NeuralBeta: Estimating Beta Using Deep Learning
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909367373135872 |
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| author | Liu, Yuxin Lin, Jimin Gopal, Achintya |
| author_facet | Liu, Yuxin Lin, Jimin Gopal, Achintya |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_01387 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | NeuralBeta: Estimating Beta Using Deep Learning Liu, Yuxin Lin, Jimin Gopal, Achintya Statistical Finance Machine Learning 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. |
| title | NeuralBeta: Estimating Beta Using Deep Learning |
| topic | Statistical Finance Machine Learning |
| url | https://arxiv.org/abs/2408.01387 |