Is High Frequency Trading Beneficial To Market Quality?
University of Sydney – School of Business – Finance Discipline; Financial Research Network (FIRN)
University of Sydney Business School
University of Sydney Business School
October 13, 2015
This report discusses how high frequency trading (HFT) has changed the dynamics of the market and whether traditional academic measures of market “quality” are relevant in the new world of electronic trading. Using existing measures of market quality, which were designed over 20 years ago, much of the academic literature suggests HFT is beneficial for market quality. However, a closer examination of HFT reveals that the results may not be so beneficial and that many of these metrics are no longer applicable. This paper presents new metrics for market “quality”, which suggests that with the growth in HFT the probability of institutions getting orders filled has fallen and the time required to achieve a fill has increased. Additionally HFT trades tend to supply liquidity on the thick side of the order book, where it is not needed.
Is High Frequency Trading Beneficial To Market Quality? – Introduction
What Is Market Quality?
When posing the question “Is high frequency trading beneficial to market quality”, to expect a binary yes or no answer is a little naive. There are likely to be positive and negative aspects to HFT and whether on balance it is judged beneficial will depend upon the criteria used. How one defines market “quality” or if a trader’s actions are “beneficial” are open to interpretation. Accordingly, the notion of market quality and how it is to be measured must first be addressed.
Harris (2003) argues that the highest priority of financial markets is to promote the interests of utilitarian traders, or more specifically, those whose needs cause the markets to exist in the first place. A large portion of these utilitarian traders are institutional investors. Their interests should take high priority, because if they did not use the market, then the market might not exist, and then nobody would gain any benefit from the market. At the lower end of priority is the interest of traders seeking profits from trading rather than investment. In fact, Harris (2003) argues that their interests should only be supported when necessary to achieve other objectives. For example, a market maker, who adds to liquidity and contributes to price informativeness, should be supported. There is also one class of trader that Harris suggests markets should be hostile towards. These are the profit motivated traders who design trading strategies with the key purpose of exploiting other traders. Using the definition of HFT, provided in the next paragraph, it becomes apparent that high frequency trader’s fall into two possible categories; they are either profit motivated traders who support other market objectives, or they are profit motivated traders who design trading strategies to exploit other traders. Thus, the market should give HFT interests low priority, or potentially, be hostile towards them.
Defining high frequency trading
It is apparent that a definition of high frequency trading is required. Unfortunately, to date, there is no agreed upon definition for HFT in either the regulatory or academic environment. Definitions range from the very general to the very detailed (see Gomber et al. (2011) for an exhaustive list of different definitions used through time).
In our opinion, it is important to keep the definition as broad as possible, and high frequency trading is thus defined as “a fully automated proprietary trading strategy which executes multiple intraday trades for profit”. The reason for this broad definition is that the market is constantly evolving and strategies are constantly changing. Categorising HFT by a very specific and distinct set of features is likely to prove an elusive goal. Consistent with this view is the current lack of consensus on a generally accepted definition of HFT. Markets are constantly evolving and HFT is simply the evolution of new trading strategies. A strategy that works today, may not work tomorrow, with a similar notion applying to the technology used. Furthermore, the idea that all HFTs behave in a similar manner is wrong. If all high frequency traders behaved in the same manner, then the inefficiency they were all exploiting would likely quickly disappear, and shortly after so would the HFT firms themselves. HFTs do not all behave the same way and exploit the same inefficiencies. Brogaard (2010), for example, shows that HFTs demand (supply) liquidity in 42.7% (41.1%) of all dollar volume traded. This suggests that HFTs both take and supply liquidity in approximately equal quantities. Similarly, there are just as many contrarian HFTs as there are momentum HFTs. HFT is not one specific type of trading strategy, much of it is simply an evolution of traditional trading strategies into an automated process on a shorter time frame.
It is important to be clear about the definition of HFT as it is a loosely used term. As Vuorenmaa (2013) highlights, HFT and algorithmic trading (AT) are not the same concepts. Nonetheless, HFT is often defined as a subset of algorithmic trading (see Gomber et al. (2011) for a review of the different definitions of HFT and AT). Among the trading community however, HFT is not considered to be a subset of AT. As Vuorenmaa points out, HFT and AT are subsets of automated trading, but HFT is not a subset of AT. High frequency trading strategies are designed to determine when a profitable trade should be made, whereas AT is about determining how to execute a large order so as minimise market impact. It is important to understand that these strategies and their objectives are different. In a following section, where a large body of literature which supports the benefits of AT is presented, it is often concluded that if high frequency trading is a subset of AT and AT is beneficial to market quality then HFT must also contribute to market quality. This is not the necessarily the case when it is realised that HFT is not a subset of AT. While they may use the same technology, their objectives, and hence the strategies behind them are very different.
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