Through a review of the literature on automated high-frequency trading (HFT), the author tries to determine whether HFT affects liquidity, price efficiency, and price discovery. This subject is relevant to regulators, risk managers, and traders. The Flash Crash on 6 May 2010 and recent high-profile software-induced losses bring increasing public attention to automated trading.

The author provides an excellent source of citations for those interested in automated trading. The endnotes and references provide more than 120 citations, sourced from newspapers, periodicals, professional journals, speeches, and regulatory policy statements.

In addition to the discussion on HFT, the author offers an interesting digression on the black swan theory. He points out that the tail risk profile of a black swan event is dependent on the time frequency of the measurements. Black swans are “wild” only at certain time scales, and by changing the frequency of the measurements, the lightness or heaviness of the distribution’s tails will change. The author suggests that probability distributions should model the appropriate time scales of the new technologies.

He further proposes that at such high frequencies, events may become serially dependent. “Dragon kings” is a term coined by Sornette (Cornell University 2009) to explain surprise events that are driven by internal positive feedback, which occur more frequently than expected.

The author points out that the industry may need to update its risk-measuring tools as risk-taking technology is updated.

The author conducts a literature review and summarizes the findings of several other authors. He defines HFT as a strategy for profit maximization, as opposed to algorithmic trading, which seeks to minimize transaction costs. HFT characteristics include high-speed execution, high volumes of subsecond orders and cancellations, co-location of servers to exchanges, and avoidance of overnight risk.

Some literature reports that HFT traders are preying on slower traders. Co-locating their servers near exchanges decreases the latency of the high-frequency trader’s electronic signals. Front runners watch for large institutions that are using volume-weighted average price algorithms to execute large orders and then step in front of large trades.

Other predatory methods of speed trading include stuffing, smoking, and spoofing. These techniques are used to manipulate the order book and can cause order congestion, increased short-term volatility, and decreased liquidity. Rapid order submissions and cancellations can also obscure price discovery.

Studies indicate that although HFT did not trigger the Flash Crash of 6 May 2010, it exacerbated volatility because of the creation of volume in the direction of the price changes. Homogenous speed-trading strategies could lead to more systematic risk in the form of correlation. High-speed cross-market arbitrage strategies might spread that risk among markets.

HFT firms claim that speed gives them better risk protection and faster absorption of news. The author notes that low-latency automated trading has been associated with narrower spreads and increased market depth. Most of the evidence suggests that HFT is beneficial to price efficiency. Some studies indicate that HFT decreases volatility. But the author points out that many studies are based on data taken during normal times.

Critics of HFT are pushing for more regulation, and pressure is building at regulatory agencies to define and regulate HFT. The author’s strongest point is that current research is insufficient to inform the debate surrounding HFT. Industry participants, academics, and regulators need to carefully define and understand new technologies’ effects on the markets before they regulate so that impending regulations will improve, and not impair, the financial markets.