Application of Deep Learning for Factor Timing in Asset Management
Fuente:
arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910536406401024 |
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| author | Panda, Prabhu Prasad Gharanchaei, Maysam Khodayari Chen, Xilin Lyu, Haoshu |
| author_facet | Panda, Prabhu Prasad Gharanchaei, Maysam Khodayari Chen, Xilin Lyu, Haoshu |
| contents | The paper examines the performance of regression models (OLS linear regression, Ridge regression, Random Forest, and Fully-connected Neural Network) on the prediction of CMA (Conservative Minus Aggressive) factor premium and the performance of factor timing investment with them. Out-of-sample R-squared shows that more flexible models have better performance in explaining the variance in factor premium of the unseen period, and the back testing affirms that the factor timing based on more flexible models tends to over perform the ones with linear models. However, for flexible models like neural networks, the optimal weights based on their prediction tend to be unstable, which can lead to high transaction costs and market impacts. We verify that tilting down the rebalance frequency according to the historical optimal rebalancing scheme can help reduce the transaction costs. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_18017 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Application of Deep Learning for Factor Timing in Asset Management Panda, Prabhu Prasad Gharanchaei, Maysam Khodayari Chen, Xilin Lyu, Haoshu Portfolio Management Machine Learning Computational Finance The paper examines the performance of regression models (OLS linear regression, Ridge regression, Random Forest, and Fully-connected Neural Network) on the prediction of CMA (Conservative Minus Aggressive) factor premium and the performance of factor timing investment with them. Out-of-sample R-squared shows that more flexible models have better performance in explaining the variance in factor premium of the unseen period, and the back testing affirms that the factor timing based on more flexible models tends to over perform the ones with linear models. However, for flexible models like neural networks, the optimal weights based on their prediction tend to be unstable, which can lead to high transaction costs and market impacts. We verify that tilting down the rebalance frequency according to the historical optimal rebalancing scheme can help reduce the transaction costs. |
| title | Application of Deep Learning for Factor Timing in Asset Management |
| topic | Portfolio Management Machine Learning Computational Finance |
| url | https://arxiv.org/abs/2404.18017 |