Forecasting sector stock market returns

David G. McMillan ()
Additional contact information
David G. McMillan: University of Stirling

Journal of Asset Management, 2021, vol. 22, issue 4, No 5, 300 pages

Abstract: Abstract We seek to forecast sector stock returns using established predictor variables. Existing empirical evidence focuses on market level data, and thus, sector data provide fertile ground for research. In addition to in-sample predictive regressions, we consider recursive and rolling forecasts and whether such forecasts can be used successfully in a sector rotation portfolio. The results for ten sectors and eleven predictor variables highlight that two variables, the default return and stock return variance, have significant predictive power across the stock market series. Forecast results are also supportive of these series (especially the default return), which can outperform benchmark and alternative forecast models across a range of metrics. A sector rotation strategy based on these forecasts produces positive abnormal returns and a Sharpe ratio higher than the baseline model. An examination of the sectors at each rotation reveals that a small number of dominate in the constructed portfolios.

Keywords: Sectors; Stock returns; Forecasts; Time-varying (search for similar items in EconPapers)
JEL-codes: C22 G12 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1057/s41260-021-00220-6 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:pal:assmgt:v:22:y:2021:i:4:d:10.1057_s41260-021-00220-6

Ordering information: This journal article can be ordered from
http://www.springer.com/finance/journal/41260

DOI: 10.1057/s41260-021-00220-6

Access Statistics for this article

Journal of Asset Management is currently edited by Marielle de Jong and Dan diBartolomeo

More articles in Journal of Asset Management from Palgrave Macmillan
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().