor explains how to keep it from happening again.Andrei Kirilenko, Professor of the Practice of Finance, MIT Sloan School of ManagementLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/374842015-02-13T13:06:11Z2015-02-13T13:06:11ZUK universities find a cash cow in the financial fall-out<figure><img src="https://images.theconversation.com/files/71798/original/image-20150212-16601-1rlk64p.JPG?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">A simple (but profitable) formula for university education</span> <span class="attribution"><span class="source">Andreas Kyprianou</span></span></figcaption></figure><p>The past three decades have seen an unprecedented explosion of activity in a new sub-discipline of mathematics: financial mathematics. The emergence of this field has parallelled the expansion of the quantitative financial services industry, the arm of banking that uses mathematical models to value, regulate and contrive trading strategies for complex financial derivatives such as options and futures. And it has transformed the role that university mathematics departments now play in feeding the financial services industry with its students.</p>
<p>For many universities, the volume of demand for courses relating to financial mathematics, in particular at the postgraduate level, has proved to be a gift from the gods. In the UK, MSc courses on the topic of financial mathematics are taught at well over 30 of the leading universities. </p>
<p>These masters’ programmes – a fusion of advanced probability theory, differential equations, <a href="http://www.pjaeckel.webspace.virginmedia.com/eqf013_026.pdf">Monte-Carlo simulation</a> and statistical analysis of data – are marketed as premium postgraduate education and burnished by the allure of a well-paid career in the banking industry thereafter. </p>
<p>Around two thirds of them demand <a href="https://images.theconversation.com/files/71782/original/image-20150211-16601-1cd3lip.jpeg?auto=format&q=45">fees the range £15,000-£31,000</a>. Fees are often double, treble or even quadruple those of other masters’ programmes in the same department. Can such eye-watering figures be justified?</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/71780/original/image-20150211-25693-xwkbji.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/71780/original/image-20150211-25693-xwkbji.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/71780/original/image-20150211-25693-xwkbji.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=230&fit=crop&dpr=1 600w, https://images.theconversation.com/files/71780/original/image-20150211-25693-xwkbji.jpeg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=230&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/71780/original/image-20150211-25693-xwkbji.jpeg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=230&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/71780/original/image-20150211-25693-xwkbji.jpeg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=289&fit=crop&dpr=1 754w, https://images.theconversation.com/files/71780/original/image-20150211-25693-xwkbji.jpeg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=289&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/71780/original/image-20150211-25693-xwkbji.jpeg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=289&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">The top ten Financial Mathematics courses by cost.</span>
<span class="attribution"><span class="source">http://arxiv.org/abs/1405.6739</span></span>
</figcaption>
</figure>
<h2>Value judgement</h2>
<p>The institutions promoting these courses like to talk about three key drivers for potential students: vocational specialism, opportunity and demand. However, at least two of these seem questionable. </p>
<p>A lot of the specialism within the courses in question, over and above what a general masters-level mathematical education has to offer, tends to be focused around recent academic research in financial mathematics. However, much of this turns out to be no longer relevant to the working practice of a typical modern-day quantitative analyst – <a href="http://topdocumentaryfilms.com/quants-alchemists-wall-street/">known as “quants”</a> – the job whose technical requirements should most closely align with the aspirations of the typical financial mathematics postgrad course. Many academics teaching and supervising students have, at best, rare contact with the hands-on interests and daily challenges of the quant employer, if at all.</p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/71725/original/image-20150211-25709-in5u44.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/71725/original/image-20150211-25709-in5u44.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/71725/original/image-20150211-25709-in5u44.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=316&fit=crop&dpr=1 600w, https://images.theconversation.com/files/71725/original/image-20150211-25709-in5u44.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=316&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/71725/original/image-20150211-25709-in5u44.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=316&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/71725/original/image-20150211-25709-in5u44.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=397&fit=crop&dpr=1 754w, https://images.theconversation.com/files/71725/original/image-20150211-25709-in5u44.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=397&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/71725/original/image-20150211-25709-in5u44.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=397&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">Lines in the sand. Bloomberg screens in action.</span>
<span class="attribution"><a class="source" href="https://www.flickr.com/photos/rbanks/7274814328/in/photolist-c5Rfo7-c5Ro93-c5RjRf">rbanks</a>, <a class="license" href="http://creativecommons.org/licenses/by-nc/4.0/">CC BY-NC</a></span>
</figcaption>
</figure>
<p>I have spent a modest amount of time engaging with quants in banks and financial institutions in several different countries and, in terms of opportunity, very few seem to care if a hire is in possession of an MSc in financial mathematics. Indeed, hiring strategies for quants look for sharp problem solvers who can deal with the fast pace of work and learn and develop in-house technical procedures. Therefore there is a <a href="http://www.quantstart.com/articles/Understanding-How-to-Become-a-Quantitative-Analyst">strong bias in these roles</a> towards numerate graduates (mathematics, physics, computer science), predominantly those in possession of a PhD.</p>
<h2>End justify the fees?</h2>
<p>This does not contradict the observation from those promoting the postgrad courses, that many of their graduates go on to work in the financial services industry. It is simply the case that the relatively soft technical needs of the <a href="http://joinus.barclays.com/emea/investment-bank/quantitative-analytics/">roles they are hired into</a> (for example careers in risk management or trading) are not specific to the masters they have gained. Moreover, self-selection plays a role here; commitment to an expensive postgraduate certificate shows a strong desire to work in the financial services industry.</p>
<p>All the same, as long as students are keen to pay the current level of fees, one could assert that the product speaks for itself. But is this an acceptable justification for the extent of the pricing differential in fees? What future precedent does it set and what incongruities does it establish within the current system? </p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/71727/original/image-20150211-25700-8pws6q.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/71727/original/image-20150211-25700-8pws6q.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/71727/original/image-20150211-25700-8pws6q.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=399&fit=crop&dpr=1 600w, https://images.theconversation.com/files/71727/original/image-20150211-25700-8pws6q.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=399&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/71727/original/image-20150211-25700-8pws6q.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=399&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/71727/original/image-20150211-25700-8pws6q.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=501&fit=crop&dpr=1 754w, https://images.theconversation.com/files/71727/original/image-20150211-25700-8pws6q.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=501&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/71727/original/image-20150211-25700-8pws6q.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=501&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">A life in quantitative analysis awaits.</span>
<span class="attribution"><a class="source" href="https://www.flickr.com/photos/carbonnyc/143186839/in/photolist-dDSsB-akM6PK-5FMPus-akPUhd-4U1BjS-5poQhP-a6RCW4-7zVFgu-a6jd2R-nL65bi-eRxQ88-eRxQbB-787SoB-8KvKUG-9URMHZ-eRxQ58-hCrTg-hCvFu-6N2xyN-9YiJ89-8hwawu-aJiawv-7zVGw5-eE5GBp-eEbQb9-p9JLpg-6M54Zo-oPnHEo-mmTDgK-7g5JE7-46SsWM-9Yrhpg-9SeoPE-9PKW19-nJ3Azd-nKUCGj-5TzEcs-8SKEky-fcmApa-fcATpW-fcAVGd-fcmB36-fcAWjE-4PLsUp-4Qrm16-4QrgYV-9RYfvw-4QrrBV-gsaDq-gsh3A">David Goehring</a>, <a class="license" href="http://creativecommons.org/licenses/by/4.0/">CC BY</a></span>
</figcaption>
</figure>
<p>The packaging, <a href="https://theconversation.com/uk/topics/university-fees">marketing and selling of education</a> is one of the many challenges that universities now confront as a simple matter of survival. However, one has to question the motivation behind the scramble to offer postgraduate education in financial mathematics which imposes relatively (in some cases, exceptionally) high fees, when there isn’t necessarily clear evidence of value for money. There is a danger that the answer to the question “why are fees for financial mathematics postgraduate taught courses so high?” may simply be: “because universities can get away with it”. </p>
<h2>Undesirable precedent</h2>
<p>Financial mathematics is a theory that aims to quantify randomness (or risk) within economic scenarios with a view to characterising rational behaviour, thereby promoting fairness. In this respect, it very much deserves to be researched and taught at depth in an academic environment. There can be no doubt that the MSc courses are rewarding in their intellectual content and many programmes in the UK are exemplary in this respect. </p>
<p>There is a danger, however, that the financial mathematics MSc has become an iconic symbol of an overzealous attitude towards the commercialisation of education. <a href="http://www.findamasters.com/search/courses.aspx?DID=11&SAID=52&Keywords=financial+AND+mathematics&PP=30">Marketing for masters’ programmes</a> in financial mathematics often confuses the high-level skills needed by an elite core of quants in large banks and specialist consultancies with other softer quantitative roles in banking, for which a whole array of other qualifications are equally relevant.</p>
<p>Mathematicians like to think that they are part of the solution and not part of the problem when it comes to the fall-out of the financial crisis. But by arguing the case for a sellers’ market, the business model of these MSc courses sets an undesirable precedent. Take, for example a masters degree in statistics, which, nationally, is set at standard fees. One can make a much stronger case here that there is directly relevant, high-level vocational training taking place. This qualification is needed by a whole variety of industries which hire students into <a href="http://www.statslife.org.uk/jobs">well-paid jobs as statisticians</a> because of it. What is now stopping universities charging above average fees for these degrees? </p>
<p>Twenty years ago, when the profession of the quantitative analyst was still young, financial mathematics courses meant something completely different to what they do today. The financial services industry has matured to have a much clearer understanding of how it interacts with high-end mathematics. The role that universities play in the vocational training has accordingly changed. But in the meantime, the UK MSc in financial mathematics appears to have been instituted as a valuable, but hard to justify, cash cow.</p><img src="https://counter.theconversation.com/content/37484/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Andreas Kyprianou does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>The past three decades have seen an unprecedented explosion of activity in a new sub-discipline of mathematics: financial mathematics. The emergence of this field has parallelled the expansion of the quantitative…Andreas Kyprianou, Professor of Probability, University of BathLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/336132014-11-11T10:37:51Z2014-11-11T10:37:51ZFinancial speculation: the good, the bad and the parasitic<figure><img src="https://images.theconversation.com/files/63494/original/wnbmpgg8-1414977257.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Trading floors like this one -- at the old American Stock Exchange in the 1980s -- are at the heart of capitalism and financial speculation. </span> <span class="attribution"><a class="source" href="https://www.flickr.com/photos/21734563@N04/3036628966">David Foster/Flickr via CC BY-ND</a>, <a class="license" href="http://creativecommons.org/licenses/by-nd/4.0/">CC BY-ND</a></span></figcaption></figure><p>The word “speculation” carries a connotation of negativity. And it’s probably fair to say that pretty much every financial crisis since the tulip mania of the 1630s can be <a href="http://www.amazon.com/Manias-Panics-Crashes-Financial-Investment/dp/0471467146">attributed</a> to some sort of mass speculation. There is no question that speculation caused the financial crisis of 2008, first in housing, and then in <a href="http://www.e-elgar.co.uk/bookentry_main.lasso?id=14015">derivative securities</a>. Recent reports on the multiple advantages enjoyed by high-speed traders again brings speculation to the fore and, with it, the <a href="http://online.wsj.com/articles/fast-traders-are-getting-data-from-sec-seconds-early-1414539997?KEYWORDS=high+speed+trading+and+SEC">question</a> of whether it is good, bad or indifferent for the economy. </p>
<p>But first, what does “speculation” really mean? As frequently as it’s used, the term is equally misunderstood. We don’t really define it. Rather, it is one of those things that we know when we see. In order to evaluate speculation, we must first understand what we mean by it.</p>
<p>The definition of speculation has shifted over time, at least with respect to financial markets. At the end of the 19th century, speculation generally meant investing in companies for which you had little or no information. Within a decade, the more common usage was investing in securities where dividends were uncertain. This meant common and, to some extent, preferred stock. Since all dividends are discretionary, all forms of stock were considered speculative. And dividends were <a href="http://online.wsj.com/articles/fast-traders-are-getting-data-from-sec-seconds-early-1414539997?KEYWORDS=high+speed+trading+and+SEC">important</a> because it was to get those – not capital gains – that people bought stock.</p>
<h2>Speculation’s existential crisis</h2>
<p>Matters changed starting in the 1960s. The move from dividends to capital gains had begun in earnest. Speculation now meant investing in the hope of capital appreciation – that is, selling to somebody for a higher price than you paid. Economists writing in the new field of finance claimed that speculation didn’t really exist because markets efficiently priced securities at their anticipated earnings discounted to their present value. This meant that a share of stock was worth what you paid for it. That was investing, not speculating. Cold hard realities have <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1655739">brought this belief</a> into serious question. </p>
<figure class="align-center zoomable">
<a href="https://images.theconversation.com/files/63560/original/hpx7yzk7-1415032260.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/63560/original/hpx7yzk7-1415032260.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/63560/original/hpx7yzk7-1415032260.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=450&fit=crop&dpr=1 600w, https://images.theconversation.com/files/63560/original/hpx7yzk7-1415032260.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=450&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/63560/original/hpx7yzk7-1415032260.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=450&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/63560/original/hpx7yzk7-1415032260.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=566&fit=crop&dpr=1 754w, https://images.theconversation.com/files/63560/original/hpx7yzk7-1415032260.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=566&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/63560/original/hpx7yzk7-1415032260.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=566&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px"></a>
<figcaption>
<span class="caption">The tulip mania in the Netherlands in the 1600s is often considered this first recorded speculative bubble. The price of tulip bulbs surged in value then suddenly collapsed.</span>
<span class="attribution"><a class="source" href="https://www.flickr.com/photos/veridiano3/13566892413/in/photolist-mERSsa-7D49v3-mERLUg-dZiAtQ-dZcTxk-dZcUK6-dZiB5U-dZiAy9-dZiBBN-dZcT1i-dZcTYi-dZiACY-dZcSSt-dZizUU-dZiAMU-dZcUat-dZcTV6-dZcSVD-dZcT6K-dZizZm-6iXYMA-857Mqk-4HXG55-GVuQb-F7WDV-mERHdZ-HiNRD-mERcvH-7GQhAX-7GUecA-7GUcgU-2sunmR-7GQhcv-7GQi4B-nJvuNC-FbtEe-mESJYG-HiNRr-7vjLb3-egQP1z-ec7yDM-huDz49-9DfZ2m-9N1sKR-ctzfCJ-9yASWX-gmKTRz-gmMFuK-ePyTra-a9L5">Riccardo Palazzani/Flickr via CC BY-NC-SA</a>, <a class="license" href="http://creativecommons.org/licenses/by-nc-sa/4.0/">CC BY-NC-SA</a></span>
</figcaption>
</figure>
<p>There is less doubt about the speculative nature of derivative securities that began to develop in the 1990s and exploded at the turn of the 21st century. Those “claims on claims” (or, in Warren Buffett’s felicitous phrase, “financial weapons of mass destruction”) split up the <a href="http://krugman.blogs.nytimes.com/2010/04/18/six-doctrines-in-search-of-a-policy-regime/">underlying financial assets</a> into ever smaller pieces. </p>
<p>Think of the shape of a tornado. The productive asset – the asset that generated the revenue to pay the claims – was a point at the bottom. As claims proliferated from that point up, they expanded higher and higher, wider and wider, far beyond the capacity of the energy at the bottom – the earnings – to sustain it. When investors at the top woke up and realized this, they started massive selloffs – and the whole structure came crashing down. Another way of looking at this is as a Ponzi scheme. It carries the patina of investment legitimacy because, unlike a classic Ponzi scheme, there is <em>some</em> source of earnings. But those earnings are so inadequate to support the securities superstructure that a Ponzi scheme is an appropriate metaphor.</p>
<p>Based on history and contemporary usage, I would define as “speculative” assets that have little or no identifiable financial substance, the returns from which are expected to come from its sale at a higher price to somebody else. The logical conclusion based on this definition is that speculation is never good, at least in the sense that it never contributes to the productive economy. The principle negative economic effect of speculation is to divert resources away from production and into the speculative casino. As long as it’s not excessive, it isn’t all that bad. After all, we allow gambling. Where it becomes bad is when it causes damage to the rest of the economy. And that occurs when speculation becomes parasitic on the productive economy.</p>
<h2>An economic parasite</h2>
<p>Here are a few examples of that happening. Stock bubbles are speculative. It is unlikely the underlying corporations could earn anywhere near enough money to justify prices in any reasonable time frame. That makes them speculative. Stockholders, however, expect management to sustain or increase prices. This <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1557730">puts pressure</a> on managers to manage for the short-term, damaging the long-term prospects of the productive asset – the underlying corporation.</p>
<p>Mortgage-backed securities provide another example. The concept behind them is legitimate. Commercial banks are limited in the amounts they can lend based upon their capital reserves and the risk of the loans they already have made. When banks sell off some of their risk – as they do in the case of mortgage-backed securities – the amounts of money they are able to lend increases. So it is with other asset-backed instruments – car loans, consumer loans and the like. These assets, when kept within reason, are not speculative, because their return depends upon earnings from the underlying asset. And this behavior is good for the economy because it allows banks to lend more money in the productive economy.</p>
<p>When it becomes bad – when it becomes speculation – is when ever-increasing sums of money are invested in derivative products promising substantial returns that are not supported by the actual underlying earnings. At this point, money that could be invested in the productive economy is diverted to the purely derivative economy – the speculation economy – where it continues to recirculate until the inevitable crash.</p>
<p><a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1129340">Speculation</a> has been, and always will be, with us, whether in financial markets or otherwise, the Dodd-Frank Act notwithstanding. So we would do well to impose some restraints. </p>
<p>There are a number of ways we can control speculation, or at least keep it within bounds that might diminish its harm. Perhaps first among these is tax reform, as I’ve outlined in my previous <a href="http://www.amazon.com/gp/product/B0014TQJ7E?btkr=1">research</a> on the topic. Establishing a punitive capital gains tax regime for flipping an asset too quickly and something approaching tax relief for longer-term holdings, ideally on a sliding scale, would go a long way toward eliminating non-economic “investments.” </p>
<p>Changing accounting rules so that cash flow becomes more important than earnings per share is another strategy that would significantly reduce the opportunities for creative bookkeeping. It would also help to ensure that the underlying value of the asset can support the returns of the investment securities based on it. There are many more ways to help prevent good speculation from becoming parasitic, but these suggestions are, I hope, a good start.</p><img src="https://counter.theconversation.com/content/33613/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Lawrence Mitchell has previously received funding from the Alfred P. Sloan Foundation and Ford Foundation to present conferences and other academic events.</span></em></p>The word “speculation” carries a connotation of negativity. And it’s probably fair to say that pretty much every financial crisis since the tulip mania of the 1630s can be attributed to some sort of mass…Lawrence Mitchell, Professor of Law, Case Western Reserve UniversityLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/180322013-09-12T05:32:17Z2013-09-12T05:32:17ZTaxing times for high-frequency trading<figure><img src="https://images.theconversation.com/files/31185/original/wx4n5h58-1378900440.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">I think our algorithm has called in sick.</span> <span class="attribution"><span class="source">Rafael Matsunaga</span></span></figcaption></figure><p>High-frequency trading, where computer algorithms are programmed to buy and sell financial products in a fraction of a second, is a profitable business but also a controversial practice. Regulators are concerned about its overall contribution to market quality and market stability and such concerns have led to calls for it to be taxed or constrained.</p>
<p>Italy has just become one of the first countries to implement a tax on computer-based trading activity. Regulators there recently <a href="http://www.ft.com/cms/s/0/378dcace-117e-11e3-8321-00144feabdc0.html?siteedition=uk">imposed a charge</a> on orders to buy or sell stock that are cancelled within 0.5 seconds of their submission to the exchange. This might sound like a miniscule amount of time, but in a world where a few milliseconds can be the difference between profit and loss, it is an age.</p>
<p>Given that computer-based trading is in the cross-hairs here, it is worth asking what the experts say about the effects of this activity on markets and thus the likely effects of the levy.</p>
<p>Let’s begin by being clear about one key thing. Computer-based trading is not just the preserve of hedge funds and investment banks hoping to make speculative short-term trading profits. Trades done by the pension funds that house your and my retirement savings will also, in large part, be completed using a computer algorithm that makes sub-second decisions. The Italian levy, in its attempts to constrain the former, will probably also impinge upon the latter. As such, the trading efficiency of our pension funds may be hampered and their trading costs increased.</p>
<h2>Mixed results</h2>
<p>But do we really want to reduce high-frequency trading anyway? If we look at recent academic research, the motivation for a tax on computer-based activity is far from clear. Some work suggests very clearly that algorithmic and high frequency trading make markets more liquid and <a href="http://faculty.haas.berkeley.edu/hender/HFT-PD.pdf">more efficient</a> without increasing volatility. To the extent that greater liquidity <a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.2010.01624.x/abstract">means cheaper trading</a> for the rest of us, this would suggest that computer-based trading plays a positive role in markets. </p>
<p>However, <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2238516">more recent research</a> takes a less positive note, suggesting that high-frequency traders are able to anticipate, and thus exploit, activity from slower rivals. This leads to slow folk like you and I suffering higher trading costs (for example, paying more when we buy stock and receiving less when we sell stock) than we would otherwise have done. </p>
<p>While the academic jury is still out, other groups have clearly made up their mind. European politicians and regulators, and some parts of the media, quite clearly believe that high-frequency activity is a threat to markets. They cite episodes of instability, such as the Flash Crash in May 2010, where the Dow Jones briefly <a href="http://money.cnn.com/2010/10/01/markets/SEC_CFTC_flash_crash/index.htm">lost $1 trillion in market value</a> before rebounding minutes later. And they worry about the possibility of algorithms being designed to manipulate market prices (such as in the <a href="http://articles.washingtonpost.com/2013-07-22/business/40718527_1_trading-firm-futures-contracts-high-frequency-traders">Panther Energy Trading case</a>). </p>
<p>More fundamentally, some ask what possible socially useful rationale there can be for allowing an algorithm to trade into a stock and then trade out again within 100 milliseconds. Thus, while many firms claim to be making markets rather than harming them, and despite the evidence suggesting algorithmic and high-frequency trading can benefit markets, we should be prepared for further rules to constrain computer based activity.</p>
<h2>Follow the Italians?</h2>
<p>So what about the precise implications of the Italian levy? Note that this is not a tax on trading activity itself. Italy already has one of those, a financial transaction tax imposed earlier this year, which is reported to have <a href="http://www.ft.com/cms/s/0/e593af72-8bf9-11e2-8fcf-00144feabdc0.html#axzz2eZIUdJsP">greatly reduced trading volumes</a>. </p>
<p>This is a charge for submitting and then very quickly cancelling an order to buy or sell. Thus it clearly reduces the incentives of computer-based traders to post bids to buy stock, and offers to sell stock, against which others can trade. As it hampers the market making function of computer-based trading systems then we will likely see headline measures of liquidity deteriorate (volumes will drop and bid-ask spreads will rise).</p>
<p>On the flip side, though, making it costly for algorithms to quickly post and then cancel orders may make it harder for them to prey on slower traders and to manipulate markets. It may mean that displayed quotes on an exchange are more representative of the prices a “slow trader” will get when he or she executes an order. </p>
<p>By making designers of algorithms think more carefully about how and when they place orders, it may reduce the probability of seeing wild swings in markets caused by a badly designed algorithm pumping thousands of orders into a market in error. These benefits from constraining computer-based activity, while hard to measure and to quantify, should not be ignored.</p>
<p>So regulators face a trade-off here. Constraining high frequency and algorithmic trading is likely to make markets more expensive to trade but could, at the same time, make them fairer and more robust. Quite clearly, Italian regulators feel that this type of trading endangers the integrity of markets to a great enough extent that limiting it and seeing liquidity fall is a price worth paying. Their counterparts in other countries will be considering the same question.</p><img src="https://counter.theconversation.com/content/18032/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Richard Payne has previously worked and consulted for investment banks, hedge funds and the UK Department for Business, Innovation and Skills on issues related to trading in equity and foreign exchange markets. His current work on foreign exchange markets receives funding from the Leverhulme Trust.</span></em></p>High-frequency trading, where computer algorithms are programmed to buy and sell financial products in a fraction of a second, is a profitable business but also a controversial practice. Regulators are…Richard Payne, Professor of Finance, Cass Business School, City, University of LondonLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/146672013-05-30T14:35:40Z2013-05-30T14:35:40ZToo fast, too furious? Making sense of high-frequency trading<figure><img src="https://images.theconversation.com/files/24681/original/zjrnrgv7-1369895165.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Are high-frequency traders friends or foes of the financial market?</span> <span class="attribution"><span class="source">AAP</span></span></figcaption></figure><p>As the <a href="http://www.imdb.com/title/tt1905041/">sixth iteration</a> of The Fast and the Furious franchise rolls out in cinemas, a greater speed demon lurks in our financial markets: high-frequency traders (HFTs). While the good guys in Fast 6 are clear cut, academic researchers remain undecided on whether HFTs are heroes or villains of the market. </p>
<p>The fact that HFTs can even be deemed heroes may appear outrageous to the casual observer given the media regularly portrays them as <a href="http://www.optionmonster.com/news/article.php?page=cramer_living_with_dr_algolove_81387.html">villains</a>. </p>
<p>Even some exchanges and market regulators have <a href="http://www.reuters.com/article/2012/02/29/us-ice-highfrequency-idUSTRE81S1LZ20120229">made up their mind</a> on HFTs or are <a href="http://www.asic.gov.au/asic/asic.nsf/byheadline/13-052MR+ASIC+reports+on+dark+liquidity+and+high-frequency+trading?openDocument">proposing ways</a> to curb their activity. So what has academic research found so far?</p>
<h2>What is high-frequency trading?</h2>
<p>High-frequency trading is a nebulous phrase, though it is considered a subset of algorithmic trading (AT), which is the use of computer programs to trade on electronic exchanges. </p>
<p>High-frequency traders take algorithmic trading to the extreme and quickly trade in and out of financial instruments, sometimes only holding stock for a fraction of a second. Their profitability depends crucially upon both their speed in processing information and speed in trading, measured in milliseconds. Such behaviour led high-frequency trading to be branded as digital age <a href="http://www.wallstreetdaily.com/2011/04/04/high-frequency-trading-computers/">pickpockets</a>, as their pursuit of fast profits appears to allow them to get in and out of the market before others can click the buy or sell button.</p>
<h2>Benefits</h2>
<p>Indirect evidence of the benefits of high-frequency trading is shown in studies on algorithmic trading, published in <a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.2010.01624.x/full">The Journal of Finance</a> and forthcoming in <a href="http://faculty.haas.berkeley.edu/hender/ATMonitor.pdf">Journal of Financial and Quantitative Analysis</a>. </p>
<p>These studies find that more algorithmic trading allows buyers and sellers to trade at more efficient prices. While the findings are on the broad algorithmic trading group, high-frequency traders appear to make up the majority of that group, with estimates at <a href="http://www.ft.com/intl/cms/s/0/d5fa0660-7b95-11de-9772-00144feabdc0.html#axzz2Udd9qHte">73%</a> for US trading.</p>
<h2>Costs</h2>
<p>Several papers however, argue that there are significant costs. For example, competition by HFTs may make markets worse, argues <a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1540-6261.2012.01771.x/abstract?deniedAccessCustomisedMessage=&userIsAuthenticated=false">another paper also published in The Journal of Finance</a>. They show that financial expertise improves a firms’ ability to estimate value when trading a security. </p>
<p>Such financial expertise creates an unbalanced possession of information among the market participants, which, under normal circumstances, works to the advantage of the expert. This advantage is neutralised in equilibrium, however, by offsetting investments by competitors. Moreover, when market volatility increases, a market participant with expertise could take advantage of information superiority. This triggers breakdowns in liquidity, destroying gains to trade and thus the benefits that firms hope to gain through higher levels of expertise.</p>
<p>Even in well-functioning markets, HFTs may play a dysfunctional role, as modelled in a <a href="http://www.worldscientific.com/doi/abs/10.1142/S0219024912500227?journalCode=ijtaf">paper published in International Journal of Theoretical and Applied Finance</a>. According to the paper, HFTs can create a mispricing that they unknowingly exploit to the disadvantage of ordinary investors. This contrasts with other participants who make financial markets more efficient by taking advantage of and thereby eliminating mispricing. This mispricing could be generated by the collective and independent actions of HFTs, coordinated via the observation of a common signal.</p>
<h2>Neutral views</h2>
<p>A couple of working papers provide differing neutral views on HFTs. <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2066839&download=yes">Researchers at the University of Illinois</a> argue that HFTs have no observed social benefit. They find that an arms race in speed at the sub-millisecond level is a positional game in which a trader’s pay-off depends on her need for speed relative to other traders. However when HFTs profit due to speed advantages over other traders, the profit is offset by the technological costs required to gain such speeds.</p>
<p>Using detailed transactions data of four UK stocks that identifies trading participants including individual HFTs, <a href="http://www.bankofengland.co.uk/publications/Pages/workingpapers/2012/wp469.aspx">researchers from the Bank of England</a> find that HFTs may be good or bad depending on their trading strategy. They find that while some HFTs mostly consume liquidity and therefore are potentially disruptive, others mostly supply liquidity and therefore assist markets.</p>
<p>Researchers have just started delving into the world of HFTs. Ultimately the weight of such research will impact on the stance of exchange operators and market regulators and their decisions to throttle HFTs. While movie goers await the inevitable next iteration of The Fast and the Furious, high-frequency traders are anticipating their next instalment. </p><img src="https://counter.theconversation.com/content/14667/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>The authors do not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and have disclosed no relevant affiliations beyond their academic appointment.</span></em></p>As the sixth iteration of The Fast and the Furious franchise rolls out in cinemas, a greater speed demon lurks in our financial markets: high-frequency traders (HFTs). While the good guys in Fast 6 are…Adrian Lee, Postdoctoral Research Fellow in Finance, University of Technology SydneyKIHoon Hong, Post Doctoral Research Fellow in Finance, University of Technology SydneyLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/127842013-03-18T02:43:32Z2013-03-18T02:43:32ZThe rise of the machines: High Frequency Trading and dark pools<figure><img src="https://images.theconversation.com/files/21339/original/rtv32n7r-1363563748.jpg?ixlib=rb-1.1.0&rect=14%2C7%2C974%2C564&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Are the algorithms used in high frequency trading a threat to the markets themselves? ASIC says the danger is "overstated" but the FBI and the SEC have joined forces.</span> <span class="attribution"><span class="source">AAP</span></span></figcaption></figure><p>In language that is more in keeping with hackers and crime syndicates, the Financial Times <a href="http://www.ft.com/intl/cms/s/0/11b81d74-85a4-11e2-9ee3-00144feabdc0.html#axzz2MjZ0v0xK">reported</a> last week that the FBI was going to assist securities regulators in the US to tackle “<a href="http://en.wikipedia.org/wiki/Dark_liquidity#Dark_pools">dark pools</a>” and <a href="http://en.wikipedia.org/wiki/High-frequency_trading">high frequency trading</a> abuses. Although it is not clear what the FBI’s qualifications are in the complicated area of market making or arbitrage trading, it is presumably their technical expertise they are bringing to the party as financial firms are rapidly outpacing the SEC in exploring new ways to make money with computers.</p>
<p>High frequency trading (HFT) refers to the practice of making trades using computer <a href="http://en.wikipedia.org/wiki/Algorithm">algorithms</a> that determine what stocks to buy or sell. The shares may only be owned for fractions of a second as the computer responds to both data from news feeds, social networks and other sources and also to the reactions of the market itself. HFT is one type of “<a href="http://en.wikipedia.org/wiki/Algorithmic_trading">algorithmic trading</a>” which refers to trading by computers that may, or not, involve human assistance.</p>
<p>This can give the financial firms the ability to carry out millions of transactions automatically, triggered from data sources that the computers are following. The overall number of stocks that are traded in this way is substantial and represents about 50% of the market in <a href="http://www.nytimes.com/2012/10/15/business/with-profits-dropping-high-speed-trading-cools-down.html?ref=highfrequencyalgorithmictrading">the US</a>. In Australia, the figure may be lower with <a href="http://www.smh.com.au/business/highfrequency-trading-rewriting-the-rule-book-20121026-28azm.html">one source</a> quoting 30% of the market trades being computer-driven.</p>
<p>HFT came to the public’s attention after the Dow Jones <a href="http://en.wikipedia.org/wiki/2010_Flash_Crash">experienced</a> a 9% drop over a 5-minute period in May 2010, which recovered 10 minutes later. Although the initial trigger for the crash was due to a computer error in which a mutual fund tried to sell $4.1 billion of futures contracts, HFT then kicked in and exacerbated the process first by automatically selling and then by switching off altogether as the market drop crossed a threshold. </p>
<p>Academics <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1686004&">studying</a> the phenomenon of “flash crashes” have found they are reasonably common and in most cases are due to HFT, hence the concern by market regulators to understand and control the practice.</p>
<p>The existence of dark pools is making the situation of high-frequency trading worse. Dark pools refer to the practice of making trades that are not disclosed to the public. This may be something that brokers arrange themselves or may be something offered by dark pool companies or indeed stock exchanges. Because large volumes of trades may be executed away from public scrutiny, information about these trades coupled with information from markets around the world creates an opportunity for computer algorithms to spot opportunities to make money from the differences in prices between these markets.</p>
<p>Underpinning all of this is a move by exchanges and finance companies to try and gain advantages by moving to ever faster computers, networks and sources of information. Even cables that connect exchanges in the US or across the Atlantic are being built to shave 1000s of a second off the transmission time between the exchanges. It has been <a href="http://www.motherjones.com/politics/2013/02/high-frequency-trading-danger-risk-wall-street">estimated</a> that one thousandth of a second of a speed advantage can make as much as $100 million a year difference in earnings.</p>
<p>Behind the hardware is software that is processing information so fast that the SEC is treating it as a form of insider trading because the computers have access to information that gives it an anti-competitive advantage.</p>
<p>Proponents of HFT are ready to quote the many advantages that it brings to the market. In particular there is the argument that HFT brings “liquidity” <a href="https://www.moneysmart.gov.au/investing/shares/how-to-buy-and-sell-shares/high-frequency-trading">to the market</a> by ensuring that the difference between the selling and buying price is minimised. However, this has been <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1686004&">disputed by others</a>.</p>
<p>In Australia, the Federal Government <a href="http://www.bloomberg.com/news/2013-03-12/high-frequency-trades-need-measured-rules-asic-s-medcraft-says.html">last year</a> ruled that all automated trading systems were required to have a “kill switch” by June 2014, to halt trading in the event of a malfunction. They also ruled that dark pools needed to offer a better price than public exchanges. The focus on “kill switches” has been described by one trading insider as being misguided however. </p>
<p>The Australian Securities and Investments Commission <a href="http://www.asic.gov.au/asic/asic.nsf/byHeadline/13-052MR%20ASIC%20reports%20on%20dark%20liquidity%20and%20high-frequency%20trading?opendocument">today reported</a> on the results of two taskforces set up last year to examine the impact of HFT and dark pools on the Australian market. They found that the concerns over HFT had “been overstated” but did state that dark pools would need further regulation.</p>
<p>Although there will always be a role for humans in trading, computers and computer algorithms will continue to play a significant part in how financial firms make money. With ever increasing amounts of data that are being factored into decisions at ever increasing speeds, humans are just not capable of making these decisions reliably. </p>
<p>The complexity of the software is such that it is almost impossible to know what the overall impact on financial markets is, let alone what it will be. Perhaps the constant speculation about the stock value of companies like Apple and Facebook makes more sense if they are seen as being driven not by announcements like Samsung’s new phone or new features in news feeds, but by millions of tiny bits of information seen only by computers.</p><img src="https://counter.theconversation.com/content/12784/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>David Glance does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>In language that is more in keeping with hackers and crime syndicates, the Financial Times reported last week that the FBI was going to assist securities regulators in the US to tackle “dark pools” and…David Glance, Director, Centre for Software Practice, The University of Western AustraliaLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/109682012-12-03T23:23:27Z2012-12-03T23:23:27ZMaking sense of ASIC’s new rules on dark liquidity and high-frequency trading<figure><img src="https://images.theconversation.com/files/18153/original/4dch885q-1354161879.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">The growth in high-frequency trading and the changing nature of dark liquidity in equities markets have prompted ASIC to release new rules for market integrity. </span> <span class="attribution"><span class="source">AAP</span></span></figcaption></figure><p>The Minister for Financial Services and Superannuation recently announced new <a href="http://www.asic.gov.au/asic/ASIC.NSF/byHeadline/Market%20integrity%20rules">ASIC Market Integrity Rules</a> to address the growth in automated trading and the changing nature of <a href="https://theconversation.com/transparency-dark-pools-and-the-australian-market-231">dark liquidity</a> in equities markets. </p>
<p>These new rules appropriately balance the need for additional regulation to address the changes in the equities markets without succumbing to the unsubstantiated hysteria that has plagued the public debate on these issues in recent months.</p>
<p><strong>New automated trading rules</strong></p>
<p>The two most crucial elements of the new automated trading rules relate to extreme price movements and “kill switches”.</p>
<p>The extreme price movement rules, which take effect immediately, require ASX and Chi-X to impose a brief trading pause in the event of an extreme price movement. This pause is intended to allow investors to reconsider positions and allow algorithms to reset in response to large price changes.</p>
<p>A “kill switch” requires that brokers have direct and immediate control over algorithms which are sending orders to the market through their systems. The rules require that brokers prohibit the entry of orders to the market where they “<a href="http://www.asic.gov.au/asic/pdflib.nsf/LookupByFileName/MIR-ASX-Market-Am-No-3-2012.pdf/$file/MIR-ASX-Market-Am-No-3-2012.pdf">have interfered with or are likely to interfere with the efficiency or integrity of the market</a>”. These rules will not take effect until May 2014.</p>
<p>Most of the media discussion on these new rules has suggested that they are designed to curb the activities of high frequency traders. But in reality, their scope is much broader. The rules address the potential for anomalous orders from all types of trading algorithms, not just those used in high-frequency trading (HFT). HFT is a subset of algorithmic trading that is dependent on being able to enter and amend orders at very high speed. </p>
<p><strong>New dark liquidity rules</strong></p>
<p>There are two changes relating to dark liquidity. The first, and arguably most important change, is a new requirement for dark trades below block size to offer “economically meaningful price improvement”. This requirement means that investors trading in the dark must do so, at a better price than what is offered by the ASX and Chi-X. This requirement offers two important benefits:</p>
<p>(1) It rewards investors that display orders on the exchange by ensuring that other investors can not trade before them unless they are prepared to offer an economically superior price. This will encourage investors to place their orders on the exchange and should therefore increase liquidity in the exchange order book.</p>
<p>(2) It will reduce the fraction of activity that occurs in the dark, which should help to ensure the quality of the price discovery process.</p>
<p>The second change relates to block trades. Historically, block trades, which were defined as trades over $1 million could be executed at any price and without any pre-trade transparency. The new rules set three different block trade thresholds based on the liquidity of the stock. The thresholds range from $1 million down to $200,000. These changes will make it easier for institutions to trade blocks in smaller and less liquid stocks.</p>
<p>The price improvement requirement and the new block trade thresholds will take effect in May 2013.</p>
<p>ASIC also provided details about two internal taskforces that are in the process of investigating HFT and dark liquidity. The taskforces involve substantial fact-finding and data gathering, which has included formal requests for dark pool operators to produce information about their operations under the Corporations Act. These taskforces will assist ASIC to better understand the impact of HFT and dark liquidity in Australia on market quality and fairness. This will aid in the development of future Market Integrity Rules.</p>
<p>In addition, the Minister has announced that the Treasury will undertake a review of financial market licensing arrangements to determine whether there is a need for changes to this regime. In particular, it will determine whether or not dark pool operators should be licensed as markets. This will help to address the concerns of an unfair playing field expressed by some segments of the market.</p><img src="https://counter.theconversation.com/content/10968/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Carole Comerton-Forde acts as an economic consultant for the Australian Securities and Investments Commission. She is also an Australian Securities Exchange shareholder.</span></em></p>The Minister for Financial Services and Superannuation recently announced new ASIC Market Integrity Rules to address the growth in automated trading and the changing nature of dark liquidity in equities…Carole Comerton-Forde, Professor of Finance, The University of MelbourneLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/95722012-09-17T04:26:30Z2012-09-17T04:26:30ZCould high frequency trading lead to our own ‘flash crash’?<figure><img src="https://images.theconversation.com/files/15448/original/xhbrt3pd-1347585206.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">As high frequency trading and dark pools worry Australian regulators, can a market be too fluid? </span> <span class="attribution"><span class="source">AAP</span></span></figcaption></figure><p>The dangers of massive high frequency trading are becoming increasingly clear in equity markets. Greg Medcraft, the chairman of corporate regulator ASIC, confirmed to a Federal parliamentary committee:</p>
<p>“Regulators around the world are very concerned about the systemic risk on high frequency trading. We have already had the flash crash, we have had Knight Capital, but there have been incidents in other major markets as well.”</p>
<p>His colleague, ASIC deputy commissioner Belinda Gibson, suggested algorithmic and high frequency trading is sometimes manipulative or illegal, but it is often simply predatory on other investors.</p>
<p>In response, ASIC is proposing mandatory computer “kill” switches that stop trades which appear to be out of control.</p>
<p>In addition, regulators are concerned about the increase of trading taking place in “dark pools”, and are encouraging trades back out on to open exchanges.</p>
<p>A recent discussion of this issue in the Australian Financial Review ironically juxtaposed David Gonski’s sage call for a long-term view of investment and University of Sydney’s Professor of Finance, Alex Frino’s acute explanation of the increasing prevalence of high-frequency trading (HFT), which starkly highlighted the complexities of contemporary finance markets.</p>
<p>The immense divide between the 20-year timeframe of fund managers to provide retirement benefits to the public, and the frantic high-velocity trading in which micro-seconds are critical, demands further investigation.</p>
<p>Firstly, there is a profound distinction between investing and trading. These are very different activities and deserve to be regulated, supervised and taxed in different ways.</p>
<p>The <a href="http://www.bis.gov.uk/assets/biscore/business-law/docs/k/12-917-kay-review-of-equity-markets-final-report">Kay Review of UK Equity Markets and Long-Term Decision Making</a> published in July analysed this distinction. High frequency traders are driven by short-term market trends, and turn their portfolios over rapidly. Underlying performance is of less interest than immediate opportunity. In contrast, investors intent on holding assets for the long-term will analyse a companies’ prospects and underlying performance.</p>
<p>Kay concludes: “Equity markets work effectively for the corporate sector when they encourage, and do not impede, decisions which enhances the long-term competitive capabilities of the business.” The concern is that the short-term emphasis of equity markets may have encouraged unproductive value extraction at the expense of sustainable value creation.</p>
<p>Advances in financial, computing and communications technologies have facilitated the dramatic reduction of the average holding period of equity: on the NYSE this has diminished from seven years in the 1950s to six months today. More worryingly, as much as 70% of trading volume on the NYSE is measured now in milliseconds, and other exchanges are similarly overwhelmed.</p>
<p>The more impact short-term traders have in the market, the more volatile prices will be as these become less rooted in the fundamentals of the value of corporations traded, as the Bank of England’s Andrew Haldane has <a href="http://www.bankofengland.co.uk/publications/Documents/speeches/2011/speech509.pdf">documented</a>.</p>
<p>Present financial wisdom, and the securities regulation that has been developed within the same paradigm, suggests there is no such thing as too much liquidity, too much volatility or too much trading, as Justin Fox, Harvard Business Review’s editorial director and Harvard Business School professor Jay Lorsch argue in the current issue of the HBR.</p>
<p>Yet this creates very hazardous financial seas in which to navigate any corporate vessel. <a href="http://hbr.org/1992/09/capital-disadvantage-americas-failing-capital-investment-system/ar/1">Michael E. Porter</a> once warned the US Council on Competitiveness of the problems for business created by a too fluid capital market.</p>
<p>More recently the consequences for corporate America were revealed by <a href="http://www.upjohninst.org/Publications/Titles/SustainableProsperityintheNewEconomy">Professor Bill Lazonick</a> of the University of Massachusetts in his barnstorming tour of Australia in July: US corporations have hoarded trillions of dollars, and they will only spend money on dividends, share buy backs and executive options – all designed to enhance their share price.</p>
<p>Disastrously, investment in innovation, product and skill development has <a href="http://www.finnov-fp7.eu/events/finnov-final-conference-2012">collapsed in US industry</a> (with the large corporation exceptions of Apple and Google). Last year the America had a US$60 billion trade deficit in high tech goods, according to the <a href="http://www.commerce.gov/sites/default/files/documents/2012/january/competes_010511_0.pdf">US Department of Commerce</a>.</p>
<p>Australian industry could be heading rapidly in the same direction. The optimistic forecast of the Prime Minister’s Manufacturing Task Force <a href="https://theconversation.com/the-blueprint-for-a-smarter-australia-starts-with-manufacturing-8900">published this month</a> will only be secured if patient capital is available.</p>
<p>Business innovation is fuelled by investment. Innovation trajectories are shaped not simply by new knowledge and technical capability, but the rates and criteria by which financial markets and institutions will allocate resources to innovative business enterprise.</p>
<p>As <a href="http://www.regnan.com/">Regnan</a> has observed, long term innovation and investment performance requires attention to more than short term financial metrics.</p><img src="https://counter.theconversation.com/content/9572/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Thomas Clarke does not work for, consult, own shares in or receive funding from any company or organisation that would benefit from this article, and has disclosed no relevant affiliations beyond their academic appointment.</span></em></p>The dangers of massive high frequency trading are becoming increasingly clear in equity markets. Greg Medcraft, the chairman of corporate regulator ASIC, confirmed to a Federal parliamentary committee…Thomas Clarke, Professor, Centre for Corporate Governance , University of Technology SydneyLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/39002011-10-30T19:44:35Z2011-10-30T19:44:35ZChi-X launch - what does it mean for the Australian market?<figure><img src="https://images.theconversation.com/files/4913/original/sharemarket.jpg?ixlib=rb-1.1.0&rect=120%2C83%2C3934%2C2672&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Australia's trading market faces new competition from today.</span> <span class="attribution"><span class="source">AAP</span></span></figcaption></figure><p>Today for the first time in its history, the Australian Securities Exchange will face competition in equities trading. </p>
<p>This means that there will now be a choice of trading venue for the execution of orders in ASX-listed stocks. </p>
<p>ASX’s new competitor, <a href="http://www.chi-x.com/resources/au/file/181011%20Press%20Release%20-%20Chi-X%20Global%20Announces%20New%20Equity%20Investors.pdf">Chi-X Australia</a>, will begin trading in just eight stocks, but will quickly expand this to include all ASX/S&P 200 stocks and ASX-listed Exchange Traded Funds. </p>
<p>Twenty two ASX participants, including all of the large brokers, are ready to begin trading with Chi-X from today. </p>
<h2>Who is Chi-X Australia?</h2>
<p>Chi-X Australia is owned by <a href="http://www.chi-x.com/">Chi-X Global</a>, which also operates <a href="http://www.chi-xcanada.com/includes/indexShow.jsp?thePage=/index.jsp">Chi-X Canada</a> and <a href="http://www.chi-x.com/japan/">Chi-X Japan</a>. Until very recently Chi-X Global was a wholly owned subsidiary of the <a href="http://www.nomura.com/">Nomura Group</a>. </p>
<p>However, five additional financial institutions have now taken a minority stake in the company. The new owners are BofA Merrill Lynch, Goldman Sachs, Morgan Stanley, GETCO and Quantlab. </p>
<h2>What can we expect from competition in equities trading?</h2>
<p>Markets overseas have enjoyed competition in equities trading for many years. The overseas experience suggests that the introduction of competition can lead to rapid and significant change. </p>
<p>In the UK, Chi-X Europe captured over 20% of the London Stock Exchange’s (LSE) market share within the first 12 months. </p>
<figure class="align-left ">
<img alt="" src="https://images.theconversation.com/files/4918/original/lse.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/4918/original/lse.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=401&fit=crop&dpr=1 600w, https://images.theconversation.com/files/4918/original/lse.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=401&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/4918/original/lse.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=401&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/4918/original/lse.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=504&fit=crop&dpr=1 754w, https://images.theconversation.com/files/4918/original/lse.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=504&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/4918/original/lse.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=504&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">Chi-X challenged LSE’s market share.</span>
<span class="attribution"><span class="source">AAP</span></span>
</figcaption>
</figure>
<p>Today, four and a half years after its launch, Chi-X Europe is the largest exchange in Europe by dollar value traded. In Canada, the Toronto Stock Exchange has lost more than 35% the trading activity in its stocks to new trading venues. </p>
<p>Multiple trading platforms also increase the level of high frequency trading (HFT). This is due to the low latency technology offered by the new trading platforms and the arbitrage opportunities created by the existence of multiple venues. </p>
<p>An <a href="http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1722924">academic study</a> of the launch of Chi-X in the Dutch market shows that Chi-X’s growth in market share was driven by the growth in HFT activity.</p>
<p>The combination of competition and increased HFT has generally led to significant increases in trading activity and decreases in bid-ask spreads. </p>
<p>We can expect similar trends in Australia.</p>
<p>While these changes offer benefits to the market there is some debate about whether reduced spreads have come at the expense of the depth of orders in the market. </p>
<p>There has also been a trend toward lower average trade sizes which potentially makes it more difficult for institutional investors to get large orders filled.</p>
<h2>Are there other benefits?</h2>
<p>Even before the launch of Chi-X, the Australian market has benefitted from the threat of competition offered by Chi-X. </p>
<p>ASX slashed trading fees in July 2010 with the headline fee falling from 0.28 basis points (bps) to 0.15 bps for each side of the trade. ASX also upgraded its trading technology, offered new co-location facilities and introduced a range of new order types to meet the needs of traders and investors. </p>
<p>ASX will also launch its own low latency platform – <a href="http://www.asx.com.au/documents/trading_services/purematch_factsheet.pdf">PureMatch</a> – to compete directly with Chi-X for HFT order flow. PureMatch is set to launch on November 28. </p>
<p>Trading fees will face continued downward pressure. Chi-X is launching with a <a href="http://www.chi-x.com/resources/au/file/Market%20Operations%20Notice%200019-11.pdf">maker-taker pricing model</a>; which means that different fees are charged depending on whether an order provides or takes liquidity from the market. Liquidity demanders will be charged, 0.12 bps and liquidity suppliers will be charged 0.06 bps.</p>
<figure class="align-right ">
<img alt="" src="https://images.theconversation.com/files/4914/original/shares.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/4914/original/shares.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=803&fit=crop&dpr=1 600w, https://images.theconversation.com/files/4914/original/shares.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=803&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/4914/original/shares.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=803&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/4914/original/shares.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=1009&fit=crop&dpr=1 754w, https://images.theconversation.com/files/4914/original/shares.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=1009&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/4914/original/shares.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=1009&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">The ASX has introduced new services.</span>
<span class="attribution"><span class="source">AAP</span></span>
</figcaption>
</figure>
<p>The differential fee is aimed at attracting liquidity suppliers to the new market. The ASX is yet to announce its pricing model for PureMatch.</p>
<h2>What will these changes mean for retail investors trading in the market?</h2>
<p>If overseas trends are followed, retail investors should enjoy the benefits of lower bid-ask spreads. For investors that trade using market orders (which most retail orders do) this will mean a lower cost of trading. </p>
<p>In terms of the trading process for the retail investor, there will be little change, as the decision on where to trade will be made by brokers. </p>
<p>ASIC has introduced new <a href="http://www.asic.gov.au/asic/ASIC.NSF/byHeadline/Market%20integrity%20rules">Market Integrity Rules </a>which require brokers to deliver “best execution” for their clients.</p>
<p>For retail orders, this means brokers must ensure the order is routed to the market offering the best price. There is however a 12 month transitional period during which time brokers may
choose to send all orders to ASX.</p>
<p>For brokers who have not connected to Chi-X, ASX is offering an <a href="http://www.asxgroup.com.au/media/PDFs/110713mr_asx_best_and_fidessa.pdf">order routing service</a>. This means that ASX may route the order to Chi-X if Chi-X is offering a better price. </p>
<p>So retail investors should feel confident that they can achieve good trading outcomes in the new trading environment. </p>
<p>It is important to remember, that Chi-X’s trading services are limited to the largest and most liquid ASX-listed stocks. So for smaller stocks, there is no change. </p>
<p>Investors and traders can expect ASX and Chi-X to compete vigorously in the coming months and years. This will ensure continued innovation and competitive pricing for the market.</p><img src="https://counter.theconversation.com/content/3900/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Carole Comerton-Forde acts as an economic consultant for the Australian Securities and Investments Commission. She is also an Australian Securities Exchange shareholder.
</span></em></p>Today for the first time in its history, the Australian Securities Exchange will face competition in equities trading. This means that there will now be a choice of trading venue for the execution of orders…Carole Comerton-Forde, Professor of Finance, The University of MelbourneLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/37502011-10-09T19:01:45Z2011-10-09T19:01:45ZOut of our hands: the hidden dangers of high-frequency trading<figure><img src="https://images.theconversation.com/files/4218/original/trader.jpg?ixlib=rb-1.1.0&rect=71%2C60%2C2245%2C1548&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Sharemarket trading is now mostly out of human hands, thanks to algorithimic programs.</span> <span class="attribution"><span class="source">AAP</span></span></figcaption></figure><p>Stock market trading has been transformed over the last two decades in ways that are fated to increase the likelihood of complete market collapse. </p>
<p>Stocks used to be traded by human beings through shouted buy and sell bids (the “open outcry” market). </p>
<p>Trades could only be made when the exchange was open; records were kept on paper. Today, every element of this has changed: trading decisions are “roboticised”, buy and sell orders are made by powerful computers according to the proprietary algorithms that hedge funds and investment banks have programmed into them.</p>
<p>Record-keeping is computerised and trades are made any time of the day or night. Because computers can respond more quickly than humans, electronic trading platforms have cut the time required to complete a trade to mere milliseconds, which in turn has caused trade volumes to soar. </p>
<figure class="align-left ">
<img alt="" src="https://images.theconversation.com/files/4210/original/shouts.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/4210/original/shouts.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=657&fit=crop&dpr=1 600w, https://images.theconversation.com/files/4210/original/shouts.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=657&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/4210/original/shouts.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=657&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/4210/original/shouts.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=826&fit=crop&dpr=1 754w, https://images.theconversation.com/files/4210/original/shouts.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=826&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/4210/original/shouts.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=826&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">The open outcry market has been superseded.</span>
<span class="attribution"><span class="source">AAP</span></span>
</figcaption>
</figure>
<h2>High speed launch</h2>
<p>The high-speed platform launched by the Australian Securities Exchange earlier this year, for example, boasts that <a href="http://www.asx.com.au/trading_services/asx-trade.htm">ASX Trade</a> “takes ASX’s latency down to 300 microseconds”. </p>
<p>In other words the machines can process 100,000 orders per second, 500 million per day. </p>
<p>Predatory traders constantly labour to discover and “game” the algorithms of opposing firms (the shelf life of an algorithm is now down to 14 days). </p>
<p>As a senior technology analyst at Japanese trading powerhouse Sumitomo Corporation has <a href="http://www.computing.co.uk/ctg/opinion/2112759/opinion-leaders-bring-economy-crashing">concluded</a>: it was “a disturbing certainty” this “Brave New World” will lead to “a complete market collapse”.</p>
<p>Stock market crashes were hardly unknown when humans were in charge – the Great Depression of the 1930s is a case in point - algorithmic trading on electronic platforms <a href="http://www.nytimes.com/2011/10/09/business/clamping-down-on-rapid-trades-in-stock-market.html?_r=1&hp">poses significant new risks to world capital markets</a>. </p>
<p>The interaction of high-speed trading systems processing thousand of orders per second can - and has - created disastrous, uncontrollable feedback loops. </p>
<h2>Dramatic plunge</h2>
<p>The <a href="http://www.reuters.com/article/2010/05/06/us-markets-stocks-idUSTRE6341EA20100506">most dramatic</a> recent illustration occurred on May 6, 2010 when the Dow Jones plunged 573 points in five minutes and dropped close to 1000 points in less than an hour.</p>
<p>And although regulatory agencies around the world have tried to prevent this from happening again by requiring the installation of “circuit breakers” which shut down trading when “abnormal” patterns appear, mini flash-crashes are today a regular occurrence.</p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/4209/original/trade.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/4209/original/trade.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=354&fit=crop&dpr=1 600w, https://images.theconversation.com/files/4209/original/trade.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=354&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/4209/original/trade.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=354&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/4209/original/trade.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=445&fit=crop&dpr=1 754w, https://images.theconversation.com/files/4209/original/trade.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=445&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/4209/original/trade.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=445&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
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<span class="caption">Meltdown.</span>
<span class="attribution"><span class="source">AAP</span></span>
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<p>Australia has been a relatively minor player in this global poker game until now. But in May 2010 ASIC (the Australian Securities and Investment Commission, Australia’s national regulatory agency), began allowing high speed trading platforms and off-exchange trading here. The ASX immediately unveilled ASX Trade. </p>
<h2>The arrival of Chi-X</h2>
<p>From October 31, the Australian Securities Exchange will face competition for the first time when <a href="http://theconversation.com/transparency-dark-pools-and-the-australian-market-231">Chi-X,</a> a multi-national market operator owned by Japanese investment bank Nomura launches its alternative execution venue. More competition and more market fragmentation, is sure to follow.</p>
<p>Preventing fraud in trades occurring at this speed, in this volume, with this level of programming sophistication and computer power, is what can only be called a significant regulatory challenge. </p>
<p>And regulatory agencies have indeed attempted to meet this challenge. Their main tools have been real-time surveillance systems that monitor stock market trades and signal officials when trading patterns appear abnormal. </p>
<p>One of the most sophisticated is the SMARTS system, devised by Mike Aitken and a group of academics at the <a href="http://www.cmcrc.com/">Capital Markets Cooperative Research Centre</a> in Sydney. </p>
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<img alt="" src="https://images.theconversation.com/files/4220/original/hands.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/4220/original/hands.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=783&fit=crop&dpr=1 600w, https://images.theconversation.com/files/4220/original/hands.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=783&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/4220/original/hands.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=783&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/4220/original/hands.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=984&fit=crop&dpr=1 754w, https://images.theconversation.com/files/4220/original/hands.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=984&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/4220/original/hands.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=984&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
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<span class="caption"></span>
<span class="attribution"><span class="source">AAP</span></span>
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<p>Their system sets out to measure both market efficiency and integrity. When trading patterns seem to indicate insider trading, market manipulation or other fraudulent acts are occurring, an ALERT is issued and regulators then check it out. </p>
<h2>Deterring fraud</h2>
<p>The efficiency of such systems in detecting and deterring stock market fraud, however, depends on the sensitivity of the program – too sensitive and a massive number of false positives will be generated; too insensitive and many frauds will remain undetected. </p>
<p>Ultimately, however, the success of these systems lies less in the technological capacity of the regulators (though this is never close to matching the computer sophistication of the regulated) than in their political power. </p>
<p>Only the bravest of regulatory agencies dare intervene in the highly profitable games of Goldman Sachs and their worldwide equivalents when markets are booming. </p>
<p>This has more significant consequences – more potential for wreaking social harm – today than ever before. </p>
<h2>Stock dependency</h2>
<p>From the 1980s on, governments around the world have been under the sway of neoliberal economic doctrines that mandated the downsizing or elimination of many universal entitlement programs, such as health care, education and pensions. </p>
<p>This made citizens increasingly dependent for their health and wellbeing on savings invested in world markets, usually done through pension (superannuation) funds. </p>
<p>As a result, like it or not, we are all dependent on stock markets now – as demonstrated most dramatically in the 2008 financial crisis when millions lost their jobs, pensions and life chances overnight.</p>
<p>It is incumbent on governments, those who are elected to protect the interests of all their citizens – not just those of the wealthiest 2% – to question what we are all told is technological “progress”; the happy results of traders’ “innovations”. </p>
<p>Which groups are reaping the lion’s share of the benefits? Which are taking the lion’s share of the risks? </p>
<p>In the words of the aforementioned technology analyst, action is necessary “to pre-empt the re-manifestation of greed and the birth of flash mega-recessions”.</p><img src="https://counter.theconversation.com/content/3750/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Laureen Snider was a keynote speaker at the 24th Australian and New Zealand Society of Criminology Conference – "Crime and the regions: from the local to regional, national and international", convened by Deakin University on September 28 - September 30.</span></em></p>Stock market trading has been transformed over the last two decades in ways that are fated to increase the likelihood of complete market collapse. Stocks used to be traded by human beings through shouted…Laureen Snider, Professor, Department of Sociology, Queen's University, OntarioLicensed as Creative Commons – attribution, no derivatives.