We survey recent methodological contributions in asset pricing using factor models and
machine learning. We organize these results based on their primary objectives: estimating …

Standard estimators of risk premia in linear asset pricing models are biased if some priced
factors are omitted. We propose a three-pass method to estimate the risk premium of an …

Over the years, many asset pricing studies have employed the sample cross‐sectional
regression (CSR) R2 as a measure of model performance. We derive the asymptotic …

The asset pricing literature has produced hundreds of potential risk factors. Organizing this
“zoo of factors” and distinguishing between useful, useless, and redundant factors require …

The reliability of traditional asset pricing tests depends on:(i) the correlations between asset
returns and factors;(ii) the time series sample size T compared to the number of assets N …

Landslide susceptibility map is considered as one of the important steps in assessing
vulnerability of an area to landslide hazard. In this study, the main objective is to propose …

This paper shows that in misspecified models with risk factors that are uncorrelated with the
test asset returns, the conventional inference methods tend to erroneously conclude, with …

We compare major factor models and find that the Stambaugh and Yuan (2016) 4-factor
model is the overall winner in the time-series domain. The Hou, Xue, and Zhang (2015) q …

When excess returns are used to estimate linear stochastic discount factor (SDF) models,
researchers often adopt a normalization of the SDF that sets its mean to 1, or one that sets its …

Intermediary and downside risk asset pricing theories lay the foundations for spanning the
multi-asset return space by a small number of risk factors. Recent studies show strong …