Next-Generation Personalized Investment Recommendations

McCreadie, R. , Perakis, K., Srikrishna, M., Droukas, N., Pitsios, S., Prokopaki, G., Perdikouri, E., Macdonald, C. and Ounis, I. (2022) Next-Generation Personalized Investment Recommendations. In: Soldatos, J. and Kyriazis, D. (eds.) Big Data and Artificial Intelligence in Digital Finance: Increasing Personalization and Trust in Digital Finance using Big Data and AI. Springer: Cham, pp. 171-198. ISBN 9783030945893 (doi: 10.1007/978-3-030-94590-9_10)

Abstract

Recent advances in Big Data and Artificial Intelligence have created new opportunities for AI-based agents, referred to as Robo-Advisors, to provide financial advice and recommendations to investors. In this chapter, we will introduce the concept of investment recommendation and describe how automated services for this task can be developed and tested. In particular, this chapter covers the following core topics: (1) the legal landscape for investment recommendation systems, (2) what financial asset recommendation is and what data it needs to function, (3) how to clean and curate that data, (4) approaches to build/train asset recommendation models and (5) how to evaluate such systems prior to putting them into production.

• Item Type: Book Sections
• Status: Published
• Glasgow Author(s) Enlighten ID: Macdonald, Professor Craig and Srikrishna, Ms Maanasa and Ounis, Professor Iadh and Mccreadie, Dr Richard
• Authors: McCreadie, R., Perakis, K., Srikrishna, M., Droukas, N., Pitsios, S., Prokopaki, G., Perdikouri, E., Macdonald, C., and Ounis, I.
• College/School: College of Science and Engineering > School of Computing Science
• Publisher: Springer
• ISBN: 9783030945893
• Published Online: 24 December 2021
• Copyright Holders: Copyright © The Author(s) 2022
• First Published: First published in Big Data and Artificial Intelligence in Digital Finance: Increasing Personalization and Trust in Digital Finance using Big Data and AI: 171-198
• Publisher Policy: Reproduced under a Creative Commons License