Attribution Methods in Asset Pricing: Do They Account for Risk?
This work addresses the need for risk-aware attribution in finance, but it is incremental as it builds on existing methods by incorporating domain-specific axioms without introducing a new paradigm.
The study tackled the problem of ensuring attribution methods in machine learning accurately reflect underlying risks in asset pricing, showing that while Shapley value and Integrated Gradients preserve most axioms derived from domain knowledge, neither satisfies all axioms.
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.