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Predicting Stock Return with Economic Constraint: Can Interquartile Range Truncate the Outliers?
Author(s) -
Zhifeng Dai,
Xiaoming Chang
Publication year - 2021
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2021/9911986
Subject(s) - outlier , constraint (computer aided design) , econometrics , robustness (evolution) , computer science , stock market , stock (firearms) , mathematical optimization , economics , mathematics , artificial intelligence , engineering , context (archaeology) , mechanical engineering , paleontology , biochemistry , chemistry , geometry , biology , gene
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.

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