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Financial Analysts Journal Volume 70 Number 3 2014 CFA Institute PERSPECTIVES High-Frequency Trading and Its Impact on Markets Maureen O'Hara At the 2013 CFA Institute Financial Analysts Seminar, held in Chicago on 22-25 July, Maureen O'Hara discussed a new market paradigm: Trading has become faster, and market structure has fundamentally changed. In today's market, high-frequency traders (HFTs) act on information revealed by low-frequency traders (LFTs). To survive, LFTs must avoid being detected by predatory algorithms of HFTs. LFTs can thrive by adopting trading strategies appropriate to the high-frequency trading world. H igh-frequency trading is now the norm, and it is not going away. Across markets, both in the United States and in global settings, the bulk of trading is from high-frequency traders (HFTs). For portfolio managers and those involved in investment management, the good old days of running a portfolio and paying little attention to how it is traded are long gone. Managers need to understand how the new markets work because trading affects alpha. The high-frequency trading world is mindboggling. The Chicago Mercantile Exchange (CME) and NASDAQ, for example, have a joint venture to build a better linkage between the CME's computers in Chicago and NASDAQ's servers in New Jersey. The challenge concerns transmission speed, so they are building towers to transmit orders via microwaves. To minimize the effect of the curvature of the earth, the towers are 60 feet above the ground, which saves a mere 4 milliseconds in the transmission of orders between New Jersey and Chicago. How fast is this? It takes the human eye 400-500 milliseconds to recognize and respond to visual stimuli. So, in the blink of an eye, you are now left far behind by the high-frequency crowd. But high-frequency trading is not just about speed; a new paradigm is at work. Fundamental aspects of the market have changedfor example, the nature of market making and even basic Maureen O'Hara is the Robert W. Purcell Professor of Finance at the Johnson Graduate School of Management, Cornell University, Ithaca, New York. 18\twww.cfapubs.org\b concepts like liquidity. Whenever paradigms shift, things that used to work stop working. In this presentation, I will discuss how high-frequency trading is different, how it is changing markets, and what this implies for portfolio managers and regulators. My discussion is based on my work with David Easley and Marcos Lpez de Prado.1 Easley is an economics and information sciences professor at Cornell University. Lpez de Prado was head of high-frequency futures trading at Tudor Investment Corporation and is currently developing quantitative strategies for Guggenheim Partners. I am a finance professor and chairman of the board of a broker/dealer firm. Our diverse backgrounds speak to the challenges of trying to work on microstructure issues in this new world. The databases are so big, a hedge fund is required just to be able to handle the computing power! A New Paradigm at Work: Is Speed the Issue? David Leinweber (2009) has made the point that in the context of trading, there is nothing new about being faster. In the 19th century, carrier pigeons brought the news of Napoleon's defeat at Waterloo, with the Rothschild banking family most notably profiting from trading on this almost real-time information. In the past, the inventions of the telegraph, the telephone, and the radio allowed speed advantages to some traders over others. Today, sending information by racing pigeons would be laughable, but 200 years ago, it was cutting-edge technology. 2014 CFA Institute High-Frequency Trading and Its Impact on Markets High-frequency trading is not just lowfrequency trading on steroids; markets actually behave very differently now. HFTs have been characterized as \"cheetah traders\" by Bart Chilton of the US Commodity Futures Trading Commission (CFTC). I do not agree with that assessment. As in all areas of the markets, some HFTs (or, more precisely, the computer programmers behind some HFTs) are behaving and some are misbehaving. Actual high-frequency trading is done by computers or \"silicon traders,\" but low-frequency traders (LFTs) are still humans. Today, a battle is being waged between the silicon traders and the humans. HFTs use super-high-speed computers and co-located servers. To understand what they do, consider all the stocks or all the futures contracts out there and think about calculating a giant variance-covariance matrix of how each security moves. When a stock goes up, an HFT instantly computes a probability that another stock will go up on the basis of predicted correlations and then trades in that stock. A lot of what HFTs do, such as predicting relationships between assets, is based on strategy. Because they are predictive, they also act on information revealed by LFTs (LFTs are the rest of us). HFTs engage in sequential games, and sometimes they behave like predators and take advantage of LFTs. They can do so because they are working under a different paradigm. Humans like to think about time. For example, the market opens at 9:30 a.m. and closes at 4:00 p.m. An order is sent to a broker, and she chops it up into pieces and trades those pieces over the course of the day. That is a natural way to think about trading, but it's not the only way. Machines do not \"think\" in terms of time. They think in terms of cycles, and in trading, this often relates to an amount of volume. For example, suppose that of the next 200,000 e-mini futures contracts, a high-frequency trader wants to buy 50,000 of them. That trading strategy is based on a volume clock, not a time clock. How long will it take to trade 300,000 futures contracts? It depends. In the middle of the night, it could take quite a while; during the day, when markets are busier, it could take 10 minutes. Looking at the world the way machines do gives HFTs an advantage. Figure 1 compares the standardized return distributions of the e-mini S&P 500 futures contract calculated every minute and calculated for every 1/50 of the daily volume (i.e., returns are calculated using a volume clock). The figure also gives the normal distribution for returns. The actual time-weighted distribution is skinnier and has fatter tails than the normal distribution, properties that are associated with leptokurtosis. In financial markets, these properties are undesirable in that extreme losses occur at a higher probability than would be expected with the normal distribution. However, the volume clock Figure 1. \u0007Standardized Returns on the E-Mini S&P 500 Futures Contract Density 0.25 0.20 0.15 0.10 0.05 0 -5 -4 -3 -2 -1 0 1 2 3 4 5 Standardized Return Time Clock Volume Clock Normal Distribution (Same Bins as Time Clock) Sources: Easley, Lpez de Prado, and O'Hara (2012b); based on Bloomberg data from 1 January 2008 to 22 October 2010. May/June 2014 www.cfapubs.org 19 Financial Analysts Journal distribution behaves more like the normal distribution, which matters because it is easier to predict something that has a better-behaved distribution. The machines predict on the basis of the betterbehaved distribution because they are thinking with a volume clock, whereas humans are thinking with a time clock. The new paradigm is thus \"event based,\" where volume takes the place of time. Machine vs. Machine Spreads are small now because a lot of highfrequency trading is intermarket arbitragebuying here and selling there. HFTs basically move liquidity from a market where it is more plentiful to a market where it is less plentiful. This is the good side of high-frequency trading. There is also a bad side. HFTs can use predatory algorithms, whereby their own actions can trigger a microstructure mechanism with a foreseeable outcome. Examples include quote stuffers, quote danglers, and pack hunters. Such activities are illegal but are hard to catch. A quote stuffer overwhelms an exchange with messages, with the sole intent of slowing down competing algorithms. Quote danglers enter and instantaneously cancel limit orders, with the goal of obfuscating the quote process. In O'Hara (2010), I described a situation where Investment Technology Group (ITG) had a client who wanted to buy and the ITG trading algorithm placed a buy limit order. Then, a high-frequency silicon trader attempted to raise the price by entering and immediately canceling orders over and over again, never actually trading. The HFT's goal was to entice the ITG algorithm to raise its price, which meant it was essentially competing with itself. There is a happy ending to this story: ITG canceled the order and moved it someplace else because the company has anti-gaming technology that watches for this type of problem. The HFT then turned its predatory algorithm toward somebody else. Another example of predatory algorithms involves pack hunters: Several HFTs independently become aware of each other's activities and then form a pack to maximize the chance of triggering a cascading effect. In one variant of such behavior, HFTs try to force a stop loss by moving the quotes up and down to find the hidden stop orders. When they find them and trigger the stops, the market has to go down. Similar predatory algorithms can be deployed when LFTs use a predictable algorithm, such as TWAP (time-weighted average price). It is not hard for HFTs to step in front of predictable algorithms, much to the detriment of LFTs. Not every weird thing that happens to the market is the result of \"evil\" HFTs. Figure 2 shows the price pattern for Coca-Cola on 19 July 2012. The pattern is completely deterministic or predictable. On this day, IBM, McDonald's, and Apple displayed similar patterns. This market is not efficient; a naive trader could figure out the predictable oscillating pattern. But this market behavior was not the result of a sophisticated, manipulative HFT strategy. Subsequent investigation revealed that the cause was a \"dumb\" algorithma broker's computer program that was simply not well crafted or monitored. Similarly, India's flash crash, which occurred on 5 October 2012, was caused by a trader who sent in an order with far too many zeroes. Markets today are fast and highly technological. Without proper controls, these types of episodes will arise. Figure 2. \u0007Price Pattern for Coca-Cola, 19 July 2012 Price ($) 77.60 77.40 77.20 77.00 76.80 76.60 76.40 10:00 a.m. 11:00 a.m. 12:00 p.m. 1:00 p.m. 2:00 p.m. 3:00 p.m. Source: Based on data from Bloomberg Finance L.P. 20\twww.cfapubs.org\b 2014 CFA Institute High-Frequency Trading and Its Impact on Markets Big Data One reason why HFTs are so successful is that they base decision making on \"big data.\" For example, HFTs look at so-called Level III quotes. The rest of us are simply looking at the trade price or quoted spread, but HFTs are looking at the order book depth. Again, HFTs are strategic, and they want to figure out where their orders are going to be. They know the exact way that exchangematching engines work, so they optimize against the exchange-matching engine where they trade. They estimate the order's position in the queue and other players' liquidity needs and consider asymmetric information. HFTs also use natural language programing (NLP), which allows for trading on the basis of new \"fundamental\" information by reading millions of webpages at once. NLP derives an aggregate statistic, like an index, based on all that reading and translates it into an immediate buy or sell. RavenPack and Reuters are big players in developing these market sentiment indices. Remember Watson, IBM's supercomputer that played on the television game show Jeopardy? Watson now has a job working for the hedge funds, reading webpages and deciding how to trade. Figure 3 shows the US stock market reaction to the \"Tweet Crash\" of 23 April 2013, in which a hacker posted a false tweet on the Associated Press (AP) Twitter account stating that an explosion had occurred at the White House, injuring President Obama. This tweet was instantly read and translated into a trading signalall the consequence of using big data. Of course, as soon as the AP tweeted that its account was hacked and the story was false, the market immediately went back up. Such stories may sound like science fiction, but they are not. Frankly, as an academic, a chairman of a broker/dealer firm, and an LFT, I find it difficult to keep up with these fast and changing markets. The good news is that LFTs do not have to swim faster than the HFT \"sharks\"; they simply have to swim faster than other LFT \"fish.\" HFTs are scary because they can take advantage of LFTs. For example, if a broker chops an order up and sends it in at the beginning of every minute, a higher percentage of trades will be executed at the first second of the minute. For a silicon trader, this pattern is not hard to figure out. If LFTs signal what they are doing, then HFTs can take advantage of them. Figure 4 shows the percentage of orders per trade size in e-mini S&P 500 futures based on data for 2011. The little spikes around the round order sizes of 5, 10, and 100 come from an LFT or a GUI (pronounced \"gooey\") trader, so-called because the trader is a human being who uses a graphical user interface. Humans like round numbers, such as 5, 10, and 100, but machines do not care about round numbers. Once a trader uses round lot numbers, they flag themselves as human LFTs. Figure 3. \u0007US Stock Market Reaction to the \"Tweet Crash\" of 23 April 2013 Dow Jones Industrial Average 14,700 14,650 False Tweet Posted 1:07 p.m. 14,600 10:00 a.m. 11:00 a.m. 12:00 p.m. 1:00 p.m. 2:00 p.m. 3:00 p.m. Source: Based on data from FactSet. May/June 2014 www.cfapubs.org 21 Financial Analysts Journal Figure 4. \u0007Percentage of Orders per Trade Size in E-Mini S&P 500 Futures, 2011 Percentage of Total Trades 0.5 0.4 0.3 0.2 0.1 0 1 10 100 1,000 Trade Size Source: Easley et al. (2012b). With New Risks Come New Tools It is scary out there, but it does not necessarily have to stay that way. We need to become smarter, and we need better tools to understand the dynamics of markets. The old rules of thumb do not work. For example, regulation that comes after an event occurs typically does not work. Think about circuit breakers: They kick in after the market falls apart. This mechanism is not helpful in a high-frequency world; the market must close before it falls apart. The flash crash of 6 May 2010 ended because the CME had something called \"stop logic,\" which looks at all the orders on its book and considers the ramifications of those orders being executed. The \"stop logic\" predicted that the market would tumble and closed the market before further damage could occur. The bottom line is that in a super-fast world, regulation must be executed in advance. But to regulate in advance, regulators need prediction tools. Over the years, my co-authors and I have written a variety of papers on something called \"PIN,\" which stands for the probability of informed trading (see, e.g., Easley, Hvidkjaer, and O'Hara 2002). In microstructure, we believe that bid-ask spreads reflect the risk that the market maker faces in trading. If there are informed traders who know more about where things are going and uninformed traders who don't have special information, the market maker knows he will lose if he trades with the informed trader but will usually win if he trades with an uninformed trader. The market maker sets spreads to break even or earn a small profit, and to do so, he needs to know the probability of the 22\twww.cfapubs.org\b informed trade. PIN models provide a way to estimate this probability, but this empirical technique is difficult to implement given the huge trade volumes in today's high-frequency markets. Our new trading tool is VPIN (volume synchronized probability of informed trading), which captures the same notion that when there is new information, people who have the information can benefit only if they trade on it. If a trader believes that a stock is undervalued, she will buy it. If other people share that nonpublic information, then more buys than sells will occur. This imbalance moves prices. VPIN is a way to measure this imbalance, and it provides a measure of the market's \"toxicity.\" VPIN is estimated using a volume clock, and it works by capturing the imbalance between buy-initiated volume and sell-initiated volume over volume increments. Figure 5 shows the e-mini S&P 500 futures during the flash crash of 6 May 2010. Our VPIN calculation itself does not seem to be doing anything particularly spectacular, but the cumulative distribution function (CDF) of the VPIN explains what is to come (a CDF essentially tells you the probability that a random variable is less than or equal to a given number X). As the VPIN measure goes up, its cumulative distribution function enters unusual territory. In fact, by 11:56 a.m., the realized value of the VPIN was in the 10% tail of its distribution (meaning that 90% of the time, VPINs are below this level); that is, this market was very toxic. And the VPIN keeps going up: By 1:08 p.m., it is at the 1% tail of the distribution (or 99% based on the CDF)almost an hour and a half before the market crashes at 2:32 p.m. 2014 CFA Institute High-Frequency Trading and Its Impact on Markets Figure 5. \u0007E-Mini S&P 500 Futures and VPIN, 6 May 2010 Probability E-Mini S&P 500 Market Value ($) 60,000 1.0 0.9 59,000 0.8 58,000 0.7 Market Value 0.6 0.5 57,000 CDF(VPIN) 56,000 0.4 0.3 0.2 55,000 VPIN 54,000 0.1 0 2:31 a.m. 53,000 9:55 a.m. 11:18 a.m. 1:12 p.m. 2:24 p.m. 2:48 p.m. 3:19 p.m. 4:02 p.m. Source: Easley, Lpez de Prado, and O'Hara (2011). The CFTC-SEC report on the crash noted that the book of buy orders in the e-mini were hollowing out all morning, and no one noticed because HFTs kept coming in and putting in orders at the bid, keeping prices up.2 Then, all of a sudden, the silicon traders reached their limits and stopped buying. There was nothing in the book; orders that had been there moved away in microseconds. Therefore, the market plunged. This buildup of toxicity was captured by the VPIN, suggesting it is a potent tool for monitoring markets going forward. Interestingly, the Chicago Board Option Exchange's Volatility Index (VIX) was completely unstable during the crash. Its behavior before the crash was also completely different from that of the VPIN, as shown in Figure 6. The VIX did not react until the market collapsed. This is not really surprising: The VIX was not designed to pick up this toxicity. In high-frequency markets, the risks are different and so, too, must be the risk management tools. What Can LFTs Do? First off, for LFTs to take back their markets from HFTs, they need a new attitude. LFTs cannot defeat HFTs and cannot become HFTs. Investment managers and portfolio traders need to understand May/June 2014 that markets are now different. I think part of HFTs' success in the past was the result of LFTs' waiting for things to go back to the way they were, but they won't. LFTs need to learn new ways to look at the data, and they need to be cognizant of the new risks that arise in these markets. Portfolio managers and traders also need to have smarter brokers who can go head-to-head with quantitative brokers. When quantitative brokers trade a eurodollar future, for example, they do not put a trade in for a particular eurodollar future. They look at all the eurodollar futures and try to figure out the combinations in the spreads. One of the things that they look for, interestingly enough, is hidden liquidity. Smart brokers who specialize in searching for liquidity and avoiding a footprint are crucial for LFTs. The goal of LFTs is to be invisible to the HFTs. Smart execution algorithms can help, but neither VWAP nor TWAP are particularly advisable because their deterministic nature means that both can be taken advantage of by HFTs. Figure 7 shows the tradeoff between volume horizon and the expected loss on the trade.3 Implementation shortfall, which measures transaction costs from the time a trader decides to trade to the time he actually trades, is composed of a liquidity component and a timing component. www.cfapubs.org 23 Financial Analysts Journal Figure 6. \u0007The VIX and VPIN, 6 May 2010 Probability Market Value ($) 1.0 42 0.9 40 CDF(VPIN) 0.8 VPIN 0.7 38 36 0.6 34 0.5 32 CDF(VIX) 0.4 30 0.3 28 0.2 VIX 0.1 26 0 24 12:00 a.m. 2:52 a.m. 5:45 a.m. 8:38 a.m. 11:31 a.m. 2:24 p.m. 5:16 p.m. 8:09 p.m. 11:02 p.m. Source: Easley et al. (2011). Figure 7. \u0007Tradeoff between Liquidity Risk and Timing Risk Expected Trading Loss ($) 12,000 10,000 8,000 6,000 4,000 Probabilistic Loss 2,000 Timing Component Liquidity Component 0 0 10,000 20,000 30,000 40,000 50,000 Volume Horizon Trading immediately means liquidity costs will be high. By waiting, liquidity costs will go down, but timing risk will be higher because prices could move in an unfavorable direction. Ideally, traders need to find the point where the timing risk and liquidity component are minimized. 24\twww.cfapubs.org\b Easley, Lpez de Prado, and I developed a smarter VWAP that uses the big data toxicity information in the market. To understand how it works, consider a trade of 50,000 shares. If the market is currently overrun with people who want to sellthe market is imbalanced on the 2014 CFA Institute High-Frequency Trading and Its Impact on Markets sell sidethen a trader who wants to buy 50,000 shares has low liquidity costs. In contrast, if the market is already full of traders who want to sell, then liquidity costs will be high for a trader who wants to sell because she is trying to take liquidity from a market that does not have liquidity. Our trading algorithm uses the information in a trade, combined with an estimate of the market's current toxicity, to reduce trading costs. Figure 8 illustrates how quickly traders should trade when they are buying in a selling market. Our algorithm can outperform both standard VWAPs and volume participation rate strategies, which shows the value that can be gained by portfolio managers by using smarter trading tools. Conclusion We need to recognize that a new paradigm exists. Markets are not just faster; they are different. LFTs have to change; they do not have to be faster, but they do have to be smarter. HFTs cannot survive by trading only with other HFTs. Doing so has driven some HFTs out of business. Portfolio managers and traders need to think about the high-frequency trading world expanding beyond trading to information. The same technology that can send orders so swiftly can also get fundamental information more quickly than you can. The rise of the silicon analyst might be an interesting topic for a future presentation. Question and Answer Session Question: What role do exchanges play as markets change? O'Hara: Exchanges have to get smarter. Exchanges have been catering to HFTs because they generate so much volume for the exchanges. Eurex gives the schematic of its matching engine to anybody who wants it, so HFTs can build it into their algorithms. But exchanges have to build better tools to monitor the risk that comes about in this environment. Some exchanges are beginning to think through new designs for trading platforms. Also, I believe regulators must get smarter, and they are. The US SEC now has MIDAS, which is basically a high-frequency trading package that lets the SEC watch the market in the same way HFTs do. Question: What are your thoughts on the transaction tax proposed in Europe to slow down trading? O'Hara: I am not a fan. My views are similar to those of Harris, Ritter, and Schaefer (2014). I believe it will make European markets less attractive to HFTs and potentially less attractive to all traders because liquidity may be reduced. It won't Figure 8. \u0007A \"Smarter\" VWAP Expected Trading Loss ($) 12,000 10,000 8,000 6,000 Probabilistic Loss 4,000 Timing Component 2,000 Liquidity Component 0 0 10,000 20,000 30,000 40,000 50,000 Volume Horizon Note: This picture is drawn for particular parameters for the model developed in Easley, Lpez de Prado, and O'Hara (forthcoming). Source: Easley et al. (forthcoming). May/June 2014 www.cfapubs.org 25 Financial Analysts Journal become a norm because the United States and London won't do it. We've already seen what happened in France and Italy when they instituted a transaction tax. Their markets are down 20% in volume. The reality is that you're going to want to trade where liquidity is greatest. Although I do not like transaction taxes, I believe some things clearly do need adjusting in the high-frequency trading world. Question: Have HFTs helped improve liquidity? O'Hara: Before high-frequency trading, there was your friendly New York Stock Exchange specialist, who earned a very good living providing liquidity. When we talk about how much money HFTs make, we are talking about the price for someone to provide liquidity in the market. Liquidity costs are lower now than they've ever been. HFTs actually do provide a service: When a trader sells, HFTs are buying. I believe that is a valuable service to the markets. Question: Could fees for excessive order cancelations slow down HFTs? O'Hara: Futures markets have fees for excessive cancelations. Equity markets traditionally have not imposed them. Suppose you have an equity algorithm that is trying to find liquidity, and there are 50 different trading venues. Algorithms put orders in these venues to see whether anything is there, and if no liquidity is found, the algorithm cancels the order. Searching for liquidity is not evil, so large numbers of cancelations are not necessarily bad. Similarly, in futures, when traders begin to move in and across markets, they may be trying to hedge their position in futures, given what they are doing in the physical equities. In the exchange-traded fund world, for example, a trader may be using the futures to hedge all those underlying equities. So, it gets complicated. The London Stock Exchange has imposed a message cap, but the cap is so large that it is rarely violated. The argument for setting it so high was that anyone who trades derivatives cancels a lot and anyone who trades in fragmented markets, such as equities, cancels a lot as well. I do believe excessive charges make sense because the message traffic puts broker/dealer firms at a disadvantage. We have to build all kinds of capacity, not for our clients' trades, but for all these messages because we have to keep every single message. But how to structure these charges is not entirely clear. Question: Why has it taken so long for the concept of the volume clock to catch on? O'Hara: The volume clock is not new. It goes back to the 1960s, when such people as famed mathematician Benoit Mandelbrot were writing papers that suggested that volumes may be a better way to measure frequencies. It didn't catch on, in large part because it's hard to translate between a volume clock and a time clock. If you're a money manager, it is hard to move back and forth. What has changed now is that the major players trade on a volume clock. We don't know enough yet about its statistical properties going forward, but I believe that the volume clock is always going to be a better-behaved distribution. It won't be a perfectly behaved distribution, but its statistical properties will be better. Question: Describe the range of traders who would be characterized as HFTs. O'Hara: HFTs are a very broad group. A highfrequency trader is essentially a computer program that buys and sells with the lowest possible latency. Latency refers to a delay in a system. When we talk about it in trading, it's essentially the delay in getting an order from you to the exchange and back. So, the smaller the latency, the faster the system is. High-frequency trading is now the bulk of market making in most markets. At times in equity markets, they have been up to 50% of the volume; in futures and currencies, even more. Most major hedge funds use high-frequency trading. There are also specialized high-frequency trading firms that trade on a proprietary basis. This article qualifies for 0.5 CE credit. Notes 1. See, e.g., Easley, Lpez de Prado, and O'Hara (2011, 2012a, 2012b, 2013, forthcoming). 2. CFTC and SEC, \"Findings Regarding the Market Events of May 6, 2010\" (30 September 2010): www.sec.govews/ studies/2010/marketevents-report.pdf. 3. See Almgren and Chriss (2000/2001) for more discussion about these concepts. References Almgren, Robert, and Neil Chriss. 2000/2001. \"Optimal Execution of Portfolio Transactions.\" Journal of Risk, vol. 3, no. 2 (Winter):5-39. 26\twww.cfapubs.org\b Easley, David, Soeren Hvidkjaer, and Maureen O'Hara. 2002. \"Is Information Risk a Determinant of Asset Prices?\" Journal of Finance, vol. 57, no. 5 (October):2185-2221. 2014 CFA Institute High-Frequency Trading and Its Impact on Markets Easley, David, Marcos Lpez de Prado, and Maureen O'Hara. 2011. \"The Microstructure of the Flash Crash.\" Journal of Portfolio Management, vol. 37, no. 2 (Winter):118-128. . 2012a. \"Flow Toxicity and Liquidity in a High-Frequency World.\" Review of Financial Studies, vol. 25, no. 5 (May):1457-1493. . 2012b. \"The Volume Clock: Insights into the High-Frequency Paradigm.\" Journal of Portfolio Management, vol. 39, no. 1 (Fall):19-29. , eds. 2013. High-Frequency Trading: New Realities for Traders, Markets and Regulators. London: Risk Books. . Forthcoming. Mathematical Finance. \"Optimal Execution [ADVERTISEMENT] Horizon.\" Harris, Larry, Jay Ritter, and Stephen Schaefer. 2014. \"Statement of the Financial Economists Roundtable, October 2013: Financial Transaction Taxes.\" Financial Analysts Journal, vol. 70, no. 1 (January/February):5-8. Leinweber, David. 2009. Nerds on Wall Street: Math, Machines and Wired Markets. Hoboken, NJ: John Wiley & Sons. O'Hara, Maureen. 2010. \"What Is a Quote?\" Journal of Trading, vol. 5, no. 2 (Spring):10-16. CFA INSTITUTE BOARD OF GOVERNORS 20132014 Chair Charles J. Yang, CFA T&D Asset Management Tokyo, Japan Vice Chair Aaron Low, CFA Lumen Advisors Singapore CFA Institute President and CEO John D. Rogers, CFA CFA Institute Charlottesville, Virginia Saeed M. Al-Hajeri, CFA Abu Dhabi Investment Authority Abu Dhabi, United Arab Emirates Giuseppe Ballocchi, CFA Le Grand-Saconnex, Switzerland Heather Brilliant, CFA Morningstar, Inc. Chicago, Illinois May/June 2014 Beth Hamilton-Keen, CFA Mawer Investment Management Ltd. Calgary, Alberta, Canada Robert Jenkins, FSIP London Business School London, United Kingdom James G. Jones, CFA Sterling Investment Advisors, LLC Bolivar, Missouri Attila Koksal, CFA Standard Unlu A.S. Istanbul, Turkey Mark J. Lazberger, CFA Colonial First State Global Asset Management Sydney, Australia Alan M. Meder, CFA Duff & Phelps Investment Management Co. Chicago, Illinois Matthew H. Scanlan, CFA RS Investments San Francisco, California Jane Shao, CFA Lumiere Pavilions Limited Beijing, China Sunil Singhania, CFA Reliance Mutual Fund Mumbai, India Roger Urwin Towers Watson Limited Surrey, United Kingdom Frederic P. Lebel, CFA HFS Hedge Fund Selection S.A. Founex, Switzerland Colin W. McLean, FSIP SVM Asset Management Ltd. Edinburgh, United Kingdom www.cfapubs.org 27