Predicting stock returns: A risk measurement perspective
Jie Kang and
International Review of Financial Analysis, 2021, vol. 74, issue C
This paper proposes a new and efficient model selection strategy to obtain significant stock returns predictability from a risk measurement perspective. The risk interval is defined as the distance between the current actual return and the returns' historical average. The model selection strategy involves switching stock return forecasting models according to different risk intervals from the mean reversion and extreme value theory. This new strategy generates encouraging results in the empirical analysis. A mean-variance investor can realize sizeable economic gains by allocating assets through this new approach relative to competing forecasting models. Furthermore, the strategy performs robustly under alternative settings from both statistical and economic perspectives.
Keywords: Stock return predictability; Risk measurement; Model selection; Out-of-sample forecasting; Asset allocation (search for similar items in EconPapers)
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1) Track citations by RSS feed
Downloads: (external link)
Full text for ScienceDirect subscribers only
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
Persistent link: https://EconPapers.repec.org/RePEc:eee:finana:v:74:y:2021:i:c:s1057521921000193
Access Statistics for this article
International Review of Financial Analysis is currently edited by B.M. Lucey
More articles in International Review of Financial Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().