| 基于神经网络的股票收益率预测研究 |
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| 引用本文： | 潘水洋,刘俊玮,王一鸣.基于神经网络的股票收益率预测研究[J].浙江大学学报(理学版),2019,46(5):550-555. |
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| 作者姓名： | 潘水洋 刘俊玮 王一鸣 |
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| 作者单位： | 北京大学 经济学院，北京 100871 |
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| 基金项目： | 国家社会科学基金青年项目(18CJY057). |
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| 摘 要： | 在金融领域的资产定价模型修正过程中，股市的非线性现象往往被选择性忽视，未纳入模型框架，现有模型亦无法刻画因子之间的非线性定价结构。为解决上述问题，引入了机器学习领域中的神经网络模型，以捕获市场组合收益率、市值、账面市值比三因子间的非线性定价结构，并对股票收益率进行预测。将该模型与经典Fama-French三因子模型在样本外拟合优度、多空策略业绩表现上做了对比，结果表明：神经网络模型能精准捕获市场组合收益率、市值、账面市值比3个因子之间的非线性关系，且在样本外拟合优度、多空策略业绩表现上均要优于传统三因子线性定价模型。
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| 关 键 词： | 机器学习 非线性资产定价 神经网络 |
| 收稿时间： | 2018-12-26 |
Forecasting stock returns with artificial neural networks. |
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| PAN Shuiyang,LIU Junwei,WANG Yiming.Forecasting stock returns with artificial neural networks.[J].Journal of Zhejiang University(Sciences Edition),2019,46(5):550-555. |
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| Authors: | PAN Shuiyang LIU Junwei WANG Yiming |
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| Affiliation: | School of Economics, Peking University, Beijing 100871,China |
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| Abstract: | In the process of correcting the asset pricing model, the stock market exhibits nonlinear phenomena.Nevertheless this widespread financial disparity is ignored selectively and not included in the model framework. The challenge is that the existing model cannot describe the nonlinear pricing structure among factors. In this paper, a neural network model in the field of machine learning is presented to capture the nonlinear pricing structure between the three factors of firm size, book-to-market ratio and return on the market portfolio. Based on the Chinese A-share market data, this model is fully compared with the classic Fama-French three-factor model. The empirical results show that the neural network model is better than the Fama-French three-factor linear pricing model in terms of goodness of fit outside the sample and performance of long-short strategy. |
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| Keywords: | machine learning nonlinear asset pricing neural network |
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