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This article examines the issue of stock returns forecasting and in particular extends the analysis of the recently introduced sum of the parts modelling technique. The sum of the parts technique undertakes a first-stage regression analysis where the predictor variables themselves are estimated and the fitted values from these equations are then used in the forecast model. We conduct a series of one-step ahead recursive forecasts using the above methodology and compare that to the usual predictive regression approach for 11 markets, and a variety of forecast metrics and tests. Across the full range of markets and forecast measures, our results suggest that no single model dominates. Notably, while the sum of the parts approach often reports a lower Mean Absolute Error (MAE) and Root Mean-Squared Error (RMSE), it is rarely significantly lower than competing forecasts. Similar results are found on the basis of both regression and sign based tests. Thus, across the range of markets the new approach meets with only limited success in providing better forecasts, although it rarely performs significantly worse. Furthermore, in specific markets, the sum of the parts approach does perform well. Notably for Italy, the UK, US and Korea, this approach outperforms the alternate models on all or nearly all measures. Thus, in terms of guiding researchers on the appropriate forecast model, the sum of the parts approach is interesting and does suggest some forecast improvement. However, that is only for specific markets. Hence, in choosing which forecast method to adopt there remains the trade-off between the simplicity of the predictive regression approach and the sum of the parts approach, which is more involved but on occasion more accurate, although not universally so.
