Winners from Winners: A Tale of Risk Factors

US efficient factors in a Bayesian model scan framework

“…Assuming that the factor data follows a multivariate normal distribution, Chib et al (2022) show that the log ML of each model can be solved analytically in Proposition 5 of their paper. Chib et al (2022) apply the results of to evaluate model comparison tests of all models that can comprise the factor models of Fama and French (2018), Hou et al (2015), Stambaugh and Yuan (2017), and Daniel et al (2020), which they identify as winner factor models given their performance in historical tests of model comparison. There are J 5 2 KþL -1 potential factor models that can be constructed and is defined as a model scan.…”

“…The attraction of their approach is that the ML can be solved analytically as relies on multivariate normality. Chib et al (2022) use this approach to run a model scan for comparing models in a set of 12 U.S factor returns.…”

“…We use the model scan approach building on Chib et al (2022) to examine the performance of different models that can be formed from 13 investment factors and their efficient counterparts over the sample period is between July 1972 and December 2022. We use the first 10% of the sample as a training sample as in Chib et al (2022) to estimate the hyperparameters for the prior distribution, and we use the remaining 90% to conduct the model comparison tests. We also compare Student-t distributed factor models with Gaussian distributed factor models over our period of analysis.…”

“…First, we are the only authors to examine if the efficient factors developed by Ehsani and Linnainmaa (2022) have an impact on model comparison tests in U.S stock returns. Second, we complement the Bayesian model scan studies of Barillas and Shanken (2018), Chib et al (2021a, b), andChib et al (2022) in U.S. stock returns by conducting a model scan on an extended group of factors. Third, as well as conducting a model scan across the whole sample period, we also consider whether the best factor model in more recent data is different from the best factor model using all data using the approach of Chib et al (2021b).…”

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Purpose -The author examines the impact these efficient factors have on factor model comparison tests in US returns using the Bayesian model scan approach of Chib et al(2022). Design/methodology/approach -Ehsani and Linnainmaa (2022) show that time-series efficient investment factors in US stock returns span and earn 40% higher Sharpe ratios than the original factors.

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