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High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model

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  • Liao Zhu
  • Sumanta Basu
  • Robert A. Jarrow
  • Martin T. Wells

Abstract

The paper proposes a new algorithm for the high-dimensional financial data -- the Groupwise Interpretable Basis Selection (GIBS) algorithm, to estimate a new Adaptive Multi-Factor (AMF) asset pricing model, implied by the recently developed Generalized Arbitrage Pricing Theory, which relaxes the convention that the number of risk-factors is small. We first obtain an adaptive collection of basis assets and then simultaneously test which basis assets correspond to which securities, using high-dimensional methods. The AMF model, along with the GIBS algorithm, is shown to have a significantly better fitting and prediction power than the Fama-French 5-factor model.

Suggested Citation

  • Liao Zhu & Sumanta Basu & Robert A. Jarrow & Martin T. Wells, 2018. "High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model," Papers 1804.08472, arXiv.org, revised Dec 2021.
  • Handle: RePEc:arx:papers:1804.08472
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    Cited by:

    1. Liao Zhu & Haoxuan Wu & Martin T. Wells, 2021. "A News-based Machine Learning Model for Adaptive Asset Pricing," Papers 2106.07103, arXiv.org.
    2. Liao Zhu, 2021. "The Adaptive Multi-Factor Model and the Financial Market," Papers 2107.14410, arXiv.org, revised Aug 2021.
    3. Liao Zhu & Ningning Sun & Martin T. Wells, 2021. "Clustering Structure of Microstructure Measures," Papers 2107.02283, arXiv.org, revised Dec 2021.
    4. Liao Zhu & Ningning Sun & Martin T. Wells, 2022. "Clustering Structure of Microstructure Measures," Applied Economics and Finance, Redfame publishing, vol. 9(1), pages 85-95, December.

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