High Frequency Trading Volumes Continue to Increase Throughout the World

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1 High Frequency Trading Volumes Continue to Increase Throughout the World High Frequency Trading (HFT) can be defined as any automated trading strategy where investment decisions are driven by quantitative models at speeds faster than those possible using human decision making. Although there is no perfect definition that easily encompasses this concept, the previous description broadly outlines this controversial topic. For this paper we will breakout High Frequency Trading into three categories: 1. Market Making 2. Statistical Arbitrage 3. Predatory Trading Market Making is one of the most accepted forms of High Frequency Trading, this strategy involves buying and selling between the spreads and is generally thought of as adding liquidity to the marketplace. Automated market making is not a new concept as NASDAQ basically pioneered this process in the early 1970s. Today NASDAQ is generally credited with reducing spreads and being the first fully electronic stock exchange. However, in the early years NASDAQ was a highly controversial marketplace, especially from the perspective of traditional established OTC dealers. Automated market making clearly was the first step towards reducing spread costs and eventually leading to more transparent OTC trading costs. Similar to most new concepts on Wall Street there were several competing factions battling for and against NASDAQ development. Ultimately smart technology prevailed as is evident from the present day trading environment where the vast majority of equity market making is automated and performed electronically. High Frequency Market Making will provide the next wave of growth to the marketplace by increasing competitiveness and scalability of financial markets. Many critics of high frequency trading argue that these strategies create a two tiered marketplace where firms with superior technology have an unfair advantage. Historically, any market place creates an opportunity for certain groups to capitalize on the structure and activity occurring within a market. Ironically, the NASDAQ marketplace is already a three tiered market where NASDAQ dealers with level 2 and 3 access have superior insight into order flows. Statistical Arbitrage is a very broad area of high frequency trading and encompasses everything from price discrepancy arbitrage to event arbitrage. These strategies attempt to use ultra-low latency trading to buy and sell between securities and across asset classes to profit from price adjustments. Statistical arbitrage is one of many influences which have propelled trading volumes to record levels. High frequency trading firms use advanced real time data feeds and superior proprietary databases to analyze vast amounts of market data and profit on opportunities which may only be spotted in milliseconds. ETFs provide an easy target for high frequency trading firms whom can easily take advantage of price movements which are not consistent with the underlying securities

2 Pricing differences between asset classes including, derivatives, currencies, bonds and equities can provide an additional opportunity, as well as processing qualitative data including, merger announcements, earnings announcements and corporate actions for profit opportunities. Predatory Trading is considered the most taboo form of High Frequency Trading and is the area which is causing the greatest unrest among long term investors and regulatory bodies. First popularized by traditional day trading strategies, predatory traders attempt to uncover patterns within market data which provide insight into large institutional orders flows. Predatory trading attempts to profit by essentially front running large orders. With such a large amount volume now automated by mega money managers, these automated institutional orders flows are very difficult to fully conceal. Many of these automated orders have goals of trading in-line with market volumes in order to match the Volume Weighted Average Price (VWAP) or minimize slippage against an Implementation Shortfall benchmark. Automated VWAP orders attempt to trade in-line within volumes throughout the day in order to essentially become the VWAP price. These orders can be spotted by High Frequency Traders whom have the ability to post orders for free being on the maker side of a maker-taker model and have the ability to spot supply and demand imbalances in real-time and profit from the incoming order flow. Many critics of High Frequency Trading blame some of the immense volatility since the initiation of Reg NMS on these predatory trading strategies. Reg NMS opened up a series of changes within the equity markets that enables many High Frequency Strategies to become profitable and paved the way for lucrative maker-taker venue pricing. Electronic venues can now profit from their market data and actively court high frequency orders with liquidity rebates in order to increase this revenue stream. Reg NMS further requires trades to be routed to the venues with the most optimal price. High Frequency Traders use their speed advantage to stay in front of the National Best Bid and Offer and potentially gain intelligence on large orders. One of the most popular methods for gaining this type of intelligence is called Pinging. Pinging is the process of putting out a series of small orders in order to spot a large incoming order. Pinging has come under intense scrutiny of late as the number of canceled equity orders has sky rocketed over that last few years. Pinging is most recognized in certain dark pools where a trader will place an IOI (Indication of Interest) or Immediate or Cancel (IOC) order for intelligence gaining purposes. Although many investors only associate Pinging with Dark Pools, certain High Frequency Traders use these types of strategies on lit markets as well

3 Although predatory High Frequency Trading is clearly an unethical market activity, intense critics should tread lightly on regulation since the overall result of Reg NMS and High Frequency Trading has been a reduction in trading costs, increased liquidity and technological advances in trading technology that continues to move financial markets forward. From 2Q2007 to 2Q2011 overall US Institutional Equity Trading Costs have declined over 11%. Trading Cost Data Provides No Significant Insight Into High Frequency Trading Despite much concern, non-bias trading data does not reveal any solid evidence that High Frequency Trading disadvantages long term investors. Round trip equity trading costs have never been lower. Commission averages continue to fall year over year driven by technology and intense scrutiny from buy-side money managers. The overall results of these market changes has been a significant increase in market volume and reduced trading costs for investors

4 Equity Trading Volumes Soar to All Time Record Levels Although equity commission averages have declined substantially this decade, banks and brokers have benefited from large increases in trading volume. To put this increase into perspective, the total NYSE trading volume in 1960 was 767 million shares for the entire year. On 08/25/2011 one single stock, Bank of America (BAC), traded 860 million shares in a single trading day. It s not just Bank of America (BAC); many individual stocks and ETFs routinely trade over 100 million shares per day may turn out to again be another record breaking volume year despite a relatively slow first seven months. January 1 st to July 31 st were low volume months, as compared to the previous few years. This volume slow down appeared to be due to market volumes reverting back to standard historical growth. However, August 2011 has proved unexpectedly to be one of the busiest months on record, as August trading volumes soared during a traditionally slow summer market period. Sparked by market uncertainty, but apparently compounded by vast amounts of high frequency trading, market volumes are showing no signs of declining long term. Despite these spikes in August share volumes, average shares per trade did not increase in August. Average shares per trade never increased above 243 shares in August and was pretty consistent within a range between 243 on 08/02/2011 and 220 on 08/10/2011. The highest volume days on 08/08/2011 and 08/09/2011 averaged 231 and 228 shares per trade respectively. Despite these low trade sizes, overall market volumes are clearly on an incredible long term rise. US Equity markets can now trade more shares in one single day, than traded during an entire year during the 1960s, 1970s and early 1980s. Technology and automating processes have clearly revolutionized the equity business

5 Large Trader Rule The Large Trader Rule which will become effective in the US on 10/3/2011, will require firms or individuals trading over 2 million shares or $20 million in USD on any given day, or 20 million shares or $200 million in principal on any given month to be assigned a unique trading ID. This ID will be used by the SEC to monitor the trading activities of these Large Traders. The SEC is clearly concerned with monitoring the activities of High Frequency Trading firms and better understanding their strategies. The additional compliance burden on investors and brokers will be nothing compared to the vast amounts of trade data which will need to be mined by regulators. Since these volume requirements are relatively low, we estimate a few thousand firms will need to register and be issued a unique ID. Money managers, hedge funds, internally managed asset owners, high frequency or prop firms, corporations and governments could all potentially need to be identified. Critics of the Large Trader Rule bring up the obvious issue of confidentiality and information leakage; however, the overall results of this project should reinsure small investor s belief in the equity markets. Many retail investors are skeptical of high frequency trading and fear their trades are being picked off by devious Wall Street trading operations. Although, pin pointing a predatory trade within these vast amounts of Large Trader data will be challenging; the mere implementation of this rule should discourage market abuse and help long term investor confidence