Trading with Time Series Causal Discovery: An Empirical Study

Abstract

This study investigates the application of causal discovery algorithms in equity markets, with a focus on their potential to enhance investment strategies. An investment strategy was developed based on the causal structures identified by these algorithms, and its performance was evaluated to assess the profitability and effectiveness in stock market environments. The results indicate that causal discovery algorithms can successfully uncover actionable causal relationships in large markets, leading to profitable investment outcomes. However, the research also identifies a critical challenge: the computational complexity and scalability of these algorithms when dealing with large datasets, which presents practical limitations for their application in real-world market analysis.