Analysis demonstrates the modified Fama-MacBeth model improves predictive power for excess stock returns, highlighting significant factors affecting portfolio performance.
The aim of this essay is to use a single-index model in developing and adjusting Fama-MacBeth. Penalized smoothing spline regression technique (SIMPLS) foresaw this adjustment. Two generalized cross-validation techniques, Generalized Cross Validation Grid (GGCV) and Generalized Cross Validation Fast (FGCV), anticipated the regular value of smoothing covered under this technique. Due to the two-steps nature of the Fama-MacBeth model, this estimation generated four estimates: SIMPLS(FGCV) - SIMPLS(FGCV), SIMPLS(FGCV) - SIM PLS(GGCV), SIMPLS(GGCV) - SIMPLS(FGCV), SIM PLS(GGCV) - SIM PLS(GGCV). Three-factor Fama-French model—market risk premium, size factor, value factor, and their implication for excess stock returns and portfolio returns—were estimated on the Iraqi Stock Exchange using the modified Fama-MacBeth. SIMPLS(FGCV)-GGCV performed best based on the findings. Results also revealed the statistical significance of the three factors of the Fama-French model, which enhanced the explanatory power of the model in terms of the performance of Iraq Stock Exchange
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Mousa et al. (2025) studied this question.
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