23 April 2015, Computational Social Choice Seminar, Michael Wellman
Recent years have seen a dramatic increase in algorithmic trading, to the point that the majority of orders in major equity exchanges today are generated by machines without direct human control. Experience has shown that this automation--particularly at the extremes of high-frequency trading (HFT)--makes a qualitative difference, and raises fundamental issues for the efficiency, fairness, and stability of financial markets. Over the past few years, my group has started to develop computational models of financial markets, with the goal of understanding the implications of new trading technology. Not surprisingly, whether HFT improves or degrades market performance depends on the market context, as well as the specific trading tactic employed. Our study of latency arbitrage--an HFT strategy that exploits speed advantages in processing information across fragmented markets--concludes that this particular practice reduces overall market efficiency. Introducing discrete-time clearing via one-second call markets defeats the latency arms race and improves market efficiency. Another algorithmic practice, market making, is generally regarded as improving markets through liquidity provision. Through a methodology combining agent-based modeling with game-theoretic reasoning, we show that market making is indeed beneficial in circumstances where investors are sufficiently impatient.
Michael Wellman is Professor of Computer Science & Engineering at the University of Michigan. He received his PhD from MIT in 1988 for work in qualitative probabilistic reasoning and decision-theoretic planning. For the past 20+ years, his research has focused on computational market mechanisms for distributed decision making and electronic commerce. He is a Fellow of the Association for the Advancement of Artificial Intelligence (AAAI) and the Association for Computing Machinery (ACM).