High-Frequency Trading: An Innovative Solution to Address Key Issues

The following post comes to us from Jon Lukomnik of the IRRC Institute and is based on the summary of a report commissioned by the IRRC Institute and authored by Professors Khaldoun Khashanah, Ionut Florescu, and Steve Yang of Stevens Institute of Technology; the full report is available here.

As controversial as is HFT, the large volume of the discussion sometimes makes it hard to understand the content. What elements of HFT positively impact the trading markets? Which are problematic? What are the proposed mitigations? Therefore, the Investor Responsibility Research Center Institute (IRRC Institute) asked Khashanah, Florescu, and Yang (KF&Y) to look at HFT from various perspectives. The result includes:

  • The effect of HFT on volume, price efficiency and liquidity.
  • The problems and risks seen by various stakeholders from their vantage points.

While KF&Y dismiss many alleged problems as the inevitable result of disruptive technology, they do credit the criticism that HFT may create two unfair practices due to asymmetric information: one is that HFT effectively uses advance access to “micro front run” other investors, and the other is a claim of micro-price manipulation.

Finally, KF&Y make their own innovative proposal to mitigate the problems created by HFT while maintaining the benefits it creates. Building on existing financial concepts and regulation as to who is a market maker and who is a trader, on the need for fairness in the dissemination of price quotes and trades, and on the mechanics of HFT, they introduce a concept of “information transmission zoning”. Among the advantages of their proposal—which is designed to create a fair playing field insofar as the dissemination of price information without decreasing liquidity—is that requires minimum financial information flow re-architecting and builds on the Security and Exchange Commission’s Regulation National Market System (NMS). It does not require any major change in regulation or regulatory authority.

Introduction and Literature Review

High Frequency Trading (HFT) is a specific type of algorithmic trading. Before understanding HFT, then, it would be beneficial to define algorithmic trading. These strategies attempt to discover underlying temporary but recurring pricing phenomena that can generate trading opportunities. By and large, these strategies focus on stock price relationships, such as those between pairs or groups of stocks, or of an instantaneous stock price to that security’s historic price, or price/volatility relationships. In general, they do not involve fundamental analysis of the company.

Algorithmic trading strategies may include microsecond price movements that allow a trader to benefit from market-making trades, several minute-long strategies that trade on momentum forecasted by market microstructure theories, and several hour-long market movements that surround recurring events and deviations from statistical relationship (Aldridge (2010)).

HFT strategies have attracted much attention from investors, regulators, policy makers, academics, the press and the public broadly. In part, this is because of the disruptive nature of the technology used, and in part because of the widespread perception that high frequency traders are “gaming” the system, as express most notably in the bestselling book, Flash Boys.

According to the U.S. Securities and Exchange Commission, high-frequency traders are “professional traders acting in a proprietary capacity that engage in strategies that generate a large number trades on daily basis.” The SEC characterizes HFT itself to include:

  • 1. The use of high-speed and sophisticated computer programs for generating, routing, and executing orders;
  • 2. Use of co-location services and individual data feeds offered by exchanges and others to minimize network and other types of latencies. Co-location means placing a proprietary trading computer adjacent to the exchanges’ order-taking system. Latency, generally, is a measure of the time lag between a stimulation and a response; in terms of HFT it is generally used to mean how long it takes an HFT system to place a trade order once it senses a trading opportunity;
  • 3. Very short timeframes for establishing and liquidating positions;
  • 4. The submission of numerous orders that are canceled shortly after submission; and
  • 5. Ending the trading day in as close to a zero position as possible (that is, not carrying significant positions over night).

Recent academic and practitioner research (Jones, 2012) has identified a number of HFT strategies, including:

  • 1. Acting as an informal or formal market-maker;
  • 2. High-frequency relative-value trading; and
  • 3. Directional trading on news releases, order flow, or other high-frequency signals.

In the past few years, there have been a number of studies of HFT and algorithmic trading more generally. The KF&Y white paper surveyed 56 academic research papers. The literature primarily covers five primary topics: economic impact, theoretical modeling, price discovery impact, limit order book dynamic modeling, and behavior studies of algorithmic and HFT trading practices. The first three topics directly deal with the question of whether HFT provides positive or negative value to the market’s overall quality.

Financial Economic Research

The primary objective of researchers examining the economic impact of HFT is to understand the impact of these algorithmic trading practices on market quality including liquidity, price discovery process, trading costs, etc. Given the amount of information provided by exchanges and data vendors, it is possible to describe patterns in order submission, order cancellation, and trading behavior. It is also possible to see whether algorithmic or HFT activities are correlated with bid-ask spreads, temporary and/or permanent volatility, trading volume, and other market activity and quality measures. Hendershott et al. (2011) conclude that the implementation of an automated quotation system at the New York Stock Exchange is associated with an increase in electronic message traffic and an improvement in market quality including narrowed effective spreads, reduced adverse selection, where undesired results occur when buyers and sellers have asymmetric information (access to different information) and increased price discovery. However, they note that these effects are concentrated in large-cap stocks, and there is little effect in small-cap stocks. Menkveld (2012) studied the July 2007 entry of a high-frequency market-maker into the trading of Dutch stocks. He argues that competition between trading venues facilitated the arrival of this high-frequency market-maker and HFT more generally, and he shows that high-frequency market-maker entry is associated with 23% less adverse selection. Volatility is unaffected by the entry of the high-frequency market-maker. Riordan et al. (2012) examine the effect of a technological upgrade on the market quality of 98 actively traded German stocks. They conclude that the ability to update quotes faster helps liquidity providers minimize their losses, and more price discovery takes place. Boehmer et al. (2012) examine international evidence on electronic message traffic and market quality across 39 stock exchanges over the 2001-2009 period. They conclude that co-location increases algorithmic trading and HFT, and that the introduction of colocation improves liquidity and the information efficiency of prices. However, they claim volatility does not decline as much others may claim. Gai et al. (2012) study the effect of two recent 2010 NASDAQ technology upgrades and conclude that reduced time between electronic messages leads to substantial increase in the number of canceled orders without much change in overall trading volume. There is also little change in bid-ask spreads. Overall, these studies suggest that an increase in algorithmic trading positively influences market quality in general, and is particularly positive to liquidity providers, such as market makers.

Financial Theoretical Modeling Research

The second topic focuses on the theoretical modeling of the algorithmic and HFT trading practices to understand their economic impact. Biais et al. (2012) conclude HFT can trade on new information more quickly, generating adverse selection costs for non-HFT investors. In addition, they suggest that HFT requires significant fixed investments in technology, and that only sufficiently large institutions are likely to make these fixed investments, leaving smaller firms and investors to bear the adverse selection costs from HFT. Iovanovic et al. (2010) show that HFT can update limit orders quickly based on new information. As a result, HFT can avoid some adverse selection, and HFT can provide some of that benefit to uninformed investors who need to trade. They note that some of these trades might not have occurred otherwise, in which case HFT can improve welfare. Martinez et al. (2012) conclude from their model that HFT obtains and trades on information an instant before it is available to others, and it imposes adverse selection on market-makers. Therefore liquidity is worse and prices are no longer efficient. They focus on HFTs that demand liquidity. Foucault, Hombert, and Rosu (2012) show that HFT obtains and trades on information an instant before it is available to others. This imposes adverse selection on market-makers, so liquidity is worse, and prices are no more efficient. The common theme in these models is that HFT may increase adverse selection for non-HFT investors, and it is harmful for liquidity.

Order Book Dynamics Modeling Studies

The third topic area is concerned with modeling limit order book dynamics. Albert J. Menkveld (2007) observes that it has become common for firms to cross-list shares on different markets, which has proved to benefit firms by reducing the cost of capital and enhancing the liquidity of the stock. He concludes that it is the arrival of large liquidity trader volume and the lower profits of informed traders that make the market more liquid. John Y. Campbell et al. (2005) look at high-frequency trading information and quarterly information on institutional equity holdings to draw conclusions about institutional equity ownership. David Easley et al. (2012) present a new method of estimating flow toxicity based on volume imbalance and trade intensity (VPIN). They assert that order flow is toxic when it adversely selects market makers, who may be providing liquidity at a loss unknowingly. They suggest that high levels of VPIN signify a high risk of subsequent large price movements, deriving from the effects of toxicity on liquidity provision. Boyan Jovanovic and Albert J. Menkveld (2012) study how high frequency trading might reduce informational friction. Their model also implies that regulations or fee structures that induce HFTs to shift from producing price quotes to consuming them could result in substantial welfare losses. Joel Hasbrouck (2012) studies price variance and shows that the highest quoted volatilities occurred during the 2004-2006 time period, which corresponds to the transition to electronic trading in the markets. Joel Hasbrouck and Gideon Saar (2013) propose a new measure of low-latency activity in order to discover the impact of high frequency trading. They conclude that increased low-latency activity improves market quality in the area of liquidity and short-term volatility. Overall, though these papers do not provide direct interpretation of influences of HFT, they offer insight for researchers into the mechanics of these automated trading practices.

Trading Strategies Studies

The forth topic addresses the impact of HFT on the price discovery process; price discovery commonly considered a way to measure market efficiency. Frank Zhang (2010) documents that HFT has become a dominant driver of trading volume in the U.S. capital market, that HFT strategies are agnostic to a stock’s price level and have no intrinsic interest in the fate of the underlying companies, and so there is little room for a firm’s fundamentals to play a role in HFT trading strategies. He finds that HFT is positively correlated with stock price volatility. He also finds that HFT is negatively related to the market’s ability to incorporate information about firm fundamentals into asset prices, and stock prices tend to overreact to fundamental news when HFT trading is high. Ryan Riodan and Andreas Storkenmaier (2011) document that decreasing the latency in a market leads to increased liquidity, mostly in small and medium sized stocks. Terrance Hendershott and Ryan Riordan (2011) conclude that HFT plays a positive role in price efficiency by trading in the direction of permanent price changes and in opposite direction of transitory pricing errors on average days and the highest volatility days. David Easley et al. (2013) examine the impact of a major upgrade that happened to the New York Stock Exchange in 1980 to improve its technical environment. This increase in transparency and reduction in transaction latency allowed off-floor traders to condition their orders on more up-to-date information and reduced the free option that limit orders had provided. They also conclude that the upgrades also generated relatively greater turnover and relatively lower transaction costs. The results of their study indicate that leveling the playing field between the public and intermediaries leads to higher liquidity and better prices. Bozdog et.al. (2011), discovered that mini market crashes are a much more frequent occurrence than previously known. They found that mini-crashes are related to pressure in the market and a lack of liquidity existing in the market at the time of those events.

HF Traders Behavioral Studies

A number of studies focus on algorithmic traders’ behaviors. Hendershott et al. (2012) find that algorithmic traders concentrate in smaller trade sizes, while large block trades of 5,000 shares or more are predominantly originated by human traders. Algorithmic traders consume liquidity when bid-ask spreads are relatively narrow, and they supply liquidity when bid-ask spreads are relatively wide. This suggests that algorithmic traders help markets maintain a more consistent level of liquidity. Hendershott et al. (2011) find that HFT contributes to price discovery and efficient stock prices. Brogaard (2012) finds that 68% of trades have an HFT on at least one side of the transaction, and he also finds that HFT participation rates are higher for stocks with high share prices, large market caps, narrow bid-ask spreads, and low stock-specific volatility. He finds that HFT liquidity suppliers face less adverse selection than non-HFT liquidity suppliers, suggesting that they are somewhat judicious in supplying liquidity.

The full KF&Y paper provides a comprehensive overview of the current academic research in HFT. Overall, although there are still differences in opinion with regard to HFT, they conclude that HFT provides liquidity and on average improves market quality, with more discernible positive effects in large-cap stocks. However, they note that under distressed market conditions such as the 2010 Flash Crash, HFTs reportedly played a very different role, and contributed to the disorderly price decline (Kirilenko, Kyle, Samadi, and Tuzun (2011)). They state that, due to the limited empirical data that academic researchers can access, answers to questions regarding HFTs’ economic merit and regulation surrounding HFT behaviors are far from being definitive.

The full report is available here.

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