High frequency trading - page 8

The use of computers to execute trades, often with very low latency, has increased over time, resulting in a variety of computer algorithms executing electronically targeted trading strategies at high speed. We describe the evolution of increasingly fast automated trading over the past decade and some key features of its associated practices, strategies, and apparent profitability. We also survey and contrast several studies on the impacts of such high-speed trading on the performance of securities markets. Finally, we examine some of the regulatory questions surrounding the need, if any, for safeguards over the fairness and risks of high-speed, computerized trading.

This paper reviews recent theoretical and empirical research on high-frequency trading (HFT). Economic theory identifies several ways that HFT could affect liquidity. The main positive is that HFT can intermediate trades at lower cost. However, HFT speed could disadvantage other investors, and the resulting adverse selection could reduce market quality.

Over the past decade, HFT has increased sharply, and liquidity has steadily improved. But correlation is not necessarily causation. Empirically, the challenge is to measure the incremental effect of HFT beyond other changes in equity markets. The best papers for this purpose isolate market structure changes that facilitate HFT. Virtually every time a market structure change results in more HFT, liquidity and market quality have improved because liquidity suppliers are better able to adjust their quotes in response to new information.

Does HFT make markets more fragile? In the May 6, 2010 Flash Crash, for example, HFT initially stabilized prices but were eventually overwhelmed, and in liquidating their positions, HFT exacerbated the downturn. This appears to be a generic feature of equity markets: similar events have occurred in manual markets, even with affirmative market-maker obligations. Well-crafted individual stock price limits and trading halts have been introduced since. Similarly, kill switches are a sensible response to the Knight trading episode.

Many of the regulatory issues associated with HFT are the same issues that arose in more manual markets. Now regulators in the US are appropriately relying on competition to minimize abuses. Other regulation is appropriate if there are market failures. For instance, consolidated order-level audit trails are key to robust enforcement. If excessive messages impose negative externalities on others, fees are appropriate. But a message tax may act like a transaction tax, reducing share prices, increasing volatility, and worsening liquidity. Minimum order exposure times would also severely discourage liquidity provision.

High-frequency trading (HFT) has recently drawn massive public attention fuelled by the U.S. May 6, 2010 flash crash and the tremendous increases in trading volumes of HFT strategies. Indisputably, HFT is an important factor in markets that are driven by sophisticated technology on all layers of the trading value chain. However, discussions on this topic often lack sufficient and precise information. A remarkable gap between the results of academic research on HFT and its perceived impact on markets in the public, media and regulatory discussions can be observed. The research at hand aims to provide up-to-date background information on HFT. This includes definitions, drivers, strategies, academic research and current regulatory discussions. It analyzes HFT and thus contributes to the ongoing discussions by evaluating certain proposed regulatory measures, trying to offer new perspectives and deliver solution proposals.

Earlier this year, the high-frequency trading firm Virtu Financial LLC published details about the firms long-term operations in an SEC filing. One remarkable fact stood out like a sore thumb in the company’s filing: The firm had suffered just “one losing trading day” in over 1,238 days of trading Virtu Financial made profits with their high-frequency trading scheme on 1237 out of the last 1238 days over the last five plus years. Greg Laughlin, professor of astrophysics at the University of California at Santa Cruz, points out, even if you only win 51% of your trades, if you make enough trades you are statistically certain to make profits. Laughlin determined that Virtu was making around 800,000 trades day. He concludes by highlighting that with 800,000 trades a day, it is literally statistically impossible for Virtu to have a losing day

nevar:
Very interesting read: http://www.valuewalk.com/2014/11/high-frequency-traders-virtu/

The rapid growth of high frequency trading (HFT) has aroused considerable public attention and policy interests in its impact on institutional investors. Previous studies show that HFT decreases the average bid-ask spread. However, the major component of institutional trading costs is the price impact, as measured by the execution shortfall. Combining data on institutional trades and HFT trades, I find that HFT increases traditional institutional investors' trading costs. Specifically, one standard deviation increase in the intensity of HFT activities increases institutional execution shortfall costs by a third. I also perform various tests to rule out an alternative explanation that high frequency traders are attracted to stocks that have high trading costs. Further analysis suggests that HFT represents a short-lived and expensive source of liquidity provision when demand and supply among institutional investors are imbalanced, and that the impact on institutional trading costs is most pronounced when high frequency traders engage in directional strategies. Additionally, I find that institutional trading skills can alleviate the adverse impact of HFT

The use of computers to execute trades, often with very low latency, has increased over time, resulting in a variety of computer algorithms executing electronically targeted trading strategies at high speed. We describe the evolution of increasingly fast automated trading over the past decade and some key features of its associated practices, strategies, and apparent profitability. We also survey and contrast several studies on the impacts of such high-speed trading on the performance of securities markets. Finally, we examine some of the regulatory questions surrounding the need, if any, for safeguards over the fairness and risks of high-speed, computerized trading.

We examine the impact of changes in market microstructure, particularly algorithmic trading (AT) and high frequency trading (HFT), on trade size across 24 stock exchanges around the world. Using colocation services as a proxy for AT and HFT, we find mixed results on the impact of AT and HFT on the average trade size. Furthermore, we test whether the presence of HFT leads to the introduction of colocation services. The data are consistent with the view that HFT pre-dates colocation by at least 8 months on most exchanges, and has strong power in explaining the introduction of colocation services. In effect, our results show that colocation services do not properly measure effective AT and HFT; rather, colocation services are the result of HFT. Exchanges choose to offer colocation services due to the fact HFT requires higher speed transactions. Finally, we show there have been substantial changes in trade size in other countries such as China where there is no HFT, and offer explanations for these changes and suggest avenues for future research.

SEC and CFTC reports estimate that High Frequency strategies are responsible for about 60% of all transactions on U.S. shares. In Europe, this percentage is around 40% and growing. High Frequency strategies are those characterized by a brief holding period, which can range from a split second to a few hours. This enables traders to place numerous independent bets per day on an instrument or portfolio, profiting from the multiplicative effect postulated by the Fundamental Law of Active Management. The goal is to exploit inefficiencies derived from the market’s microstructure (rigidities, agents’ idiosyncrasies, asymmetric information, etc.). The generalization of electronic markets and ubiquitous automation of financial transactions has rendered many established models and theories obsolete. The objective of this work is to present a new scientific framework for the study of some of the most relevant questions concerning High Frequency.

Over the last two centuries, technological advantages have allowed some traders to be faster than others. We argue that, contrary to popular perception, speed is not the defining characteristic that sets High Frequency Trading (HFT) apart. HFT is the natural evolution of a new trading paradigm that is characterized by strategic decisions made in a volume-clock metric. Even if the speed advantage disappears, HFT will evolve to continue exploiting Low Frequency Trading’s (LFT) structural weaknesses. However, LFT practitioners are not defenseless against HFT players, and we offer options that can help them survive and adapt to this new environment.