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Data-Adaptive Factor Model (DAFM)

Updated 14 July 2026
  • DAFM is a family of factor models where the factor structure is learned from data, adapting to nonstationarity, regime shifts, and nonlinearities.
  • It employs techniques like locally adaptive processes, sparse basis selection, and regime-dependent decoding to overcome rigid, fixed-model assumptions.
  • Applications span multivariate time series, high-dimensional asset pricing, online deep factor models, and robust covariance estimation with scalable inference.

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