Modeling the cross-section of stock returns using sensible models in a model pool

I-Hsuan Ethan Chiang, Yin Liao, Qing Zhou

Research output: Contribution to journalArticlepeer-review

Abstract

An increase in the number of asset pricing models intensifies model uncertainties in asset pricing. While a pure “model selection” (singling out a best model) can result in a loss of useful information, a full “model pooling” may increase the risk of including noisy information. We make a trade-off between the two methods and develop a new two-step trimming-then-pooling method to forecast the joint distributions of asset returns using a large pool of asset pricing models. Our method allows investors to focus on certain regions of the distributions. In the first step, we trim the uninformative models from a pool of candidates, and in the second step, we pool the forecasts of the surviving models. We find that our method significantly enhances portfolio performance and predicts downside risk precisely, and the improvements are mainly due to trimming. The pool of sensible models becomes larger when focusing on extreme events, responds rapidly to rising uncertainty, and reflects the magnitude of factor premiums. These findings provide new insights into asset pricing model evaluation.
Original languageEnglish
Pages (from-to)56-73
Number of pages18
JournalJournal of Empirical Finance
Volume60
Early online date27 Nov 2020
DOIs
Publication statusPublished - 1 Jan 2021

Keywords

  • Asset pricing model
  • Joint density forecast
  • Model confidence set
  • Model pooling
  • Model selection
  • Model uncertainty

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