  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.
