DP14525 Artificial Intelligence in Asset Management

Author(s): Söhnke M Bartram, Jürgen Branke, Mehrshad Motahari
Publication Date: March 2020
Date Revised: April 2020
Keyword(s): Algorithmic trading, decision trees, deep learning, evolutionary algorithms, Lasso, Machine Learning, neural networks, NLP, random forests, SVM
JEL(s): G11, G17
Programme Areas: Financial Economics, International Macroeconomics and Finance
Link to this Page: cepr.org/active/publications/discussion_papers/dp.php?dpno=14525

Artificial intelligence (AI) has a growing presence in asset management and has revolutionized the sector in many ways. It has improved portfolio management, trading, and risk management practices by increasing efficiency, accuracy, and compliance. In particular, AI techniques help construct portfolios based on more accurate risk and returns forecasts and under more complex constraints. Trading algorithms utilize AI to devise novel trading signals and execute trades with lower transaction costs, and AI improves risk modelling and forecasting by generating insights from new sources of data. Finally, robo-advisors owe a large part of their success to AI techniques. At the same time, the use of AI can create new risks and challenges, for instance as a result of model opacity, complexity, and reliance on data integrity.