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Martin, R.D., Guerard, J.B., and Xia, D.Z. (2024) Resurrecting Earnings-to-Price with Machine Learning Robust Control for Outliers.
https://ssrn.com/abstract=4746580

has been cited by the following article:

• TITLE: Earnings to Price Analysis with mOpt versus Bisquare Robust Regression

  AUTHORS: Richard Douglas Martin, John B. Guerard, Daniel Z. Xia

  KEYWORDS: Robust Regression, Cross-Section Factor Models, EP Factor, Outliers, Bias

  JOURNAL NAME: Journal of Mathematical Finance, Vol.14 No.2, May 31, 2024

  ABSTRACT: Recently Martin, Guerard, and Xia [1] used a new optimal bias robust regression estimator, called the mOpt estimator, in Fama-MacBeth cross-section regressions to study the statistical significance of the earnings-to-price (EP) and book-tp-price (BP) factors, among others. An earlier study by Markowitz et al. [2], and a number of studies referenced therein, used an alternative well-known Tukey Bisquare robust regression estimator. This begs the question of how the Bisquare estimator fares relative to the mOpt robust regression with regard to determining the statistical significance of the EP and BP factors. Here we show that the Bisquare robust regression estimator performs almost as well as mOpt with regard to the size of their significant t-statistics.