Do Fama–French common risk-factor portfolio investors herd on a daily basis? Implications for common risk-factor regressions

Abstract

Purpose The purpose of this paper is to examine whether Fama–French common risk-factor portfolio investors herd on a daily basis for five developed markets, namely, Europe, Japan, Asia Pacific ex Japan, North America and Globe. Design/methodology/approach To examine the herd behavior of common risk-factor portfolio investors, this paper utilizes the cross-sectional absolute deviations (CSAD) methodology, covering a daily data sampling period of July 1990 to January 2019 from Kenneth R. French-Data Library. CSAD driven by fundamental and non-fundamental information is assessed using Fama–French five-factor model. Findings The results do not provide evidence for herding under normal market conditions, either when reacting to fundamental information or non-fundamental information, for any region under consideration. However, Fama–French common risk-factor portfolio investors mimic the underlying risk factors in returns related to size and book-to-market value, size and operating profitability, size and investment and size and momentum of the equity stocks in European and Japanese markets during crisis period. Also, no considerable evidence is found for herding (on fundamental information) under crisis and up-market conditions except for Japan. Ancillary findings are discussed under conclusion. Research limitations/implications Further research on new risk factors explaining stock return variation may help improve the model performance. The performance can be improved by adding new risk factors that are free from behavioral bias but significant in explaining common stock return variation. Also, it is necessary to revisit the existing common risk factors in order to understand behavioral aspects that may affect cost of capital calculations (e.g. pricing errors) and valuation of investment portfolios. Originality/value This is the first paper that examines the herd behavior (fundamental and non-fundamental) of Fama–French common risk-factor investors using five-factor model.

Predicting Optimal Portfolio by Algorithm Analysis Systems

“…Post-Markowitz research has shown that the use of variance as a risk factor has shortcomings that cause the traditional tools in forecasting to be criticized (Raei et al, 2020). Studies such as Herberger and Reinle (2020); Senarathne (2019); Gupta et al (2014), and Ince and Trafalias (2007) state that using traditional forecasting tools and methods has a high error rate and they have poorer performance compared to newer methods and nonlinear models. In this study, one of the artificial intelligence methods called support vector machine, along with one of the most widely used algorithms in this field, the adaptive neural fuzzy inference system, are examined to predict the most desirable stock portfolio because the goal is to choose the appropriate forecasting method in selecting the desired portfolio.…”