Mispricing and Algorithm Trading

• 成果类型: Article
• 署名作者: Zhang, Lihong; Zhang, Xiaoquan (Michael)
• 署名单位: Tsinghua University; Chinese University of Hong Kong
• 刊物名称: INFORMATION SYSTEMS RESEARCH
• ISSN/ISSBN: 1047-7047
• DOI: 10.1287/isre.2021.0570
• 发表日期: 2025
• 关键词: information-technology; STOCK; MARKET; COMPETITION; prices; rationality; volatility; strategies; EFFICIENCY; investors

摘要

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.