| Abstract: |
We introduce a novel framework to financial time series forecasting that
leverages causality-inspired models to balance the trade-off between
invariance to distributional changes and minimization of prediction errors. To
the best of our knowledge, this is the first study to conduct a comprehensive
comparative analysis among state-of-the-art causal discovery algorithms,
benchmarked against non-causal feature selection techniques, in the
application of forecasting asset returns. Empirical evaluations demonstrate
the efficacy of our approach in yielding stable and accurate predictions,
outperforming baseline models, particularly in tumultuous market conditions. |