Robo-Advisors: A Big Data Challenge

Federico Severino () and Sébastien Thierry ()
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Federico Severino: Université Laval
Sébastien Thierry: Université Laval

A chapter in Big Data in Finance, 2022, pp 115-131 from Springer

Abstract: Abstract At the frontier of personal finance and FinTech, robo-advisors aim to provide customized portfolio strategies without human intervention. These new investment technologies typically propose passive strategies that match investor objectives and risk profiles at a low cost. This chapter explores how digital advisors lack precision in capturing clients’ attitudes towards risk and exposure. In this context, leveraging big data and artificial intelligence techniques can improve the principal strength of robo-advisors, i.e., their ability to provide automated, personalized investment solutions. Text data from dialogue systems, such as chatbots, can be employed to improve the client profile, while recommendation systems can use big data from financial social networks to recommend targeted investment strategies. Analysis of big data through machine learning methods can also improve the performance of the optimization algorithms employed by digital advisors. This chapter explores the vast potential for exploiting big data and artificial intelligence in automated asset management.

Keywords: Robo-advisors; FinTech; Portfolio management; Big data; Artificial intelligence (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-12240-8_7

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DOI: 10.1007/978-3-031-12240-8_7

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