 The RP-PCA factors and stock return predictability: An aligned approachQi Shi The North American Journal of Economics and Finance, 2023, vol. 64, issue C Abstract: Our study first investigates robust evidence for the predictive power of risk premium principal component analysis (RP-PCA) in forecasting equity returns and macroeconomic activity. We use the partial least squares (PLS) method to extract the optimal information from five RP-PCA factors, and the aligned RP-PCA index appears to outperform the original RP-PCA factors in various in-sample and out-of-sample diagnostic tests with little evidence of instability. Furthermore, the aligned RP-PCA index can generate adequately more profits than most of the other RP-PCA factors in an active market-timing trading strategy in excess of the historical mean forecast strategy. A vector autoregression-based stock return decomposition shows that the economic source of the forecasting power for the aligned RP-PCA index predominantly comes from the future cash flow channel. Keywords: RP-PCA; Partial least squares; Aligned RP-PCA index; Generate profits; Future cash flow channel (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:ecofin:v:64:y:2023:i:c:s1062940822001978 DOI: 10.1016/j.najef.2022.101862 Access Statistics for this article The North American Journal of Economics and Finance is currently edited by Hamid Beladi More articles in The North American Journal of Economics and Finance from Elsevier |
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