A seminar by Professor Hui Chen from MIT
Title: Teaching Economics to the Machines
Abstract: While structural models in economics can offer valuable insights, they often sufferfrom a poor fit with the data and demonstrate suboptimal forecasting performances. Machine learning models, in contrast, offer rich flexibility but are prone to overfitting and tend to struggle to generalize beyond the confines of training data. We propose a novel framework that incorporates economic restrictions from a structural model into a machine learning model through transfer learning. Specifically, we first construct a neural-network representation of the structural model by training it on the synthetic data generated by the structural model, and then fine-tune the network using real data. In an application to option pricing, the transfer learning model significantly outperforms both the structural model and a conventional data-driven deep neural network. The out-performance is more significant when the sample size of real data is small or under
volatile market conditions.
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