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A deeper comprehension of financial markets necessitates understanding notonly the statistical dependencies among various entities but also the causaldependencies. This paper extends the Constraint-based Causal Discovery fromHeterogeneous Data algorithm to account for lagged relationships in time-seriesdata (an algorithm we call CD-NOTS), shedding light on the complex causalrelations between different financial assets and variables. We compare theperformance of different algorithmic choices, such as the choice of conditionalindependence test, to give general advice on the effective way to use CD-NOTS.Using the results from the simulated data, we apply CD-NOTS to a broad range ofindices and factors in order to identify causal connections among the entities,thereby showing how causal discovery can serve as a valuable tool forfactor-based investing, portfolio diversification, and comprehension of marketdynamics. Further, we show our algorithm is a more effective alternative toother causal discovery algorithms since the assumptions of our algorithm aremore realistic in terms of financial data, a conclusion we find isstatistically significant.

Causal Discovery in Financial Markets: A Framework for Nonstationary Time-Series Data