Causality-Inspired Models for Financial Time Series Forecasting

ArXiv ID: 2408.09960 “View on arXiv”

Authors: Unknown

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.

Keywords: Causal Discovery, Asset Return Forecasting, Causality-Inspired Models, Distributional Changes, Machine Learning, Equities

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced causal inference theory and invariant prediction frameworks, with substantial mathematical derivations. It provides a comprehensive empirical evaluation on real financial data (SPY and macroeconomic indicators, 2000-2022), comparing multiple causal discovery algorithms against baselines and assessing economic performance through a trading strategy.

  flowchart TD
    A["Research Goal:<br>Causal Discovery for<br>Asset Return Forecasting"] --> B["Data Inputs:<br>Equities & Financial Indicators"]
    B --> C["Methodology:<br>Causal Discovery vs.<br>Non-Causal Feature Selection"]
    C --> D["Computational Process:<br>Causality-Inspired<br>ML Forecasting Models"]
    D --> E{"Key Findings:<br>Superior Stability & Accuracy<br>Under Distributional Changes"}