Robo-Advising: Less AI and More XAI? Augmenting Algorithms with Humans-in-the-Loop

Milo Bianchi and Marie Brière
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Marie Brière: Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres

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Abstract: Page de couverture- Leveraging the research efforts of more than sixty experts in the area, this book reviews cutting-edge practices in machine learning for financial markets. Instead of seeing machine learning as a new field, the authors explore the connection between knowledge developed by quantitative finance over the past forty years and techniques generated by the current revolution driven by data sciences and artificial intelligence. The text is structured around three main areas: 'Interactions with investors and asset owners,' which covers robo-advisors and price formation; 'Risk intermediation,' which discusses derivative hedging, portfolio construction, and machine learning for dynamic optimization; and 'Connections with the real economy,' which explores nowcasting, alternative data, and ethics of algorithms. Accessible to a wide audience, this invaluable resource will allow practitioners to include machine learning driven techniques in their day-to-day quantitative practices, while students will build intuition and come to appreciate the technical tools and motivation for the theory.

Date: 2023-08-10
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Published in Machine Learning and Data Sciences for Financial Markets, Cambridge University Press, pp.33-59, 2023, 978 1 0090 2894 3. ⟨10.1017/9781009028943⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04373576

DOI: 10.1017/9781009028943

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