Mispricing and Algorithm Trading

Lihong Zhang () and Xiaoquan (Michael) Zhang ()
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Lihong Zhang: School of Economics and Management, Tsinghua University, Beijing 100084, China
Xiaoquan (Michael) Zhang: School of Economics and Management, Tsinghua University, Beijing 100084, China; and CUHK Business School, Chinese University of Hong Kong, Shatin, NT, Hong Kong Special Administrative Region, China

Information Systems Research, 2025, vol. 36, issue 1, 21-40

Abstract: 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.

Keywords: mispricing; algorithm trading; fintech; market efficiency; financial trading (search for similar items in EconPapers)
Date: 2025
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