Understanding the Implications of Algorithmic and High-Frequency Trading

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 couple of years, my group has started to develop computational models of financial markets, with the goal of understanding the implications of new trading technology. Our first study focuses on latency arbitrage, an HFT strategy that exploits speed advantages in processing information across fragmented markets. We find that this particular practice reduces overall market efficiency. A simple mechanism remedy–introducing discrete-time clearing via one-second call markets–defeats the latency arms race and improves market efficiency. In ongoing research, we are extending the model to cover a broader range of trading strategies and market contexts, and parse out the various causal pathways whereby automated trading may benefit or harm financial markets.

Joint work with Elaine Wah.