Investigating Causal Relationships in Stock Returns with Temporal Logic Based Methods

TL;DR

This paper introduces a logical formula-based framework for causal inference in stock return time series, outperforming traditional methods like Granger causality and uncovering novel relationships in real financial data.

Contribution

The paper presents a novel logical formulas approach for causal inference in financial time series, enabling complex hypothesis testing and integration of diverse data sources.

Findings

Outperforms Granger causality on simulated data

Discovers new causal relationships in real stock data

Framework allows integration of price, volume, and qualitative data

Abstract

We describe a new framework for causal inference and its application to return time series. In this system, causal relationships are represented as logical formulas, allowing us to test arbitrarily complex hypotheses in a computationally efficient way. We simulate return time series using a common factor model, and show that on this data the method described significantly outperforms Granger causality (a primary approach to this type of problem). Finally we apply the method to real return data, showing that the method can discover novel relationships between stocks. The approach described is a general one that will allow combination of price and volume data with qualitative information at varying time scales (from interest rate announcements, to earnings reports to news stories) shedding light on some of the previously invisible common causes of seemingly correlated price movements.