High-Dimensional Estimation, Basis Assets, and the Adaptive Multi-Factor Model
This addresses the challenge of modeling complex financial data with many factors for asset pricing, though it appears incremental as it builds on existing theories.
The paper tackles the problem of estimating asset pricing models with many risk-factors in high-dimensional financial data, proposing the GIBS algorithm and AMF model, which achieve significantly better fitting and prediction power than the Fama-French 5-factor model.
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.