Research Paper#Financial Forecasting, Causal Inference, Time Series Analysis🔬 ResearchAnalyzed: Jan 3, 2026 08:52
Causal Observables for Financial Forecasting
Published:Dec 31, 2025 04:30
•1 min read
•ArXiv
Analysis
This paper addresses the challenge of short-horizon forecasting in financial markets, focusing on the construction of interpretable and causal signals. It moves beyond direct price prediction and instead concentrates on building a composite observable from micro-features, emphasizing online computability and causal constraints. The methodology involves causal centering, linear aggregation, Kalman filtering, and an adaptive forward-like operator. The study's significance lies in its focus on interpretability and causal design within the context of non-stationary markets, a crucial aspect for real-world financial applications. The paper's limitations are also highlighted, acknowledging the challenges of regime shifts.
Key Takeaways
- •Focuses on constructing interpretable and causal signals for financial forecasting.
- •Employs a multi-step methodology including causal centering, aggregation, filtering, and an adaptive operator.
- •Highlights the potential and limitations of causal signal design in non-stationary markets.
- •Emphasizes online computability and causal constraints.
Reference
“The resulting observable is mapped into a transparent decision functional and evaluated through realized cumulative returns and turnover.”