Abstract
Predicting stock returns has been instrumental in our understanding of capital market structure. The validity of models, like the Capital Asset Pricing Model or the Gordon Growth Model, has influenced and contributed to building mathematical representations in predicting required return. Several studies attempted to explore different variables to determine the explanatory power of proxies in predicting stock return. For example, it is reported that dividends can explain up to 25% of the variance in returns. The explanatory power of dividends in the regression analysis showed a significant variation when the analysis follows time-series methodology. This study aims at examining the predicting power in the U.K. equity market by plugging into the regression model some of the variables conventionally measured in the Structural Equation Modeling. The study is quantitative and uses secondary data. The findings of this study suggest that the selected proxies, dividend growth, earnings per share, and beta exhibit weak explanatory power in predicting returns of large U.K. companies.
Highlights
The paper contributes to on-going debate on the possibility of predicting stock returns
The motivation of this research comes from the understanding developed on existing literature evidence that it would be highly useful and relevant to examine the returns predictability in the U.K. equity market by plugging into the regression some of the variables conventionally measured in the Structural Equation Modeling (SEM)
The study is the first attempt to examine this for large UK companies by not performing the Structural Equation Modelling but plugging into simple and multiple regression analysis some the latent variables conventionally used in the SEM
Summary
The paper contributes to on-going debate on the possibility of predicting stock returns. The research area is rich and there exists a voluminous literature on it. The motivation of this research comes from the understanding developed on existing literature evidence that it would be highly useful and relevant to examine the returns predictability in the U.K. equity market by plugging into the regression some of the variables conventionally measured in the Structural Equation Modeling (SEM). The study is the first attempt to examine this for large UK companies by not performing the Structural Equation Modelling but plugging into simple and multiple regression analysis some the latent variables conventionally used in the SEM. The research framework and findings are likely to be of interest and will encourage further investigation into plugging together isolated indicators to determine proxy power of predicting stock returns
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