Predicting Returns for Growth and Value Stocks: A Forecast Assessment Approach Using Global Asset Pricing Models

Shailesh Rana, William H. Bommer and G. Michael Phillips
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Shailesh Rana: Craig School of Business, California State University, Fresno, 5245 N. Backer Ave M/S PB 5 Fresno, California, 93740, USA,
William H. Bommer: Craig School of Business, California State University, Fresno, 5245 N. Backer Ave M/S PB 5 Fresno, California, 93740, USA,
G. Michael Phillips: David Nazarian College of Business and Economics, California State University, Northridge, 18111 Nordhoff St., Northridge, California, 91330, USA.

International Journal of Economics and Financial Issues, 2020, vol. 10, issue 4, 88-106

Abstract: The present study tests the forecasting strength of widely used asset pricing models, using monthly stock returns of two style-based, large-cap US growth and value index funds for 1993 2015. Global variables are added to the models to test the global linkage impact. As we impose a positive forecast returns constraint, there is a considerable reduction in the root mean squared error (RMSE), providing significant economic implications. RMSE of constrained models for non-negativity restriction outperforms the unconstrained models improving them by an average of 17%. As evidenced by the forecasting power measured by RMSE, we found the value stocks to be more predictable with lower overall RMSE when compared to growth stocks. The global models provide better forecast for growth stocks, whereas there are mixed implications for value stocks. The Global Carhart consistently ranks as one of the best models for both growth and value stocks.

Keywords: Forecasting Stock Returns; International Asset Pricing; Global Linkage; Growth Versus Value; Predictive Regressions; Root Mean Squared Error (search for similar items in EconPapers)
JEL-codes: G11 G15 G17 (search for similar items in EconPapers)
Date: 2020
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