We formalize "degrees of fungibility" by differentiating goods according to both their underlying attributes and the perceived value and/or usefulness of those attributes to a value assessor. This allows us to distinguish between goods that appear to be "exactly the same" from those goods that...

-- 1 Financial Crime and The Law: Identifying and Mitigating Risks. -- 2 The Crime-Crypto Nexus: Nuancing Risk across Crypto-Crime Transactions. -- 3 Financing Environmental Crime: Financial Sector Complicity in Global Deforestation and Opportunities for Regulatory Intervention. -- 4 Weeding Out...

The nature of learning processes as well as evolutionary considerations suggest that aesthetic judgement is of central importance in the formation of custom. Learning and extrapolation rely on evaluations of non-instrumental features like simplicity, analogy, straightforwardness, and clarity....

Most modern supervised statistical/machine learning (ML) methods are explicitly designed to solve prediction problems very well. Achieving this goal does not imply that these methods automatically deliver good estimators of causal parameters. Examples of such parameters include individual...

Long short-term memory (LSTM) networks are a state-of-the-art technique for sequence learning. They are less commonly applied to financial time series predictions, yet inherently suitable for this domain. We deploy LSTM networks for predicting out-of-sample directional movements for the...

In recent years, machine learning research has gained momentum: New developments in the field of deep learning allow for multiple levels of abstraction and are starting to supersede well-known and powerful tree-based techniques mainly operating on the original feature space. All these methods...