Abstract
We find that imposing economic constraint on stock return forecasts based on the Interquartile Range of equity premium can significantly strengthen predictive performance. Specifically, we construct a judgment mechanism that truncates the outliers in forecasts of stock return. We prove that our constraint approach can realize more accurate predictive information relative to the unconstraint approach from the perspective of statistics and economics. In addition, the new constraint approach can effectively defeat CT constraint and CDA strategy. The three mixed models we proposed can further enhance the accuracy of prediction, especially the mixed model combined with our constraint approach. Finally, utilizing our new constraint approach can help investors obtain considerable economic gains. With the application of extension and robustness analysis, our results are robust.
Highlights
Stock is a major financial product in the financial market. e uncertainty of stock market fluctuation involves numerous economic behaviors and contains many political factors. erefore, researching the prediction of stock return has always been a considerable subject for many scholars and practitioners
A large number of scholars have proposed many predictors linked to macrofundamentals, which can enhance the predictability of out-of-sample stock returns, for example, oil-related variables (Liu et al [7]), the variance risk premium (Bollerslev et al [8, 9]), technical indicators (Neely et al [10]), economic policy uncertainty (Brogaard and Detzel [11]), manager sentiment
When investors use our constraint approach, the higher certainty equivalent return (CER) gains can be yielded compared to the unconstraint model among all predictors, which means that eliminating outliers in stock return forecasts can help investors to allocate their assets and create more substantial economic gains
Summary
Stock is a major financial product in the financial market. e uncertainty of stock market fluctuation involves numerous economic behaviors and contains many political factors. erefore, researching the prediction of stock return has always been a considerable subject for many scholars and practitioners. We establish a new constraint approach to truncate stock return forecasts. Investors will not believe in extremely positive forecasts, too Another motivation is that it is unlikely to generate extreme return forecasts in the future, investors will pay special attention to the extreme changes of stock return prediction information. Compared with the original model, CT constraint and the CDA strategy can improve the prediction performance of 9 and 10 variables, respectively. When investors use our constraint approach, the higher CER gains can be yielded compared to the unconstraint model among all predictors, which means that eliminating outliers in stock return forecasts can help investors to allocate their assets and create more substantial economic gains.
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