What is it about?
Machine Learning methods have been recently employed in Financial Economics as an alternative to other conventional techniques thanks to their flexibility in high-dimensional settings and their great prediction accuracy. I review and critically asses the most recent contributions in Asset Pricing, summarizing the empirical findings and providing hints forfuture developments.
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Why is it important?
Machine Learning in Asset Pricing represents a very promising and dynamic field that has been growing steadily over the last years. A sound grasp of these methods is imperative for both practitioners and academics willing to keep abreast of the latest frontier of research. This work groups the most recent contrinbutions into 5 categories based on the main approach used, guiding the reader towards a sound economic interpretation of the findings, and points out the econometric challenges one must be wary of when applying Machine Learning in this area.
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This page is a summary of: Asset Pricing and Machine Learning: A critical review, Journal of Economic Surveys, September 2022, Wiley, DOI: 10.1111/joes.12532.
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