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The Impacts of Automation and High Frequency Trading on Market Quality1

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The Impacts of Automation and High Frequency Trading on Market Quality1 The Impacts of Automation and High Frequency Trading on Market Quality1 Robert Litzenberger2, Jeff Castura and Richard Gorelick3 In recent decades, U.S. equity markets have changed from predominantly manual markets with limited competition to highly automated and competitive markets. These changes occurred earlier for NASDAQ stocks (primarily between 1994 and 2004) and later for NYSE-listed stocks (mostly following Reg NMS and the 2006 introduction of the NYSE hybrid market). This paper surveys the evidence of how these changes impacted market quality and shows that overall market quality has improved significantly, including bid-ask spreads, liquidity, and transitory price impacts (measured by short-term variance ratios). The greater improvement in market quality for NYSE-listed stocks relative to NASDAQ stocks beginning in 2006 suggests causal links between the staggered market structure changes and market quality. Studies using proprietary exchange provided data sets that distinguish activity by high frequency trading firms show they contributed directly to: narrowing bid-ask spreads, increasing liquidity, and reducing intra-day transitory pricing errors and intra-day volatility. Corresponding Author: Jeff Castura [email protected] 1 The views presented in this paper are the authors’ only and do not necessarily represent the views or opinions of any other person or entity. The authors would like to thank Richard Lindsey for his historical perspectives on market structure and Amy Litzenberger, Cameron Smith, Bill O’Brien and Chris Smith for their helpful comments. 2 Robert Litzenberger is affiliated with the University of Pennsylvania and RGM Advisors, LLC 3 Jeff Castura and Richard Gorelick are affiliated with RGM Advisors, LLC 1 Table of Contents Introduction Pre-Electronic Trading and Institutional Investors Leveling the Playing Field Bid-Ask Spreads Posted Liquidity Trade Sizes Market Efficiency Profitability of HFT and Transaction Taxes Volatility Technology and Latency Messaging Rates Concluding Remarks References 2 Introduction Trading in financial instruments has seen dramatic changes over recent decades. Advances in computing power and improvements in telecommunications networks have facilitated the development of high- speed, interconnected electronic trading platforms and have enabled a dramatic increase in automated trading. As the ability to easily connect to electronic trading platforms has grown, traders have adapted their methods to seek new opportunities to profit. One such method is high frequency trading (HFT) which has become widespread and has received significant attention in the media and from policy makers. There has not, to date, been a consistent academic or regulatory definition of high frequency trading. People use the term in different ways and for different purposes, which has made discussions about modern, electronic trading and its impact on markets more difficult. However, the term “HFT” is often used to refer to types of automated trading that “trade frequently.” HFT is also commonly characterized as having a sensitivity to speed (i.e., latency) and transaction costs; however, many types of trading fall within this broad characterization, including without limitation, modern electronic market making, short-term directional trading, and various forms of arbitrage and statistical arbitrage. For most of these types of trading, low- 3 latency, high-throughput trading technology is important. While these types of trading are often conducted on a proprietary basis, much of the algorithmic trading conducted on an agency basis uses the same technologies and tools as those conducted on a principal basis and for some purposes is referred to as HFT. Additionally, proprietary HFT is often, but not always, characterized as trading with relatively high rates of position turnover and small risk positions held outside of regular trading hours (relative to total trading volumes). The growth of new electronic trading platforms and the increasing use of automation and advanced computing technology have raised questions about the effect these changes have had on the state, functioning and integrity of markets. Initiatives such as the U.S. SEC’s concept release on equity market structure in 2010 (U.S. SEC 2010b), the UK government’s ongoing Foresight Project on the Future of Computer Trading in Financial Markets (BIS 2011) and the European Commission’s MiFID II review in Europe (European Commission 2011), are all attempts to gain a better understanding of the true impact of these changes. To provide a benchmark for evaluating the changes of recent decades, the structure of pre-electronic markets is briefly discussed in the initial section of this paper. The regulatory and competitive changes that leveled the playing 4 field are discussed next. Then the characteristics of HFT are discussed and contrasted with the pre-electronic precursors, and testable implications for market quality are developed. Studies that provide a theoretical foundation for understanding the impacts of HFT and their empirical implications are discussed next. The remainder of the paper surveys the empirical literature. Sections on bid-ask spreads, posted liquidity, trade size, market efficiency, profitability and volatility are presented. The separate impacts on market quality of latency and messaging rates are then discussed, followed by some concluding remarks. At its essence, the purpose of a marketplace is to facilitate buying and selling, and the “quality” of a market is often expressed as the degree to which this is accomplished without imposing additional costs or frictions on market participants. There are many ways to measure the quality of markets. This paper surveys recent empirical studies relevant to the impact of HFT and other modern trading practices on market quality. The U.S. equity markets are large, liquid markets that led the rise of automated trading and as such, much of our discussion focuses on these markets. Each study takes a unique approach, often using different definitions of, or proxies for, HFT, which together paint a consistent picture of markets being improved by competition and automation. 5 Pre-Electronic Trading and Institutional Investors In order to understand the impact of the automation of markets and the growth of HFT, it is helpful to consider earlier market structures. In the 1960s the NYSE’s dominant market share and its system of exclusive specialists, floor brokers, floor traders, fixed commissions, and minimum tick sizes of 1/8th of a dollar, were put under pressure by the growth in the size and turnover rates of institutional shareholdings. One critique of modern HFT is that it somehow disadvantages institutional investors by exacerbating the market impact of their large orders. It is important to recognize that similar concerns have been expressed for a long time, going back well before automated trading. The SEC study of institutional investors during the late 1960s (the “Institutional Study”) found that sales of large blocks of stock often resulted in a large price impact (Kraus & Stoll 1972). In an attempt to mitigate the price impact, floor brokers often worked larger orders throughout the day, but their physical presence on the floor often revealed to floor traders and specialists that they were engaged in a large buy or sell program. During this era, there were concerns that being close to the center of price discovery benefited certain market participants at the expense of institutional investors. An SEC special study of floor trading on the NYSE 6 (the “Special Study”) during the early 1960s observed that: “familiarity with the trading techniques of specialists or floor brokers...combined with knowledge that a large block of stock is being accumulated or distributed... facilitates the trading activities of the floor trader” (Smidt 1985, p. 78). The Special Study further noted that NYSE members having access to the floor were able to observe trading activity minutes before it was printed on the tape and bids or offers in a matter of seconds (Ibid, pp. 77-78). It found that “floor traders are generally buyers in rising markets and sellers in declining markets, with respect to both the market as a whole and to individual stocks” (Ibid, p. 77). While the Special Study viewed this behavior as destabilizing and proprietary floor trading was subsequently prohibited, Smidt noted that: The special study's use of the term ‘destabilizing’ to describe the price effects of stock exchange floor trading implies that in the absence of this trading the price might not have moved and that with the trading a temporary movement occurs that is subsequently reversed. Nothing in the systematic data in the various SEC studies supports either of these implications of the word ‘destabilizing.’ The evidence is also consistent with the hypothesis that floor trading accelerates price movements that would otherwise have taken place more slowly (Ibid, p. 78). 7 These differing interpretations sound familiar to modern ears, as the HFT discussion has brought up similar questions about whether faster price discovery is better and how such price discovery impacts investors. As an alternative to gradually implementing a large buy or sell program on the exchange floor, large orders by institutions were often arranged through block positioners at a price negotiated off the floor of the exchange and then crossed on the exchange. The negotiated price reflected a discount (on a sell order) or a premium (on a buy order) and the institution paid a double commission for the services of the block positioner. Kraus & Stoll (1972) dichotomized the price impact of a block trade into a permanent component, which they denoted an “information effect”, and a transitory component, which they denoted a “distribution effect.” The price at which these sell blocks were traded was, on average, 1.14% less than the specialist’s bid immediately prior to the trade. The stock prices then recovered 0.71% by the end of the trading day (Ibid, Fig. 1). They interpreted the recovery as a “distribution effect” that may be viewed as a transitory pricing error that reverted to zero by the end of the trading day.
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