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.

我们寻求使用已建立的预测变量来预测行业的股票收益。现有的经验证据集中于市场水平的数据，因此，部门数据为研究提供了沃土。除了样本内预测回归之外，我们还考虑递归和滚动预测，以及这些预测是否可以成功地应用于部门轮换组合中。十个行业和十一个预测变量的结果表明，两个变量，即默认收益率和股票收益方差，在整个股票市场系列中具有显着的预测能力。预测结果也支持这些系列（尤其是默认收益），它们可以在一系列指标上胜过基准和替代预测模型。基于这些预测的行业轮换策略会产生正的异常收益，并且夏普比率高于基线模型。在每次轮换中对部门的检查表明，在构建的投资组合中有少数占主导地位。