Flexible Optimal Models for Predicting Stock Market Returns

The International Journal of Business and Finance Research, v. 12 (2) p. 39-48

23 Pages Posted: 25 Feb 2019

See all articles by Jin-Gil Jeong

Jin-Gil Jeong

Howard University - Howard University Hospital

Sandip Mukherji

Howard University - School of Business

Date Written: 2018

Abstract

This study assesses the usefulness of flexible optimal models of business cycle variables for predicting stock market returns. We find that variable estimation periods identify structural breaks in months with large absolute returns and the optimal models recognize regime switches. Flexible optimal models have much greater predictive power for stock market returns than fixed univariate or multivariate models. The dividend yield has consistent predictive power for stock market returns, but different variables make significant contributions to predicting stock market returns in different periods. These findings highlight the importance of employing flexible optimal models to consistently predict stock market returns.

Keywords: Predicting Stock Returns, Optimal Models, Business Cycles, Dividend Yield

JEL Classification: G11, G12

Suggested Citation

Jeong, Jin-Gil and Mukherji, Sandip, Flexible Optimal Models for Predicting Stock Market Returns (2018). The International Journal of Business and Finance Research, v. 12 (2) p. 39-48, Available at SSRN: https://ssrn.com/abstract=3241679

Jin-Gil Jeong (Contact Author)

Howard University - Howard University Hospital ( email )

2041 Georgia Avenue
Washington, DC 20060
United States

Sandip Mukherji

Howard University - School of Business ( email )

2600 Sixth Street, NW
Washington, DC 20059
United States
202-806-1591 (Phone)

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