Machine learning models in stock prediction are often criticized for overfitting. To mitigate this, economic theory can impose structural constraints during model building. For instance, in an interest rate expectation model, incorporate the Taylor Rule as a prior: force the model to respect that policy rates respond positively to inflation gaps and output gaps (e.g., add regularization terms penalizing deviations from ∂r/∂(π−π*) > 0 and ∂r/∂y > 0). Embed no-arbitrage conditions from the Expectations Hypothesis, ensuring predicted yield curves satisfy forward rate consistency. Such theory-guided constraints reduce parameters, enhance generalization, and align predictions with economic intuition, yielding more robust forecasts than pure data-driven approaches.
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