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The Fama–French five-factor model (FF-5) is one of the advancements of capital asset pricing models (CAPM). Along with other FF-Models, it aims to understand companies and portfolios over a period, Analyzing better return capacity over the five factors such as SMB—Business Size, HML-Spread between high and low book to market ratio, RMW- Robustness in operating profitability and CMA-investment style to be conservative or aggressive. FF-5-factor regression model widely uses Ordinary Least Squares estimator to estimate the parameter. However, due to the volatility of the markets over the years and not-normal periods, OLS estimators face setbacks due to the assumption violations that are a pre-requisite. This article presents an effort made to improve the performance of the FF-5-factor model using the Robust Dawoud-Kibria estimator. The performance of the FF-5-factor model is compared with other robust estimators such as M, MM, and MMS with MSE criteria.

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Robust Regression Approaches for the Fama–French 5-Factor Model: A Real Data Study

  • 会议论文

  • 发表日期：

    2025年

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  • A. Mayuri,
  • Hemlata Joshi

摘要

The Fama–French five-factor model (FF-5) is one of the advancements of capital asset pricing models (CAPM). Along with other FF-Models, it aims to understand companies and portfolios over a period, Analyzing better return capacity over the five factors such as SMB—Business Size, HML-Spread between high and low book to market ratio, RMW- Robustness in operating profitability and CMA-investment style to be conservative or aggressive. FF-5-factor regression model widely uses Ordinary Least Squares estimator to estimate the parameter. However, due to the volatility of the markets over the years and not-normal periods, OLS estimators face setbacks due to the assumption violations that are a pre-requisite. This article presents an effort made to improve the performance of the FF-5-factor model using the Robust Dawoud-Kibria estimator. The performance of the FF-5-factor model is compared with other robust estimators such as M, MM, and MMS with MSE criteria.