PREDICTING RETURNS FOR GROWTH AND VALUE STOCKS: A FORECAST ASSESSMENT APPROACH USING GLOBAL ASSET PRICING MODELS

Shailesh Rana,William H Bommer,G Michael Phillips

doi:10.32479/ijefi.9993

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 ErrorJEL Classifiations: G170, G150, G110DOI: https://doi.org/10.32479/ijefi.9993

Highlights

Portfolio managers, analysts, and numerous investors all seek tools to predict future stock returns as accurately as possible
This paper provides a new dimension for forecasting US stock returns driven by the motivation of disaggregating the broader US equity market into two prominent categories of growth and value stocks, and assessing predictability of each category, utilizing widely used asset pricing models
We find that the Global Carhart model consistently ranks as the best predictor for both growth and value stocks leading us to believe that it captures relevant risk premiums on US equity markets

Summary

Introduction

Analysts, and numerous investors all seek tools to predict future stock returns as accurately as possible. While it is hardly controversial to suggest that predictability is a common goal, the appropriate tool or tools to use has been a topic of study for generations A number of tools exist, but tests of their accuracy have led researchers to suggest that improvements are needed (e.g. Harvey et al, (2016); Narayan and Liu (2018); Timmermann (2008)). Studies on stock return predictability have generally fallen under one of two different perspectives. The first perspective asserts that historical average returns are the best predictor of future returns and that stock prices are not predictable (Welch and Goyal, 2007). The Efficient Market Hypothesis and the Radom Walk Theory (Fama, 1970) are both supportive of this general idea of unpredictability

Methods

Results

Conclusion