This paper surveys the research on the influence of investor feelings on equity pricing and also develops a theoretical basis with which to understand the emerging findings of this area. The theoretical basis is developed with reference to research in the fields of economic psychology and decision-making. Recent advancements in understanding how feelings affect the general decision-making of individuals, especially under conditions of risk and uncertainty [e.g. Loewenstein et al. (2001). Psychological Bulletin 127: 267-286], are covered by the review. The theoretical basis is applied to analyze the existing research on investor feelings [e.g. Kamstra et al. (2000). American Economic Review (forthcoming); Hirshleifer and Shumway (2003). Journal of Finance 58 (3): 1009-1032]. This research can be broadly described as investigating whether variations in feelings that are widely experienced by people influence investor decision-making and, consequently, lead to predictable patterns in equity pricing. The paper concludes by suggesting a number of directions for future empirical and theoretical research.

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We explore how machine learning and artificial intelligence (AI) solutions are transforming risk management. A non-technical overview is first given of the main machine learning and AI techniques of benefit to risk management. Then a review is provided, using current practice and empirical evidence, of the application of these techniques to the risk management fields of credit risk, market risk, operational risk, and compliance ('RegTech'). We conclude with some thoughts on current limitations and views on how the field is likely to develop in the short-to medium-term. Overall, we present an optimistic picture of the role of machine learning and AI in risk management, but note some practical limitations around suitable data management policies, transparency, and lack of necessary skillsets within firms.