Paper: arXiv 2409.17182

Authors: Xialu Liu, John Guerard, Rong Chen, Ruey Tsay

Abstract

Searching for new effective risk factors on stock returns is an important research topic in asset pricing. Factor modeling is an active research topic in statistics and econometrics, with many new advances. However, these new methods have not been fully utilized in asset pricing application. In this paper, we adopt the factor models, especially matrix factor models in various forms, to construct new statistical factors that explain the variation of stock returns. Furthermore, we evaluate the contribution of these statistical factors beyond the existing factors available in the asset pricing literature. To demonstrate the power of the new factors, U.S. monthly stock data are analyzed and the partial F test and double selection LASSO method are conducted. The results show that the new statistical factors bring additional information and add explanatory power in asset pricing. Our method opens a new direction for portfolio managers to seek additional risk factors to improve the estimation of portfolio returns.

Complexity vs Empirical Score

  • Math Complexity: 7.0/10
  • Empirical Rigor: 7.5/10
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper bridges modern multivariate matrix factor modeling and econometric double-selection LASSO techniques with asset pricing factor evaluation. It provides robust statistical testing (partial F-tests, DS LASSO) on U.S. stock return data to demonstrate incremental explanatory power over standard Fama-French benchmarks.

Research Flowchart

  flowchart TD
    A[Research Goal: Improve Portfolio Risk Estimation & Asset Pricing] --> B[Data Input: U.S. Monthly Stock Returns]
    B --> C[Construct New Statistical Factors via Matrix Factor Models]
    C --> D[Evaluate Factors vs. Existing Literature Models]
    D --> E[Statistical Analysis: Partial F-Test & Double Selection LASSO]
    E --> F[Findings: New Factors Add Explanatory Power & Reduce Risk Estimation Error]

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1 of 70 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.