NeuralBeta：使用深度学习估计 Beta
NeuralBeta: Estimating Beta Using Deep Learning
AI总结：
本文提出 NeuralBeta 深度学习方法，利用受正则化加权线性回归启发的输出层估计动态 beta，并在合成与市场数据上验证其优于基准方法。
摘要：
金融领域中估计 beta 的传统方法往往涉及僵化假设，无法充分捕捉 beta 的动态特征，从而限制了其在对冲等用例中的有效性。为克服这些局限，我们开发了一种名为 NeuralBeta 的新型神经网络方法，该方法能够处理单变量和多变量情形，并跟踪 beta 的动态行为。为解决可解释性问题，我们引入了一个受正则化加权线性回归启发的新型输出层，从而为模型的决策过程提供透明度。我们在合成数据和市场数据上进行了大量实验，结果表明 NeuralBeta 在多种情形下均优于基准方法，尤其是在 beta 高度时变的情况下，例如市场发生状态转换期间。该模型不仅代表了 beta 估计领域的一项进展，也展示了在其他假设存在线性关系的金融场景中的应用潜力。
英文摘要：
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.
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