Trading with Time Series Causal Discovery: An Empirical Study

TL;DR

This paper empirically evaluates causal discovery algorithms in equity markets, demonstrating their potential to inform profitable investment strategies while highlighting computational scalability challenges.

Contribution

It provides an empirical assessment of causal discovery algorithms' effectiveness in stock market investment strategies and discusses their practical limitations.

Findings

Causal discovery algorithms can identify actionable causal relationships in markets.

Strategies based on causal structures can be profitable.

Scalability issues limit real-world application.

Abstract

This study investigates the application of causal discovery algorithms in equity markets, with a focus on their potential to build investment strategies. An investment strategy was developed based on the causal structures identified by these algorithms. The performance of the strategy is evaluated based on the profitability and effectiveness in stock markets. 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. This challenge presents practical limitations for their application in real-world market analysis.