Return Decomposition

Long Chen, Xinlei Zhao
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引用次数: 1

Abstract

A crucial issue in asset pricing is to understand the relative importance of discount rate (DR) news and cash flow (CF) news in driving the time-series and cross-sectional variations of stock returns. Many studies directly estimate the DR news but back out the CF news as the residual. We argue that this approach has a serious limitation because the DR news cannot be accurately measured due to the small predictive power, and the CF news, as the residual, inherits the large misspecification error of the DR news. We apply this residual-based decomposition approach to Treasury bonds and equities, and find results that are either counter-intuitive or unrobust. Potential solutions, including modeling both DR news and CF news directly, the Bayesian model averaging approach, and the principal component analysis, are explored.
返回分解
资产定价的一个关键问题是理解贴现率(DR)新闻和现金流(CF)新闻在驱动股票收益的时间序列和横截面变化方面的相对重要性。许多研究直接估计了DR新闻,而将CF新闻作为残差剔除。我们认为这种方法存在严重的局限性,因为DR新闻由于预测能力小而无法准确测量,并且CF新闻作为残差继承了DR新闻的较大的误规范误差。我们将这种基于残差的分解方法应用于国债和股票,并发现结果要么是反直觉的,要么是不稳健的。探讨了潜在的解决方案,包括直接建模DR新闻和CF新闻,贝叶斯模型平均方法和主成分分析。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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