An Information Bottleneck Asset Pricing Model
This work addresses overfitting issues in financial asset pricing models for practitioners, representing an incremental improvement by applying an existing information bottleneck technique to this domain.
The authors tackled the problem of deep neural networks overfitting to noise in financial asset pricing data by proposing an information bottleneck model that compresses data to eliminate redundant information while retaining critical signals, resulting in improved performance, though specific numerical gains are not detailed in the abstract.
Deep neural networks (DNNs) have garnered significant attention in financial asset pricing, due to their strong capacity for modeling complex nonlinear relationships within financial data. However, sophisticated models are prone to over-fitting to the noise information in financial data, resulting in inferior performance. To address this issue, we propose an information bottleneck asset pricing model that compresses data with low signal-to-noise ratios to eliminate redundant information and retain the critical information for asset pricing. Our model imposes constraints of mutual information during the nonlinear mapping process. Specifically, we progressively reduce the mutual information between the input data and the compressed representation while increasing the mutual information between the compressed representation and the output prediction. The design ensures that irrelevant information, which is essentially the noise in the data, is forgotten during the modeling of financial nonlinear relationships without affecting the final asset pricing. By leveraging the constraints of the Information bottleneck, our model not only harnesses the nonlinear modeling capabilities of deep networks to capture the intricate relationships within financial data but also ensures that noise information is filtered out during the information compression process.