Mispricing and Algorithm Trading

Abstract

This study relaxes the efficient market hypothesis by introducing a model that accounts for initial mispricing and explores the effects of algorithmic trading. The research finds that algorithmic strategies can cause significant market volatility and affect financial stability, particularly when they amplify overpricing, leading to bubbles and crashes. Key insights include: Initial mispricing is crucial for algorithmic trading to impact market prices. Market reactions vary with the direction of the trading strategy relative to the asset’s true value. Informed traders can benefit from mispricing, whereas noise traders typically incur losses. Policy implications suggest that algorithmic trading is not universally harmful; its effects depend on the alignment of trading strategies with accurate pricing. The study advises regulators to differentiate between stabilizing and destabilizing trading practices. For traders, the research highlights the importance of adaptive strategies that help correct mispricing to ensure long-term profitability and market health. This research advances our understanding of algorithmic trading’s dual potential and informs the development of more nuanced financial regulations and trading strategies.