Merging Economic Theory And Machine Learning For Unprecedented Insights

The long-held belief that structural economic models and machine learning models must operate in complete isolation is proving to be outdated. Research now proposes a method for harmonizing these two distinct quantitative approaches. This integrated system uses economic theory not as a constraint, but as a guiding framework for advanced data analysis.

The Limits of Traditional Economic Structures

Structural economic models are recognized for being parsimonious and highly interpretable. They provide a clear view of causal mechanisms. Yet, these models frequently exhibit poor data fit. Their forecasting performance remains limited in real-world applications.

By sharp contrast, standard machine learning models offer substantial flexibility in mapping complex data relationships. This flexibility, however, often comes at a cost. The models are prone to overfitting. Weak out-of-distribution generalization is a persistent risk, making predictions unreliable when market conditions shift unexpectedly.

Integrating Theory through Transfer Learning

A new theory-guided transfer learning framework is designed to overcome these deficiencies. The core methodology integrates structural restrictions derived from economic theory directly into the machine learning architecture.

This process involves two crucial steps. A neural network is initially pre-trained on synthetic data sets. These data sets are generated entirely by the chosen structural economic model, establishing the theoretical boundaries. Following this pre-training, the network is fine-tuned using empirical market data.

This approach ensures that potentially misspecified economic restrictions still inform and regularize the learning process on empirical data.

Option Pricing Gains and Stability

Application of this framework to complex financial problems, such as option pricing, demonstrates clear advantages. The hybrid model substantially outperforms both established structural models and purely data-driven benchmarks.

The gains were especially large in small samples. Significant performance increases were noted under unstable market conditions. When model misspecification was limited, the results were strongest.

Refining Economic Insights

Beyond improved predictive accuracy, this framework introduces critical diagnostic tools. The integration provides actionable metrics for identifying weaknesses within the existing structural economic models themselves.

How effectively the structural model complements the available data? This research introduces a new model-comparison metric based on data-model complementarity. This allows for a deeper understanding of economic relationships. The complexity of financial market volatility. Understanding when to trust the assumptions. This integration accelerates the refinement of quantitative finance.

The views expressed in this research are those of the authors. They do not necessarily reflect the views of the National Bureau of Economic Research.

Structural economic models, while parsimonious and interpretable, often exhibit poor data fit and limited forecasting performance.

Alternative viewpoints and findings: See here