Explanatory Power of Selected Proxies in Predicting Stock Returns of Large U.K. Companies

  •  Daniel F. Rodriguez    
  •  Ali Malik    
  •  Nasser I. Abumustafa    
  •  Arshad Jamal    


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.

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