tag:theconversation.com,2011:/global/topics/algorithmic-trading-5360/articlesAlgorithmic trading – The Conversation2020-01-16T12:20:14Ztag:theconversation.com,2011:article/1300152020-01-16T12:20:14Z2020-01-16T12:20:14ZFinancial trading bots have fascinating similarities to people – we need to learn from them<figure><img src="https://images.theconversation.com/files/310429/original/file-20200116-181598-wdq1bi.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">Automated for the people. </span> <span class="attribution"><a class="source" href="https://www.shutterstock.com/image-illustration/double-exposure-image-financial-graph-virtual-584819980">WhiteMocca</a></span></figcaption></figure><p>In 2019, the world fretted that algorithms now know us better than we know ourselves. No concept captures this better than <a href="https://www.publicaffairsbooks.com/titles/shoshana-zuboff/the-age-of-surveillance-capitalism/9781610395694/">surveillance capitalism</a>, a term coined by American writer Shoshana Zuboff to describe a bleak new era in which the likes of Facebook and Google provide popular services while their algorithms hawk our digital traces. </p>
<p>Surprisingly, Zuboff’s concern doesn’t extend to the algorithms in financial markets that have replaced many of the humans on trading floors. Automated algorithmic trading took off around the beginning of the 21st century, first in the US but soon in Europe as well. </p>
<p>One important driver was high-frequency trading, which runs at blinding speeds, down to billionths of a second. It offered investors the prospect of an edge over their rivals, while helping to provide liquidity to a market by ensuring there was always someone willing to buy and sell at a particular price. High-frequency trading is now behind more than half of the volumes in both the <a href="https://www.ft.com/content/d81f96ea-d43c-11e7-a303-9060cb1e5f44">stock</a> and <a href="https://www.ft.com/content/4d589796-6211-11e9-a27a-fdd51850994c">futures</a> markets. In other markets, such as <a href="http://www.sps.ed.ac.uk/__data/assets/pdf_file/0005/252905/AJSVol1236May2018.pdf">foreign exchange</a>, algorithms have a smaller but still significant presence, with no signs that they will wane in future. </p>
<h2>The vices of devices</h2>
<p>Humans still program the algorithms and design their trading strategies, though the <a href="https://news.efinancialcareers.com/uk-en/329751/jpmorgans-new-guide-to-machine-learning-in-algorithmic-trading">rise of deep learning</a> is putting even this role under threat. But the moment the algorithms go live on markets, they act on their own accord without human intervention, dancing with each other in dizzying and often unexpected ways. </p>
<p>At first glance, they have little in common with us. They cannot think or feel, and despite the hype around machine learning, it’s still <a href="https://www.forbes.com/sites/fernandezelizabeth/2019/11/30/ai-is-not-similar-to-human-intelligence-thinking-so-could-be-dangerous/#74ddf6a76c22">contentious and complicated</a> to describe them as intelligent. Like human traders, however, they make decisions, observe others making decisions, and adjust their behaviour in response. </p>
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<a href="https://images.theconversation.com/files/310438/original/file-20200116-181639-pt7hk.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/310438/original/file-20200116-181639-pt7hk.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=237&fit=clip" srcset="https://images.theconversation.com/files/310438/original/file-20200116-181639-pt7hk.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=832&fit=crop&dpr=1 600w, https://images.theconversation.com/files/310438/original/file-20200116-181639-pt7hk.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=832&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/310438/original/file-20200116-181639-pt7hk.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=832&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/310438/original/file-20200116-181639-pt7hk.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=1046&fit=crop&dpr=1 754w, https://images.theconversation.com/files/310438/original/file-20200116-181639-pt7hk.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=1046&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/310438/original/file-20200116-181639-pt7hk.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=1046&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">Fooled you.</span>
<span class="attribution"><a class="source" href="https://en.wikipedia.org/wiki/Metal_Mickey#/media/File:Metal_Mickey.jpg">Wikimedia</a></span>
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<p>At speeds many times faster than humans will probably ever muster, these algorithms easily form expectations about each other’s expectations when placing their buy and sell orders. </p>
<p>For example, one algorithm might seek to manipulate another’s expectations about price movements by sending a large number of orders to either buy or sell a particular asset. The first algorithm will then quickly cancel its orders, having hopefully tricked its rival into making the wrong bet about which way the market is heading. </p>
<p>Interestingly, <a href="https://journals.sagepub.com/doi/abs/10.1177/1468795X02002001684?journalCode=jcsa">sociologists consider</a> this sort of mutual anticipation to be a central feature of what it means for humans to be social. They <a href="https://www.jstor.org/stable/2779252?seq=1">have long seen</a> markets as highly social arenas. In the heyday of the trading floors, reading other traders’ social cues correctly – a grimace or grin, anxious tones, even the hubbub of the trading floor – often spelled the difference between wealth and disaster. </p>
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<img alt="" src="https://images.theconversation.com/files/310440/original/file-20200116-181598-zt4s07.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/310440/original/file-20200116-181598-zt4s07.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=466&fit=crop&dpr=1 600w, https://images.theconversation.com/files/310440/original/file-20200116-181598-zt4s07.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=466&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/310440/original/file-20200116-181598-zt4s07.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=466&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/310440/original/file-20200116-181598-zt4s07.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=585&fit=crop&dpr=1 754w, https://images.theconversation.com/files/310440/original/file-20200116-181598-zt4s07.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=585&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/310440/original/file-20200116-181598-zt4s07.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=585&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">How it once was.</span>
<span class="attribution"><a class="source" href="http://www.sps.ed.ac.uk/__data/assets/pdf_file/0020/252902/IntOrderWeb.pdf">Everett Collection</a></span>
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<p>But if machines can be social, how similar or different is it to how humans socialise really? There are obvious differences, of course. While the human traders of the past often knew one another well, and often hung out together after work, algorithms trade anonymously. When they send orders to buy or sell assets, no other traders know whether it’s coming from a man or a machine. </p>
<p>Indeed, this is precisely why they are programmed to form expectations about one another. Facial cues are no longer available, but entire strategies have been developed that seek to find out whether a number of orders might have been placed by one and the same algorithm – and then try to <a href="http://www.sps.ed.ac.uk/__data/assets/pdf_file/0010/256564/Making_taking_and_the_material_political_economy_of_algorithmic_trading.pdf">predict what its next moves might be</a>. </p>
<p>To evade such attempts, algorithms are often designed so as not to be recognised as algorithms by other algorithms. As the Scottish sociologist Donald MacKenzie <a href="http://www.sps.ed.ac.uk/__data/assets/pdf_file/0020/252902/IntOrderWeb.pdf">has put it</a>, they may engage in dissimulation strategies and/or seek to give a particular presentation of their “self” in public. These are again attributes that sociologists have long considered key aspects of metropolitan life. </p>
<h2>Avalanche!</h2>
<p><a href="http://info.cbs.dk/algofinance">Together with colleagues</a>, I have spent the past several years in major financial hubs interviewing traders, programmers, regulators, exchange officials and other finance professionals about these trading algorithms. This has <a href="https://academic.oup.com/ser/article-abstract/15/2/283/2890744?redirectedFrom=fulltext">drawn out</a> some other interesting similarities between human and automated traders.</p>
<p>Programmers readily admit that once their algorithms start interacting with others, they get carried away and act unpredictably, as if they were in a mob. Sociologists since the late 19th century <a href="https://www.cambridge.org/dk/academic/subjects/sociology/social-theory/social-avalanche-crowds-cities-and-financial-markets?format=PB">have studied</a> how people get entranced by crowds and let their autonomy slide in “social avalanches”, but we have so far largely ignored the fact that financial machines do something similar. </p>
<p>The “<a href="https://www.sec.gov/news/studies/2010/marketevents-report.pdf">flash crash</a>” of May 6 2010 best illustrates what I mean here. In four a half minutes, the frenzied interaction of fully automated trading algorithms put the US markets into a nosedive, generating around US$1 trillion (£768 billion) of losses until trading was <a href="https://theconversation.com/flash-crashes-if-reforms-arent-ramped-up-the-next-one-could-spell-global-disaster-109362">swiftly suspended</a>. </p>
<p>Most of these trades involved were later cancelled as “clearly erroneous”. Certainly no trader or programmer had planned on creating this massive shift in prices, but decades of sociological research tell us that this sort of behaviour is expected in large groups. We need to understand how our financial algorithms interact in concert before our own tools become our undoing. </p>
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<a href="https://images.theconversation.com/files/310443/original/file-20200116-181617-msyknb.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=1000&fit=clip"><img alt="" src="https://images.theconversation.com/files/310443/original/file-20200116-181617-msyknb.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/310443/original/file-20200116-181617-msyknb.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/310443/original/file-20200116-181617-msyknb.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/310443/original/file-20200116-181617-msyknb.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/310443/original/file-20200116-181617-msyknb.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=503&fit=crop&dpr=1 754w, https://images.theconversation.com/files/310443/original/file-20200116-181617-msyknb.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=503&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/310443/original/file-20200116-181617-msyknb.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=503&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">Unstoppable momentum.</span>
<span class="attribution"><a class="source" href="https://www.shutterstock.com/image-photo/power-nature-real-huge-avalanche-comes-261590543">Lysogor Roman</a></span>
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<p>Of course, not all forms of social interaction are admirable or beneficial. Like humans, algorithms interact with each other in ways that range from caring and peaceful to cold and violent: from providing liquidity and maintaining market stability to making manipulative orders and triggering wild trading activity. </p>
<p>Getting to grips with these interactions is not only key to understanding modern trading and trying to prevent future flash crashes. Algorithms talk to one another in more and more fields today. Understanding how they behave as crowds will hopefully shed light in areas where they are just starting to come into their own – think <a href="https://www.intellias.com/how-machine-learning-algorithms-make-self-driving-cars-a-reality/">self-driving traffic systems</a> or <a href="https://www.theguardian.com/commentisfree/2018/oct/11/war-jedi-algorithmic-warfare-us-military">automated warfare</a>, for instance. It may even alert us to the avalanches that lie in wait, too.</p><img src="https://counter.theconversation.com/content/130015/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Christian Borch receives funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 725706). </span></em></p>Once algorithms go live on markets, they start behaving in ways that programmers could not have foreseen.Christian Borch, Professor of Economic Sociology and Social Theory, Copenhagen Business SchoolLicensed as Creative Commons – attribution, no derivatives.tag:theconversation.com,2011:article/1114362019-03-08T12:51:28Z2019-03-08T12:51:28ZAlgorithms have already taken over human decision making<figure><img src="https://images.theconversation.com/files/262857/original/file-20190308-150697-yx94v4.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">
</span> <span class="attribution"><a class="source" href="https://www.shutterstock.com/image-photo/turquoise-blue-colored-shadows-walking-people-765005305">Robsonphoto/Shutterstock</a></span></figcaption></figure><p>I can still recall my surprise when a book by evolutionary biologist Peter Lawrence entitled “The making of a fly” <a href="https://www.wired.com/2011/04/amazon-flies-24-million/">came to be priced on Amazon at $23,698,655.93</a> (plus $3.99 shipping). While my colleagues around the world must have become rather depressed that an academic book could achieve such a feat, the steep price was actually the result of algorithms feeding off each other and spiralling out of control. It turns out, it wasn’t just sales staff being creative: algorithms were calling the shots. </p>
<p>This eye-catching example was spotted and corrected. But what if such algorithmic interference happens all the time, including in ways we don’t even notice? If our reality is becoming increasingly constructed by algorithms, where does this leave us humans? </p>
<p>Inspired by such examples, my colleague <a href="http://allenslee.com">Prof Allen Lee</a> and I recently set out to explore the deeper effects of algorithmic technology <a href="https://demetis.wordpress.com/journal-publications/">in a paper</a> in the <a href="https://aisel.aisnet.org/jais/vol19/iss10/5/">Journal of the Association for Information Systems</a>. Our exploration led us to the conclusion that, over time, the roles of information technology and humans have been reversed. In the past, we humans used technology as a tool. Now, technology has advanced to the point where it is using and even controlling us.</p>
<p>We humans are not merely cut off from the decisions that machines are making for us but deeply affected by them in unpredictable ways. Instead of being central to the system of decisions that affects us, we are cast out in to its environment. We have progressively restricted our own decision-making capacity and allowed algorithms to take over. We have become artificial humans, or human artefacts, that are created, shaped and used by the technology.</p>
<p>Examples abound. In law, <a href="https://www.nytimes.com/2011/03/05/science/05legal.html">legal analysts are gradually being replaced</a> by artificial intelligence, meaning the successful defence or prosecution of a case can rely partly on algorithms. Software has even been allowed to <a href="https://www.propublica.org/article/machine-bias-risk-assessments-in-criminal-sentencing">predict future criminals</a>, ultimately controlling human freedom by shaping how parole is denied or granted to prisoners. In this way, the minds of judges are being shaped by decision-making mechanisms they cannot understand because of how complex the process is and how much data it involves. </p>
<p>In the job market, excessive reliance on technology has led some of the world’s biggest companies <a href="https://www.economist.com/business/2018/06/21/how-an-algorithm-may-decide-your-career">to filter CVs through software</a>, meaning human recruiters will never even glance at some potential candidates’ details. Not only does this put people’s livelihoods at the mercy of machines, it can also build in hiring biases that the company had no desire to implement, as <a href="https://www.reuters.com/article/us-amazon-com-jobs-automation-insight/amazon-scraps-secret-ai-recruiting-tool-that-showed-bias-against-women-idUSKCN1MK08G">happened with Amazon</a>.</p>
<p>In news, what’s known as <a href="https://www.nytimes.com/2009/08/24/technology/internet/24emotion.html?pagewanted=2&_r=2&partner=rss&emc=rss">automated sentiment analysis</a> analyses positive and negative opinions about companies based on different web sources. In turn, these are being used by trading algorithms that make automated financial decisions, without humans having to actually read the news. </p>
<h2>Unintended consequences</h2>
<p>In fact, algorithms operating without human intervention now play a significant role in financial markets. For example, <a href="https://onlinelibrary.wiley.com/doi/abs/10.1111/jofi.12186">85% of all trading</a> in the foreign exchange markets is conducted by algorithms alone. The growing <a href="https://uk.reuters.com/article/trading-blackbox-idINDEE84K06T20120521">algorithmic arms race</a> to develop ever more complex systems to compete in these markets means huge sums of money are being allocated according to the decisions of machines.</p>
<p>On a small scale, the people and companies that create these algorithms are able to affect what they do and how they do it. But because much of artificial intelligence involves programming software to figure out how to complete a task by itself, we often <a href="https://www.technologyreview.com/s/604087/the-dark-secret-at-the-heart-of-ai/">don’t know</a> exactly what is behind the decision-making. As with all technology, this can lead to unintended consequences that may go far beyond anything the designers ever envisaged. </p>
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<img alt="" src="https://images.theconversation.com/files/262861/original/file-20190308-150670-u38atx.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/262861/original/file-20190308-150670-u38atx.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=400&fit=crop&dpr=1 600w, https://images.theconversation.com/files/262861/original/file-20190308-150670-u38atx.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=400&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/262861/original/file-20190308-150670-u38atx.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=400&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/262861/original/file-20190308-150670-u38atx.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=503&fit=crop&dpr=1 754w, https://images.theconversation.com/files/262861/original/file-20190308-150670-u38atx.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=503&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/262861/original/file-20190308-150670-u38atx.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=503&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">Algorithms now effectively control financial markets.</span>
<span class="attribution"><a class="source" href="https://www.shutterstock.com/image-photo/stock-trader-tearing-out-his-hair-207769294">Dragon Images/Shutterstock</a></span>
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<p>Take the 2010 “Flash Crash” of the Dow Jones Industrial Average Index. The action of algorithms helped create the index’s single biggest decline in its history, <a href="https://www.theguardian.com/business/2015/apr/22/2010-flash-crash-new-york-stock-exchange-unfolded">wiping nearly 9%</a> off its value in minutes (although it regained most of this by the end of the day). A <a href="https://www.sec.gov/news/studies/2010/marketevents-report.pdf">five-month investigation</a> could only suggest what sparked the downturn (and <a href="https://www.sciencedirect.com/science/article/abs/pii/S1544612318307475">various other theories</a> <a href="https://www.sciencedirect.com/science/article/abs/pii/S1544612318307475">have been proposed</a>).</p>
<p>But the algorithms that amplified the initial problems didn’t make a mistake. There wasn’t a bug in the programming. The behaviour emerged from the interaction of millions of algorithmic decisions playing off each other in unpredictable ways, following their own logic in a way that created a downward spiral for the market.</p>
<p>The conditions that made this possible occurred because, over the years, the people running the trading system had come to see human decisions as an obstacle to market efficiency. Back in 1987 when the US stock market fell by 22.61%, some Wall Street brokers simply stopped picking up their phones to avoid receiving their customers’ orders to sell stocks. This started a process that, as author Michael Lewis put it in his book <a href="https://books.wwnorton.com/books/detail.aspx?ID=4294981104">Flash Boys</a>, “has ended with computers entirely replacing the people”.</p>
<p>The financial world has invested millions in superfast cables and <a href="https://arstechnica.com/information-technology/2016/11/private-microwave-networks-financial-hft/">microwave communications</a> to shave just milliseconds off the rate at which algorithms can transmit their instructions. When speed is so important, a human being that requires a massive 215 milliseconds to click a button is almost completely redundant. Our only remaining purpose is to reconfigure the algorithms each time the system of technological decisions fails.</p>
<p>As new boundaries are carved between humans and technology, we need to think carefully about where our extreme reliance on software is taking us. As human decisions are substituted by algorithmic ones, and we become tools whose lives are shaped by machines and their unintended consequences, we are setting ourselves up for technological domination. We need to decide, while we still can, what this means for us both as individuals and as a society.</p><img src="https://counter.theconversation.com/content/111436/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Dionysios Demetis has received funding from the European Commission. </span></em></p>From the law to the media we’re becoming artificial humans, mere tools of the machines.Dionysios Demetis, Lecturer in Management Systems, University of HullLicensed 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>
<figure class="align-center zoomable">
<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>
<figcaption>
<span class="caption"></span>
</figcaption>
</figure>
<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>
<figure class="align-center zoomable">
<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>
<figcaption>
<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>
</figcaption>
</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/807822017-07-28T16:34:23Z2017-07-28T16:34:23ZWhy crowds aren’t always wise: Lessons from mini-flash crashes on Wall Street<p>Blink. About <a href="http://bionumbers.hms.harvard.edu/bionumber.aspx?&id=100706&ver=4">300 milliseconds</a> just passed, the same time required for a lightning bolt to <a href="http://www.maine.gov/mema/prepare/prep_display.shtml?163524">travel 100,000 feet</a>, a satellite to <a href="http://howthingsfly.si.edu/ask-an-explainer/do-all-satellites-have-fly-same-speed-so-not-leave-their-orbit">fly two miles</a> or a stock price to swing <a href="http://www.nanex.net/aqck2/4178.html">from US$10 to $0.0001 and back</a>.</p>
<p>Wait, what?</p>
<p>Indeed, that actually happened to the shares of the software company Qualys a few years ago. Similar mini-flash crashes involving substantial, instantaneous price moves take place about <a href="http://money.cnn.com/2013/03/20/investing/mini-flash-crash/index.html">12 times a day</a>. </p>
<p>Remember the flash crash back in 2010, when hundreds of stocks <a href="https://www.sec.gov/news/studies/2010/marketevents-report.pdf">temporarily went bonkers</a> and the Dow Jones Industrial Average dove 1,000 points in a few minutes? Mini-flash crashes are the same thing yet on a smaller scale, with perhaps only one company’s shares <a href="https://arxiv.org/abs/1211.6667">going haywire</a> for a fraction of a second.</p>
<p>But they’re just as consequential, both for the individual stock and in the aggregate. Such bizarre events seem to contradict our basic beliefs about the fairness of values, the sophistication of modern markets and the oft-cited <a href="http://www.penguinrandomhouse.com/books/175380/the-wisdom-of-crowds-by-james-surowiecki/9780385721707/">wisdom of the crowd</a>. What’s going on?</p>
<p>To find out, we developed a <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=2975769">mathematical model</a> to explore how all of these ideas fit together. We initially presumed that as long as there were lots of sharp investors with broad-ranging market views, mini-flash crashes would be fairly uncommon.</p>
<p>Surprisingly, we observed a “too many cooks spoil the broth”-type effect instead. Even the wisest crowd, if it’s large enough, can rapidly devolve into a mad herd and bring on these wild events.</p>
<h2>Mini-flash crashes in a nutshell</h2>
<p>Over <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3769652/">20,000 mini-flash crashes</a> have been recorded since 2006, the year they really took off. Some were bigger than others, but many were <a href="https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3769652/">pretty severe</a>.</p>
<p>They’re momentary, but if you get caught in one, you might incur substantial <a href="https://www.ft.com/content/fe944768-7124-11da-836e-0000779e2340">trading losses</a>, <a href="https://www.ft.com/content/4089c7d0-bba4-11e6-8b45-b8b81dd5d080">reputational damage</a>, <a href="https://www.wsj.com/articles/merrill-lynch-to-pay-12-5-million-fine-for-mini-flash-crashes-1474906677">fines</a> and <a href="https://www.bloomberg.com/news/articles/2016-11-09/accused-flash-crash-trader-sarao-to-plead-guilty-in-chicago">legal woes</a>.</p>
<p>More broadly, they may <a href="https://www.sec.gov/news/pressrelease/2016-192.html">erode investors’ trust in markets</a>, violate <a href="http://www.bloomsbury.com/us/forecast-9781608198535/">Nobel Prize-winning theories</a> and even escalate into <a href="http://www.nytimes.com/2010/10/02/business/02flash.html">full-blown flash crashes</a> like the big one from 2010. In fact, that infamous flash crash began as a disruption in a <a href="http://www.nytimes.com/2010/10/02/business/02flash.html">single instrument</a>, the E-Mini S&P 500 futures contract.</p>
<p>Sounds pretty serious, huh?</p>
<p>Regulators agree and have <a href="https://www.sec.gov/rules/sro/nms/2012/34-67091.pdf">installed measures</a> in hopes of managing them. One rule wipes out trades that are <a href="https://www.nyse.com/publicdocs/nyse/markets/nyse-arca/NYSE%20Arca%20Rule%207.10.pdf">obviously wrong</a> (probably no one means to sell stock for hundredths of a penny). Another rule acts like a <a href="https://www.sec.gov/rules/sro/nms/2012/34-67091.pdf">circuit breaker</a> in your home, temporarily freezing markets when prices overheat.</p>
<p>Traders pitch in as well. For example, have you ever heard of a <a href="http://www.bbc.com/news/business-29454265">fat finger error</a>? Maybe you wanted to sell one share at $100 but accidentally unloaded 100 shares at $1 because of a mistaken keystroke or two. Bang, the stock plunges 99 percent instantly. Financial firms maintain <a href="https://www.sec.gov/news/pressrelease/2016-192.html">internal checks</a> such as operational risk controls to avoid such havoc.</p>
<p>Yet, despite all this, mini-flash crashes keep <a href="http://www.cnbc.com/2017/06/22/buyers-beware-lessons-from-the-ethereum-flash-crash.html">happening</a>, and some worry the problem is getting <a href="https://www.bloomberg.com/news/articles/2017-06-26/gold-plunges-as-1-8-million-ounces-traded-in-a-new-york-minute">worse</a>.</p>
<h2>An army of simulated investors</h2>
<p>To understand why the mini-flash crash problem just won’t go away, we designed a model that takes <a href="http://www.wiley.com/WileyCDA/Section/id-400799.html">all we know about investing</a> and subjected it to mathematical analysis and computer simulations so we could observe whether a group of traders armed with various strategies could steer clear of mini-flash crashes. </p>
<p>For instance, in developing our model, we wanted to ensure that our “investors” devised their strategies as if they were actually human. In addition to classic questions like “Where are prices going?,” today’s traders ponder much more sophisticated issues before buying or selling. They might ask, “How confident am I in this answer? How often should I check back to make sure it’s still right? Am I worried about betting the bank? Could my trades themselves impact the future and change whether I’m ultimately right?”</p>
<p>We plugged these considerations into mathematical formulas. Each of our investors was assigned specific parameters (like the models Wall Street traders use), which served as guides for how and when they would trade given various market conditions. </p>
<p></p><hr><p></p>
<p><iframe id="Uzge8" class="tc-infographic-datawrapper" src="https://datawrapper.dwcdn.net/Uzge8/2/" height="400px" width="100%" style="border: none" frameborder="0"></iframe></p>
<p></p><hr><p></p>
<p>In other words, we tried to make our simulated investors as complicated as real ones, with a healthy mix of characteristics.</p>
<p>Here’s what we found. </p>
<p>Our investors initially operated a calm and stable market, free of crashes. The supposed wisdom of the crowd prevailed, as long as certain conditions held true:</p>
<ol>
<li><p>All investors were confident in their opinions about future markets.</p></li>
<li><p>All investors fully reworked their models often. That is, models (like the ones described above) can become corrupted over time and need to be readjusted. A failure of investors to readjust their models likely contributed to the <a href="http://time.com/3741681/2000-dotcom-stock-bust/">dot-com bubble</a> or the <a href="https://www.forbes.com/2008/12/31/housing-bubble-crash-oped-cx_bb_0102bartlett.html">housing bubble</a>, when we watched prices go up and up and figured that the good times would last forever. If only we’d taken a step back to check ourselves, right? </p></li>
<li><p>All investors were hesitant to take big risks. </p></li>
<li><p>There weren’t too many investors. It’s not that we found a specific number here; rather we discovered that the crowd sometimes got so large that the extra stability provided by their varied opinions got outweighed by their tendency to stampede towards a mini-flash crash at the slightest tremor. </p></li>
</ol>
<p>But in a fast-moving market, these conditions didn’t always persevere. Even though investors were continually revising their views, they weren’t doing so fast enough to avoid being caught up in a herd mentality and selling off a stock along with everyone else in response to a lone investor’s opinion. </p>
<p></p><hr><p></p>
<p><iframe id="ABlMT" class="tc-infographic-datawrapper" src="https://datawrapper.dwcdn.net/ABlMT/3/" height="400px" width="100%" style="border: none" frameborder="0"></iframe></p>
<p></p><hr><p></p>
<p>Say some investor got spooked and started selling. This drove prices down a little, which may have worried a few others. They started selling too, causing prices to drop even further. Pretty soon, the whole market was unloading, and prices hit rock bottom. </p>
<p>If our investors weren’t sure about their views, this all happened much faster. They’d change their beliefs on a dime. We saw the same thing when they weren’t afraid of taking risks or didn’t step back enough to reassess their strategies.</p>
<p>What was most surprising for us, as previous believers in the strength of wise crowds, was that increased population size alone could be destabilizing. In fact, the number of investors active in our simulations was one of the greatest determinants of whether a mini-flash crash would ensue.</p>
<figure class="align-center ">
<img alt="" src="https://images.theconversation.com/files/179646/original/file-20170725-30134-6wzeq5.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&fit=clip" srcset="https://images.theconversation.com/files/179646/original/file-20170725-30134-6wzeq5.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=600&h=383&fit=crop&dpr=1 600w, https://images.theconversation.com/files/179646/original/file-20170725-30134-6wzeq5.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=600&h=383&fit=crop&dpr=2 1200w, https://images.theconversation.com/files/179646/original/file-20170725-30134-6wzeq5.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=600&h=383&fit=crop&dpr=3 1800w, https://images.theconversation.com/files/179646/original/file-20170725-30134-6wzeq5.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=754&h=482&fit=crop&dpr=1 754w, https://images.theconversation.com/files/179646/original/file-20170725-30134-6wzeq5.jpg?ixlib=rb-1.1.0&q=30&auto=format&w=754&h=482&fit=crop&dpr=2 1508w, https://images.theconversation.com/files/179646/original/file-20170725-30134-6wzeq5.jpg?ixlib=rb-1.1.0&q=15&auto=format&w=754&h=482&fit=crop&dpr=3 2262w" sizes="(min-width: 1466px) 754px, (max-width: 599px) 100vw, (min-width: 600px) 600px, 237px">
<figcaption>
<span class="caption">‘Tulipmania’ in the 17th century is often considered the first market bubble. Here, monkeys portray Dutch speculators buying and selling rare tulip bulbs.</span>
<span class="attribution"><a class="source" href="https://commons.wikimedia.org/wiki/File:Jan_Brueghel_the_Younger,_Satire_on_Tulip_Mania,_c._1640.jpg">Brueghel the Younger</a></span>
</figcaption>
</figure>
<h2>History repeats</h2>
<p>Our research suggests it may be impossible to completely stop mini-flash crashes, as recent high-profile plunges in the prices of <a href="https://www.bloomberg.com/news/articles/2017-06-26/gold-plunges-as-1-8-million-ounces-traded-in-a-new-york-minute">gold</a> and <a href="http://www.cnbc.com/2017/07/07/silver-plunges-in-yet-another-mysterious-market-flash-crash.html">silver</a> demonstrate. Many market observers claim that such instantaneous tumbles are the “<a href="https://blogs.cfainstitute.org/marketintegrity/2015/07/24/sleepless-in-seattle-is-living-with-threat-of-market-flash-crashes-the-new-normal/">new normal</a>.”</p>
<p>Perhaps it’s not so startling. After all, back in 1841, Scottish journalist Charles Mackay had already brought the recurring nature of bubbles and crashes to the public’s attention in “<a href="https://www.gutenberg.org/files/24518/24518-h/dvi.html">Memoirs of Extraordinary Popular Delusions and the Madness of Crowds</a>.” To some extent, it’s no wonder that they should be frequent and rapid these days, given the speed of <a href="https://www.bloomberg.com/news/articles/2014-07-24/high-frequency-traders-find-microwaves-suit-their-need-for-speed">today’s markets</a>.</p>
<p>That tulip trading persisted late into the night at Dutch taverns (after many rounds) has been cited as a <a href="https://mitpress.mit.edu/books/famous-first-bubbles">potential cause of “Tulipmania</a>,” an event in the 1600s where a single bulb is rumored to have cost as much as <a href="https://www.gutenberg.org/files/24518/24518-h/dvi.html">50 live pigs</a>. While we’re a long way from such things, the line between a wise crowd offering stability and a mad one creating chaos is clearly as thin as ever.</p><img src="https://counter.theconversation.com/content/80782/count.gif" alt="The Conversation" width="1" height="1" />
<p class="fine-print"><em><span>Alexander Munk receives funding from a University of Michigan Rackham Predoctoral Fellowship. </span></em></p><p class="fine-print"><em><span>Erhan Bayraktar receives funding from the National Science Foundation. </span></em></p>New research suggests mini-crashes, in which the price of a single stock or commodity temporarily goes haywire, may be unstoppable.Alexander Munk, Ph.D. Candidate in Mathematics, University of MichiganErhan Bayraktar, Professor of Mathematics, University of MichiganLicensed 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/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/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/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/137352013-04-25T01:46:28Z2013-04-25T01:46:28ZWhy the AP hack is likely to happen again<figure><img src="https://images.theconversation.com/files/22864/original/c7bbmt6w-1366853533.jpg?ixlib=rb-1.1.0&q=45&auto=format&w=496&fit=clip" /><figcaption><span class="caption">An embarrassing Twitter hack caused a plunge in the markets and revealed the weakness in our reliance on technology.</span> <span class="attribution"><span class="source">AAP</span></span></figcaption></figure><p>It has been a bad couple of weeks for social media and Twitter in particular. The degree of <a href="http://www.guardian.co.uk/world/2013/apr/22/boston-bombings-witchhunt-social-media">misinformation</a> spread by social media sites in the aftermath of the bombings at the Boston marathon has given the world pause to reconsider its ongoing role in breaking news.</p>
<p>If there is wisdom in the crowds, it has been notably absent on Twitter. Following on this, and perhaps taking advantage of the nervousness of the US population in the wake of the bombings, someone <a href="http://www.guardian.co.uk/business/2013/apr/23/ap-tweet-hack-wall-street-freefall">hacked</a> the Twitter account of news wire service Associated Press sending out a tweet claiming two explosions had occurred in the White House and that US President Barack Obama had been injured. </p>
<p>The Dow Jones Industrial Average plunged 143 points before recovering after it became obvious that the report was untrue.</p>
<h2>Were the machines to blame?</h2>
<p>It is not clear whether the crash was due to automated algorithmic trading software that scans social media and news feeds for such events. These systems have <a href="http://theconversation.com/the-rise-of-the-machines-high-frequency-trading-and-dark-pools-12784">been blamed</a> for so-called “flash-crashes” in the past. </p>
<p>Human traders were also to blame however, with <a href="http://www.guardian.co.uk/business/2013/apr/23/ap-tweet-hack-wall-street-freefall">some expressing</a> their dismay at how easily the market can be manipulated. </p>
<p>The human traders should have been better than the computer algorithms at picking up the subtle signs that the tweet from the Associated Press account was fake. The capitalisation of the word explosion in the sentence “Two Explosions in the White House and Barack Obama is injured” and the use of the US President’s first name deviate from the Associated Press normal style. More importantly, there was no corroboration of the news from other independent sources. </p>
<p>The challenge presented by algorithmic trading is that the financial opportunity may exist only for a fraction of a second and so they are designed to act ahead of everyone else in the market. In these situations, there is rarely time to validate sources of a story.</p>
<h2>The two lessons learned</h2>
<p>The hack of the AP Twitter account highlights two things. Firstly, the absolute need for <a href="http://en.wikipedia.org/wiki/Multi-factor_authentication">two-factor authentication</a> to prevent accounts like this one from being hacked so easily. Two-factor authentication means that even if a password is compromised, hackers are not able to use it without the device that generates a second token that completes the login process. </p>
<p>Twitter is <a href="http://www.wired.com/threatlevel/2013/04/twitter-authentication/?cid=co7401494">only now</a> rolling out this functionality. The fact that it has taken this long is puzzling and verging on negligent given the consequences of security breaches of their user’s accounts. The AP account password was probably obtained from <a href="http://hosted.ap.org/dynamic/stories/A/AP_TWITTER_HACKED?SITE=TXWIC&SECTION=HOME&TEMPLATE=DEFAULT">phishing emails</a> that had been sent to staff only a few hours before the hacked account was used to tweet the message.</p>
<p>The second lesson from this incident has been to reinforce the need for Twitter users everywhere to treat all information on Twitter with absolute caution until it has been verified by a number of sources. </p>
<p>This is probably more difficult than it sounds as the attraction of Twitter is its immediacy and the belief that you are getting news in real-time. Even when not hacked, media sites have fallen into this trap themselves, either sending out unverified news items or re-tweeting them. Reuters recently <a href="http://www.guardian.co.uk/media/2013/apr/22/reuters-deputy-social-media-editor">sacked</a> a senior web producer Matthew Keys for doing exactly this. Keys had been tweeting information he had picked up off police radio that later turned out to be incorrect.</p>
<h2>The hackers behind the hack</h2>
<p>As for who actually hacked the @AP account, a group calling itself the <a href="http://syrianelectronicarmy.com/">Syrian Electronic Army</a> (SEA) has <a href="http://www.theverge.com/2013/4/23/4257392/ap-twitter-hacked-claims-explosions-white-house-president-injured">claimed credit</a>. </p>
<p>The SEA are a hacking group formed to support the Syrian government against the Syrian rebels and what it saw as biased media reporting. It has <a href="http://www.buzzfeed.com/ryanhatesthis/everything-you-need-to-know-about-the-syrian-electronic-army">claimed responsibility</a> for a number of hacks of Twitter accounts and websites of media outlets such as NPR, 60 Minutes and the BBC Weather.</p>
<h2>The challenge of hearing the signal</h2>
<p>At the end of the day, the hack did limited damage and its impact on the stock exchange was likely to be an unforeseen side effect of the fake tweet. It worked because of the heightened sensitivity brought about by the recent Boston bombings. </p>
<p>It is unlikely that media agencies will have learned their lesson from this however and so we are undoubtedly going to see this episode repeated. Eventually however, society will learn the limitations of social media as a mechanism for distributing information and news. An ever-increasing challenge for all of us is being able to distinguish the signal from the noise.</p><img src="https://counter.theconversation.com/content/13735/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>It has been a bad couple of weeks for social media and Twitter in particular. The degree of misinformation spread by social media sites in the aftermath of the bombings at the Boston marathon has given…David Glance, Director, Centre for Software Practice, The University of Western AustraliaLicensed as Creative Commons – attribution, no derivatives.