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.
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Notes
Experimentation with different values does not change the qualitative nature of the results.
The sectors are consumer discretionary (CD), consumer staples (CS), energy (EN), financials (FN), health care (HC), industrials (ID), information technology (IT), materials (MT), communication services (TL), utilities (UT).
Except for the SVAR recursive forecasts, where the mean value of the hedged portfolios is lower than for the S&P500 and style portfolios.
A 0.5% trading cost would reduce average returns by around 0.3% based on the number of trades required for the long only portfolios and around 0.5% for hedged portfolios (based on when both buy and sell trades occur). For investors, the presence of such transaction costs may erode any gains identified above.
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McMillan, D.G. Forecasting sector stock market returns. J Asset Manag 22, 291–300 (2021). https://doi.org/10.1057/s41260-021-00220-6
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DOI: https://doi.org/10.1057/s41260-021-00220-6