{"title":"A portfolio-level, sum-of-the-parts approach to return predictability","authors":"Hongyi Xu , Dean Katselas , Jo Drienko","doi":"10.1016/j.jempfin.2024.101525","DOIUrl":null,"url":null,"abstract":"<div><p>Existing research on return predictability traditionally employs aggregate, market-level information. To investigate the applicability of return predictability at a finer level, we examine out-of-sample time-series return predictability at the characteristic-based portfolio level, using predictive regressions with portfolio-level predictors and a <em>sum-of-the-parts</em> approach. In addition to rejecting the null of no predictability at the market level, we detect statistically and economically significant out-of-sample predictability amongst particular portfolios. Notably, we show that large growth portfolios exhibit return predictability, consistent with predictions drawn from prior literature, while we fail to consistently detect predictability for all remaining size and book-to-market portfolios. Our results reveal a significant (relative) forecast error R-squared of 0.65 % for large-growth stocks, translating into an annualised certainty equivalent gain of 1.37 %.</p></div>","PeriodicalId":15704,"journal":{"name":"Journal of Empirical Finance","volume":"78 ","pages":"Article 101525"},"PeriodicalIF":2.1000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Empirical Finance","FirstCategoryId":"96","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0927539824000604","RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
引用次数: 0
Abstract
Existing research on return predictability traditionally employs aggregate, market-level information. To investigate the applicability of return predictability at a finer level, we examine out-of-sample time-series return predictability at the characteristic-based portfolio level, using predictive regressions with portfolio-level predictors and a sum-of-the-parts approach. In addition to rejecting the null of no predictability at the market level, we detect statistically and economically significant out-of-sample predictability amongst particular portfolios. Notably, we show that large growth portfolios exhibit return predictability, consistent with predictions drawn from prior literature, while we fail to consistently detect predictability for all remaining size and book-to-market portfolios. Our results reveal a significant (relative) forecast error R-squared of 0.65 % for large-growth stocks, translating into an annualised certainty equivalent gain of 1.37 %.
关于回报率可预测性的现有研究历来采用市场层面的总体信息。为了在更细的层面上研究收益率可预测性的适用性,我们在基于特征的投资组合层面上研究了样本外时间序列收益率可预测性,使用了投资组合层面预测因子的预测回归和部分总和法。除了拒绝市场层面无可预测性的空值外,我们还在特定投资组合中发现了具有统计和经济意义的样本外可预测性。值得注意的是,我们发现大型成长型投资组合表现出收益可预测性,这与之前文献的预测一致,而我们未能持续检测到所有其他规模和账面市值投资组合的可预测性。我们的结果显示,大型成长型股票的(相对)预测误差 R 平方为 0.65%,相当于年化确定性收益 1.37%。
期刊介绍:
The Journal of Empirical Finance is a financial economics journal whose aim is to publish high quality articles in empirical finance. Empirical finance is interpreted broadly to include any type of empirical work in financial economics, financial econometrics, and also theoretical work with clear empirical implications, even when there is no empirical analysis. The Journal welcomes articles in all fields of finance, such as asset pricing, corporate finance, financial econometrics, banking, international finance, microstructure, behavioural finance, etc. The Editorial Team is willing to take risks on innovative research, controversial papers, and unusual approaches. We are also particularly interested in work produced by young scholars. The composition of the editorial board reflects such goals.