Comparing Asset Pricing Models Research Articles

Model scan of factors in U.K. stock returns

We use the Bayesian model scan approach of Chib, S., X. Zeng, and L. Zhao. 2020. ‘On Comparing Asset Pricing Models.’ The Journal of Finance 75 (1): 551–577. https://doi.org/10.1111/jofi.12854, and Chib, S., L. Zhao, and G. Zhou. 2023. ‘Winners from Winners: A Tale of Risk Factors.’ Management Science to examine which are the best performing models in a set of 12 candidate factors in U.K. stock returns. We find that a five-factor model has the highest posterior probability across the whole sample period but the posterior probability is low. The best factor model outperforms traditional factor models using a number of metrics. However the best model performs poorly in pricing a set of anomaly portfolios.

Comparing Asset Pricing Models

Comparing Asset Pricing Models

What Explains Price Momentum and 52-Week High Momentum When They Really Work?

After long being one of the main puzzles in asset pricing, momentum has ironically become a case of observational equivalence. It can now be explained both by factors proxying for mispricing and by the investment CAPM. On top of this, q-factor theory also explains the related 52-week-high anomaly. We note that all these recent tests are unconditional exercises while the bulk of momentum profits are predictable and occur after periods of low-volatility. Comparing asset pricing models conditionally, when the strategies actually work, we find the unconditional fit is misleading. The models fit well most of the time but not when the profits are produced. The investment CAPM implies time-varying loadings that are inconsistent with the data. We proxy underreaction with earnings announcement returns and analyst forecast errors and find that it markedly decreases with volatility. This supports an underreaction channel as closer to the heart of both anomalies.

Comparing Asset Pricing Models with Non-Traded Factors and Principal Components

This paper develops a Bayesian methodology to compare asset pricing models containing non-traded factors and principal components. Existing comparison procedures are inadequate when models include such factors due to estimation uncertainties in mimicking portfolios and return covariances. Furthermore, regressions of test assets on such factors are interdependent, rendering comparisons with recently proposed priors sensitive to subsets of the test assets. Thus, I derive novel, non-informative priors that deliver invariant inferences. Simulations suggest that my methodology substantially outperforms existing methods in identifying true non-traded models. I find that macroeconomic models dominate several, recent benchmark models with traded factors and principal components.

On Comparing Asset Pricing Models

ABSTRACTRevisiting the framework of (Barillas, Francisco, and Jay Shanken, 2018, Comparing asset pricing models, The Journal of Finance 73, 715–754). BS henceforth, we show that the Bayesian marginal likelihood‐based model comparison method in that paper is unsound : the priors on the nuisance parameters across models must satisfy a change of variable property for densities that is violated by the Jeffreys priors used in the BS method. Extensive simulation exercises confirm that the BS method performs unsatisfactorily. We derive a new class of improper priors on the nuisance parameters, starting from a single improper prior, which leads to valid marginal likelihoods and model comparisons. The performance of our marginal likelihoods is significantly better, allowing for reliable Bayesian work on which factors are risk factors in asset pricing models.

Comparing Asset Pricing Models by the Conditional Hansen-Jagannathan Distance*

AbstractWe compare nonnested parametric specifications of the stochastic discount factor (SDF) using the conditional Hansen–Jagannathan (HJ-) distance. This distance measures the discrepancy between a parametric model-implied SDF and the admissible SDF’s satisfying all the conditional (dynamic) no-arbitrage restrictions, instead of just few unconditional no-arbitrage restrictions for managed portfolios chosen through the instrument selection. We estimate the conditional HJ-distance by a generalized method of moments estimator and establish its large sample properties for model selection purposes. We compare empirically several SDF models including multifactor beta pricing specifications and some recently proposed SDF models that are conditionally linear in consumption growth.

Comparing Asset Pricing Models

ABSTRACTA Bayesian asset pricing test is derived that is easily computed in closed form from the standard F‐statistic. Given a set of candidate traded factors, we develop a related test procedure that permits the computation of model probabilities for the collection of all possible pricing models that are based on subsets of the given factors. We find that the recent models of Hou, Xue, and Zhang (2015a, 2015b) and Fama and French (2015, 2016) are dominated by a variety of models that include a momentum factor, along with value and profitability factors that are updated monthly.

Comparing Asset Pricing Models by the Conditional Hansen-Jagannathan Distance

Comparing Asset Pricing Models by the Conditional Hansen-Jagannathan Distance

Comparing Asset Pricing Models: Distance-Based Metrics and Bayesian Interpretations

In light of the power problems of statistical tests and undisciplined use of alpha-based statistics to compare models, this paper proposes a unified set of distance-based performance metrics, derived as the square root of the sum of squared alphas and squared standard errors. The Bayesian investor views model performance as the shortest distance between his dogmatic belief (model-implied distribution) and complete skepticism (data-based distribution) in the model, and favors models that produce low dispersion of alphas with high explanatory power. In this view, the momentum factor is a crucial addition to the five-factor model of Fama and French (2015), alleviating his prior concern of model mispricing by -8% to 8% per annum. The distance metrics complement the frequentist p-values with a diagnostic tool to guard against bad models.

Comparing Asset Pricing Models

A Bayesian asset-pricing test is derived that is easily computed in closed-form from the standard F-statistic. Given a set of candidate traded factors, we develop a related test procedure that permits an analysis of model comparison, i.e., the computation of model probabilities for the collection of all possible pricing models that are based on subsets of the given factors. We find that the recent models of Hou, Xue and Zhang (2015a,b) and Fama and French (2015, 2016) are both dominated by a variety of models that include a momentum factor, along with value and profitability factors that are updated monthly.

Comparing Asset Pricing Models by the Conditional Hansen-Jagannathan Distance

Comparing Asset Pricing Models by the Conditional Hansen-Jagannathan Distance

Comparing Asset Pricing Models: What Does the Hansen-Jagannathan Distance Tell Us?

Comparing Asset Pricing Models: What Does the Hansen-Jagannathan Distance Tell Us?

Comparing asset pricing models: an investment perspective

We investigate the portfolio choices of mean-variance-optimizing investors who use sample evidence to update prior beliefs centered on either risk-based or characteristic-based pricing models. With dogmatic beliefs in such models and an unconstrained ratio of position size to capital, optimal portfolios can differ across models to economically significant degrees. The differences are substantially reduced by modest uncertainty about the models’ pricing abilities. When the ratio of position size to capital is subject to realistic constraints, the differences in portfolios across models become even less important and are nonexistent in some cases.