Mispricing and Algorithm Trading

The widespread adoption of information technology has fundamentally transformed the way information is processed in the financial market. One such technological advancement is algorithm trading, which allows traders to develop sophisticated strategies based on historical price data. This raises important questions: Do these algorithm trading strategies contribute to market instability? When do they yield profits for different market participants? To address these questions, we must move beyond the efficient market hypothesis, as this theory would suggest that such strategies yield no profit due to market efficiency. Instead, we explicitly incorporate initial market mispricing into our analysis and develop a stylized continuous-time model of algorithm feedback trading to investigate market outcomes. Our model yields closed-form solutions, enabling us to assess the degree to which the price diverges from the efficient level. We discover that algorithmic trading, when combined with initial market mispricing, can lead to significant market volatility, resulting in financial bubbles and crashes. However, this scenario only occurs when there is overpricing and the algorithm traders collectively employ a strategy that enlarges the mispricing. Depending on the initial mispricing in the form of underpricing or overpricing, different algorithm trading strategies (positive or negative) have different market impact, profitability, and policy implications.

History: Ram Gopal, Senior Editor; Yan Huang, Associate Editor.

Funding: L. Zhang received financial support from the National Key Research and Development Program of China [Grant 2007CB814902], the National Natural Science Foundation of China [Grant 70671061], and the Tsinghua University Initiative Scientific Research Program [Grant 2021THZWJC28]. X. (M.). Zhang received financial support from the Hong Kong Research Grant Council [Grants GRF 14500521, 165052947, 14501320, and 14503818; and TRS: T31-604/18-N].

Supplemental Material: The online appendix is available at https://doi.org/10.1287/isre.2021.0570.