中国股票市场可预测性的实证研究
发布时间：2018-12-07 20:59
【摘要】：我们研究了中国市场投资组合和根据公司行业、规模、面值市值比和股权集中度等划分的各种成分投资组合的股票收益的可预测性。选取各种经济变量作为预测变量,中国市场投资组合和各种成分投资组合都存在显著的样本内和样本外可预测性。不同成分投资组合的可预测性存在显著差异,其中金融与保险业、房地产业和制造业等行业投资组合的可预测性特别强,小市值、低面值市值比和低股权集中度的投资组合也非常容易预测。对于成分投资组合间的可预测性差异,我们给出了两个经济解释:(1)基于样本外可预测性分解,我们发现条件CAPM模型捕捉的时变系统性风险溢价可预测性可以解释成分投资组合的大部分样本外可预测性,高系统性风险暴露的投资组合有较高的样本外可预测性;(2)基于Hong,Torous,andValkanov(2007)的信息流动摩擦理论,我们发现行业集中度可以显著解释行业投资组合间的可预测性差异。
[Abstract]:We studied the predictability of the stock returns of the Chinese market portfolio and the various components of the portfolio according to the company industry, size, par to market value ratio and equity concentration. By selecting various economic variables as predictive variables, the Chinese market portfolio and various component portfolios have significant predictability in and out of samples. There are significant differences in the predictability of different component portfolios, in which the portfolios of sectors such as finance and insurance, real estate and manufacturing are particularly predictable, with small market capitalization, Low par-to-market ratios and low equity concentration portfolios are also very predictable. We give two economic explanations for the predictability differences between component portfolios: (1) based on the extrinsic predictability decomposition, We find that the conditional CAPM model captures the predictability of the time-varying systemic risk premium which can explain most of the extrinsic predictability of the composition portfolio, while the portfolio with high systemic risk exposure has higher extrasample predictability. (2) based on the information flow friction theory of Hong,Torous,andValkanov (2007), we find that the degree of industry concentration can explain the predictable difference between industry portfolios.
【作者单位】： 新加坡管理大学李光前商学院;圣路易斯大学经济系;华盛顿大学奥林商学院;上海交通大学;中国金融研究院;
【分类号】：F224;F832.51
[Abstract]:We studied the predictability of the stock returns of the Chinese market portfolio and the various components of the portfolio according to the company industry, size, par to market value ratio and equity concentration. By selecting various economic variables as predictive variables, the Chinese market portfolio and various component portfolios have significant predictability in and out of samples. There are significant differences in the predictability of different component portfolios, in which the portfolios of sectors such as finance and insurance, real estate and manufacturing are particularly predictable, with small market capitalization, Low par-to-market ratios and low equity concentration portfolios are also very predictable. We give two economic explanations for the predictability differences between component portfolios: (1) based on the extrinsic predictability decomposition, We find that the conditional CAPM model captures the predictability of the time-varying systemic risk premium which can explain most of the extrinsic predictability of the composition portfolio, while the portfolio with high systemic risk exposure has higher extrasample predictability. (2) based on the information flow friction theory of Hong,Torous,andValkanov (2007), we find that the degree of industry concentration can explain the predictable difference between industry portfolios.
【作者单位】： 新加坡管理大学李光前商学院;圣路易斯大学经济系;华盛顿大学奥林商学院;上海交通大学;中国金融研究院;
【分类号】：F224;F832.51
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