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New Evidence on Conditional Factor Models
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DOI：10.1017/S0022109018001606.png)
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Abstract
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We estimate conditional multifactor models over a large cross section of stock returns matching 25 CAPM anomalies. Using conditioning information associated with different instruments improves the performance of the Hou, Xue, and Zhang (HXZ) (2015) and Fama and French (FF) (2015), (2016) models. The largest increase in performance holds for momentum, investment, and intangibles-based anomalies. Yet, there are significant differences in the performance of scaled models: HXZ clearly dominates FF in explaining momentum and profitability anomalies, while the converse holds for value-growth anomalies. Thus, the asset pricing implications of alternative investment and profitability factors (in a conditional setting) differ in a nontrivial way.
Keywords:
ASSET PRICING-MODELS
CROSS-SECTIONAL TEST
STOCK RETURNS
DIVIDEND YIELDS
INVESTMENT
RISK
INFORMATION
MOMENTUM
GROWTH
TESTS
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JOURNAL OF FINANCEOAAI
JOURNAL OF FINANCEIF9.5
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