Financial Time Series Forecasting Using State-Space Models and Causal Discovery: A Deep Dive for Robust Investment Decisions
Predicting the complex movements of financial markets goes beyond simple statistical pattern recognition. This article explores how to combine the powerful flexibility of State-Space Models with the deep insights of Causal Discovery to identify hidden drivers in noisy financial time series data and build more robust and explainable forecasting models. This is a game-changer that can revolutionize the reliability of investment decisions.
1. The Challenge / Context
Financial markets are inherently non-linear, non-stationary, and unpredictable systems driven by numerous interconnected factors. Traditional time series models like ARIMA or GARCH capture specific patterns well but often face the fundamental problem that "correlation is not causation." We cannot simply conclude that one causes the other just because stock prices and certain economic indicators move together, which diminishes the robustness and explainability of forecasting models. For transparent and logical investment decisions, an advanced framework is needed to go beyond superficial correlations to understand true causal drivers and, based on them, infer the market's hidden intrinsic state. Now is the time for forecasting models that can answer "why will it happen?" beyond just "what will happen?"
