NeuralBeta: Estimating Beta Using Deep Learning
This work addresses limitations in beta estimation for finance applications like hedging, though it is incremental as it builds on existing neural network approaches with added interpretability.
The paper tackled the problem of estimating beta in finance by developing NeuralBeta, a neural network method that handles univariate and multivariate scenarios and tracks dynamic beta behavior, demonstrating superior performance compared to benchmark methods, especially during market regime shifts.
Traditional approaches to estimating beta in finance often involve rigid assumptions and fail to adequately capture beta dynamics, limiting their effectiveness in use cases like hedging. To address these limitations, we have developed a novel method using neural networks called NeuralBeta, which is capable of handling both univariate and multivariate scenarios and tracking the dynamic behavior of beta. To address the issue of interpretability, we introduce a new output layer inspired by regularized weighted linear regression, which provides transparency into the model's decision-making process. We conducted extensive experiments on both synthetic and market data, demonstrating NeuralBeta's superior performance compared to benchmark methods across various scenarios, especially instances where beta is highly time-varying, e.g., during regime shifts in the market. This model not only represents an advancement in the field of beta estimation, but also shows potential for applications in other financial contexts that assume linear relationships.