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.
References
[1]
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
[2]
Markowitz, H., et al. (2021) Financial Anomalies in Portfolio Construction and Management. The Journal of Portfolio Management, 47, 51-64. https://doi.org/10.3905/jpm.2021.1.242
[3]
Yohai, V.J. and Zamar, R.H. (1997) Optimally Local Robust M-Estimates of Regression. Journal of Statistical Planning and Inference, 64, 309-323. https://doi.org/10.1016/S0378-3758(97)00040-2
[4]
Konis, K. and Martin, R.D. (2021) Optimal Bias Robust Psi and Rho Revisited. https://ssrn.com/abstract=3902862
[5]
Martin, R.D. and Xia, D.Z. (2022) Efficient Bias Robust Regression for Time Series Factor Models. Journal of Asset Management, 23, 215-234. https://link.springer.com/content/pdf/10.1057/s41260- 022-00258-0.pdf
[6]
Martin, R.D., et al. (2023) Robust Statistics for Portfolio Construction and Analysis. The Journal of Portfolio Management, 49, 105-139. https://doi.org/10.3905/jpm.2023.1.527
[7]
Bali, T.G., Engle, R.F., and Murray, S. (2016) Empirical Asset Pricing: The Cross Section of Stock Returns. John Wiley & Sons, New York. https://doi.org/10.1002/9781118445112.stat07954
[8]
Maronna, R.A., et al. (2019) Robust Statistics: Theory and Methods (with R). 2nd Edition, John Wiley & Sons, New York. https://doi.org/10.1002/9781119214656