Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/32519
Appears in Collections:Accounting and Finance Journal Articles
Peer Review Status: Refereed
Title: Forecasting Sector Stock Market Returns
Author(s): McMillan, David
Contact Email: david.mcmillan@stir.ac.uk
Keywords: Sectors
Stock Returns
Forecasts
Time-Varying
Issue Date: Jul-2021
Date Deposited: 12-Apr-2021
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.
DOI Link: 10.1057/s41260-021-00220-6
Rights: This item has been embargoed for a period. During the embargo please use the Request a Copy feature at the foot of the Repository record to request a copy directly from the author. You can only request a copy if you wish to use this work for your own research or private study. This is a post-peer-review, pre-copyedit version of an article published in Journal of Asset Management The final authenticated version is available online at: https://doi.org/10.1057/s41260-021-00220-6.
Licence URL(s): https://storre.stir.ac.uk/STORREEndUserLicence.pdf

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