Ben Van Vliet

Ethics, Finance, and Automation: A Preliminary Survey of Problems in High Frequency Trading

All of finance is now automated, most notably high frequency trading. This paper examines the ethical implications of this fact. As automation is an interdisciplinary endeavor, we argue that the interfaces between the respective disciplines can lead to conflicting ethical perspectives; we also argue that existing disciplinary standards do not pay enough attention to the ethical problems automation generates. Conflicting perspectives undermine the protection those who rely on trading should have. Ethics in finance can be expanded to include organizational and industry-wide responsibilities to external market participants and society. As a starting point, quality management techniques can provide a foundation for a new cross-disciplinary ethical standard in the age of automation.

Trading Machines: Using SPC to Assess Performance of Financial Trading Systems

Traditional quantitative methods used in finance, developed to control market risks accumulated by human traders, do not incorporate quality control. Finance is moving from manual to computer numerical control (CNC). The authors view trading systems that employ CNC as machines with variable outputs—outputs that can be inspected using statistical process control (SPC). Using a sample trading system, they find that quality metrics generate an investment signal contrary to that generated by the traditional metrics of finance. To manage trading machines, SPC can be used in conjunction with classical financial metrics to control risk in systematic trading and investment.

Trading Model Uncertainty and Statistical Process Control

This article examines the use of statistical process control (SPC) as a methodology for monitoring trading model uncertainty. Traditional quantitative risk management methods do not incorporate the inherent process control problems financial modeling. The reference adaptations apply to the a priori model design process and a posteriori model control. To build and monitor trading models, SPC can be used in conjunction with classical financial metrics to better control market risk.

Whole Distribution Statistical Process Control in High Frequency Trading

High-frequency trading enables real-time control of outputs. However, sampling techniques in traditional statistical process control (SPC) may be too slow to detect rapid changes in market structure. The authors develop statistical tests that examine each event using the generalized lambda distribution. They demonstrate the manner in which this provides a more descriptive and quicker-reacting method of process control than that of traditional SPC.

Phantom Liquidity and High Frequency Quoting

This article examines every NASDAQ ITCH feed message for S&P 500 Index stocks for 2012 and identifies clusters of extremely high and extremely low limit-order cancellation activity. The authors find results consistent with the idea that cancel clusters are the result of high-frequency traders jockeying for queue position and reacting to information to establish a new price level. Furthermore, few trades seem to be executed during cancel clusters or even immediately after them. Low cancellation activity seems to be markedly different, with many level changes all caused by executions. The results are consistent with high-frequency trading firms behaving as agents who bring efficiency to the market without the need to have executions at intermediate prices. The authors also discuss the misconception that investors and low-frequency traders are synonymous and its implications for policy given these results.

Multi-Scale Capability: A Better Approach to Performance Measurement for Algorithmic Trading

This paper develops a new performance measurement methodology for algorithmic trading. By adapting capability from the quality control literature, we present new criteria for assessing control, expected tail loss and risk-adjusted performance in a single framework. The multi-scale capability measure we present is more descriptive and more appropriate for algorithmic trading than the traditional measure used in finance. It is robust to non-normality and the multiple time horizon decision processes inherent in algorithmic trading. We also argue that an algorithmic trading strategy, indeed any investment strategy, which satisfies the criteria to be multi-scale capable also satisfies any definition of prudence. It will be unlikely to harm the investor or external market participants in the event of its failure, while providing a high likelihood of satisfactory risk-adjusted performance.

Beyond the Flash Crash: Systemic Risk, Reliability, and High Frequency Financial Markets

Extreme events in financial markets can arise from fundamental information, but they can also arise from latent hazards embedded in the market design. This concept is known as systemic risk, and someone must bear it. Extreme events add to risk, and their probability and severity must be accounted for by market participants. This article shows how this risk fits into the finance literature and that, from an engineering perspective, this risk in markets has never been lower. The industry is evolving to mitigate this risk. This article presents an overview of the complexity of the automated market network and describes how market participants interact through the exchange mechanism. It defines new terms and a new framework for understanding the risk of extreme market moves from a reliability and safety perspective.

Expected Return in High Frequency Trading

Defining a in high-frequency trading is more complicated than in low-frequency trading since not all strategies are based on price forecasts. More components are required, as is an understanding of the interactions between them. In this article, we develop the a attribution model for high-frequency trading by explicating its components and the trading tactics used to implement high-frequency strategies. The results show why high-frequency traders need to be fast in order to generate positive expected returns and why they are better at providing liquidity. We provide an example implementation, using a sample of high-frequency equity data.

A Practical Real Options Approach to Valuing High Frequency Trading System R&D Projects

In the age of automation, trading and market making is about estimating the fair price of automated trading system research and development projects. This requires a new methodology to arrive at such a fair price. A real options framework is a natural choice. In this paper we review a methodology for automated trading system R&D as well as a practical real option model for valuing such projects so as to enable rapid strategy cycling.

Independent Component Analysis for Realized Volatility: Analysis of the Stock Market Crash of 2008

This paper investigates the factors that drove the U.S. equity market returns from 2007 to early 2010. The period was highlighted by volatile energy and commodity prices, the collapse of insurance and banking firms, extreme implied volatility and a subsequent rally in the overall market. To extract the driving factors, we decompose the returns of the S&P500 sector ETFs into statistically independent signals using independent component analysis. We find that the generated factors have interesting financial interpretations and are consistent with the major economic themes of the period. We find that there are two sets of general market betas during the period along with a dominant factor for energy and materials sector. In addition, we find that the EGARCH model which accommodates asymmetric responses between returns and volatility can plausibly fit the high levels of variance during the crash. Finally, estimated correlations dropped when commodity prices moved higher, but then spiked when the S&P500 crashed in late 2008.