基于小波神经网络的高阶CAPM实证研究
发布时间：2018-07-27 16:14
【摘要】：本文在传统CAPM的基础上,引入了一个高阶的CAPM。借助小波神经网络在非线性函数逼近方面的优势,使用上海证券交易所股票数据分别对二阶至四阶CAPM进行了实证分析。最终的研究结果表明:就上海股市而言,12只大盘股组合已经能够有效分散非系统风险,而12只小盘股不能充分化解非系统风险,存在所谓的"规模效应";训练后的网络预测显示,高阶CAPM无论是在预测精度还是预测稳定性上都要明显优于传统的CAPM,在一个非系统风险得到充分分散的证券组合中,加入三阶矩的CAPM已经能够比较准确地把握风险资产的市场定价。
[Abstract]:In this paper, a high order CAPM is introduced based on the traditional CAPM. Based on the advantage of wavelet neural network in nonlinear function approximation, the stock data of Shanghai Stock Exchange are used to analyze the second-order to fourth-order CAPM. The final research results show that, for Shanghai stock market, 12 large-cap stocks have been able to effectively disperse the non-systematic risk, while 12 small-cap stocks can not fully resolve the non-systematic risk. There is a so-called "scale effect", and the trained network prediction shows that the high order CAPM is superior to the traditional CAPM in both prediction accuracy and prediction stability, and in a portfolio where the non-systematic risk is fully dispersed. CAPM with three-order moment has been able to grasp the market pricing of risk assets more accurately.
【作者单位】： 中南财经政法大学金融学院;
【分类号】：F224;F832.51
[Abstract]:In this paper, a high order CAPM is introduced based on the traditional CAPM. Based on the advantage of wavelet neural network in nonlinear function approximation, the stock data of Shanghai Stock Exchange are used to analyze the second-order to fourth-order CAPM. The final research results show that, for Shanghai stock market, 12 large-cap stocks have been able to effectively disperse the non-systematic risk, while 12 small-cap stocks can not fully resolve the non-systematic risk. There is a so-called "scale effect", and the trained network prediction shows that the high order CAPM is superior to the traditional CAPM in both prediction accuracy and prediction stability, and in a portfolio where the non-systematic risk is fully dispersed. CAPM with three-order moment has been able to grasp the market pricing of risk assets more accurately.
【作者单位】： 中南财经政法大学金融学院;
【分类号】：F224;F832.51
【参考文献】
相关期刊论文 前3条
1 万欣荣;蒋少戈;朱红磊;;我国股票收益影响因素的定价模型实证研究[J];金融研究;2005年12期
2 靳云汇,刘霖;中国股票市场CAPM的实证研究[J];金融研究;2001年07期
3 杨p，
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