The paper proposes a new high-dimensional algorithm , 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 . Since the collection of basis assets is quite large and highly correlated, high-dimension methodsare used . The AMF model  along with the GIBS algorithm  is shown to have a significantly better fit 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.
R
high-dimensional algorithm , the
algorithm for the high-dimensional financial data -- the
A
,
R
. Since the collection of basis assets is quite large and highly correlated, high-dimension methodsare used
, using high-dimensional methods
A
,
A
,
R
fit
fitting and prediction power