摘要翻译:
本文研究收益可预测时的资产配置问题。我们引入了一种市场择时的贝叶斯分层(BH)方法,该方法采用了由滞后基本特征驱动的异构时变系数。我们的方法包括条件期望收益和协方差矩阵的联合估计,并考虑了投资组合分析的估计风险。分层优先级允许在跨资产共享信息的同时分别建模不同的资产。我们展示了美国股票市场的表现。尽管贝叶斯预测略有偏差,但我们的BH方法在点和区间预测方面优于大多数替代方法。在最近20年的行业投资中,我们的BH方法提供了0.92%的平均月回报率和0.32%的显著Jensen's alpha。我们还发现,技术、能源和制造业是过去十年的重要行业,规模、投资和短期逆转因素占了很大比重。最后,用我们的BH方法构造的随机贴现因子解释了大部分异常现象。
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英文标题:
《Factor Investing: A Bayesian Hierarchical Approach》
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作者:
Guanhao Feng and Jingyu He
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最新提交年份:
2020
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分类信息:
一级分类:Economics 经济学
二级分类:Econometrics 计量经济学
分类描述:Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论,微观计量经济学,宏观计量经济学,通过新方法发现的经济关系的实证内容,统计推论应用于经济数据的方法论方面。
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一级分类:Statistics 统计学
二级分类:Applications 应用程序
分类描述:Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学,教育学,流行病学,工程学,环境科学,医学,物理科学,质量控制,社会科学
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英文摘要:
This paper investigates asset allocation problems when returns are predictable. We introduce a market-timing Bayesian hierarchical (BH) approach that adopts heterogeneous time-varying coefficients driven by lagged fundamental characteristics. Our approach includes a joint estimation of conditional expected returns and covariance matrix and considers estimation risk for portfolio analysis. The hierarchical prior allows modeling different assets separately while sharing information across assets. We demonstrate the performance of the U.S. equity market. Though the Bayesian forecast is slightly biased, our BH approach outperforms most alternative methods in point and interval prediction. Our BH approach in sector investment for the recent twenty years delivers a 0.92\% average monthly returns and a 0.32\% significant Jensen`s alpha. We also find technology, energy, and manufacturing are important sectors in the past decade, and size, investment, and short-term reversal factors are heavily weighted. Finally, the stochastic discount factor constructed by our BH approach explains most anomalies.
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PDF链接:
https://arxiv.org/pdf/1902.01015