Article

Forecasting Sector Stock Market Returns

Details

Citation

McMillan D (2021) Forecasting Sector Stock Market Returns. Journal of Asset Management, 22 (5), pp. 291-300. https://doi.org/10.1057/s41260-021-00220-6

Abstract
We seek to forecast sector stock returns using established predictor variables. Existing empirical evidence focuses on market level data and thus sector data provides 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 examining of the sectors at each rotation reveals that a small number of dominate in the constructed portfolios.

Keywords
Sectors; Stock Returns; Forecasts; Time-Varying

Journal
Journal of Asset Management: Volume 22, Issue 5

StatusPublished
Publication date31/07/2021
Publication date online04/05/2021
Date accepted by journal08/04/2021
URLhttp://hdl.handle.net/1893/32519
ISSN1470-8272
eISSN1479-179X

People (1)

Professor David McMillan

Professor David McMillan

Professor in Finance, Accounting & Finance

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