Abstract

We use the cross-section regression approach of Fama and MacBeth (1973) to construct cross-section factors corresponding to the time-series factors of Fama and French (2015). Time-series models that use only cross-section factors provide better descriptions of average returns than time-series models that use time-series factors. This is true when we impose constant factor loadings and when we use time-varying loadings that are natural for time-series factors and time-varying loadings that are natural for cross-section factors.

Scientific Portfolio AI- Generated Summary

In their paper “Comparing Cross-Section and Time-Series Factor Models,” Eugene F. Fama and Kenneth R. French compare the performance of cross-section and time-series factor models in asset pricing. They use the cross-section regression approach to construct cross-section factors corresponding to time-series factors and compare the two models.

The authors find that the cross-section model outperforms the time-series model in explaining the cross-section of average returns. They also find that the time-series model has a higher explanatory power for the time-series variation in average returns.

The authors conclude that the cross-section model is more appropriate for asset pricing studies that focus on the cross-section of average returns, while the time-series model is more appropriate for studies that focus on the time-series variation in average returns. They also suggest that researchers should use both models to gain a more complete understanding of asset pricing.

The paper has important implications for asset pricing models. The authors argue that the cross-section model is more consistent with the efficient market hypothesis, which suggests that asset prices reflect all available information. The time-series model, on the other hand, is more consistent with behavioral finance theories, which suggest that asset prices are influenced by investor sentiment and other psychological factors.

The authors also suggest that the cross-section model is more appropriate for testing asset pricing theories that focus on risk factors, while the time-series model is more appropriate for testing theories that focus on behavioral factors. They argue that researchers should be careful to choose the appropriate model for their research question and to interpret their results accordingly.

Overall, the paper provides a valuable contribution to the literature on asset pricing models. By comparing the performance of cross-section and time-series factor models, the authors provide insights into the strengths and weaknesses of each approach. They also highlight the importance of using both models to gain a more complete understanding of asset pricing.