  Over the past few decades, machine learning models have been extremely
successful. As a result of axiomatic attribution methods, feature contributions
have been explained more clearly and rigorously. There are, however, few
studies that have examined domain knowledge in conjunction with the axioms. In
this study, we examine asset pricing in finance, a field closely related to
risk management. Consequently, when applying machine learning models, we must
ensure that the attribution methods reflect the underlying risks accurately. In
this work, we present and study several axioms derived from asset pricing
domain knowledge. It is shown that while Shapley value and Integrated Gradients
preserve most axioms, neither can satisfy all axioms. Using extensive
analytical and empirical examples, we demonstrate how attribution methods can
reflect risks and when they should not be used.
