Working Paper: CEPR ID: DP14525
Authors: Shnke Bartram; Jrgen Branke; Mehrshad Motahari
Abstract: 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.
Keywords: algorithmic trading; machine learning; lasso; neural networks; deep learning; decision trees; random forests; support vector machines; evolutionary algorithms; natural language processing
JEL Codes: G11; G17
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| AI techniques (C45) | improved portfolio performance (G11) |
| AI (C45) | enhanced accuracy of risk and return forecasts (G17) |
| AI (C45) | construction of portfolios under complex constraints (G11) |
| AI facilitates trading (C45) | trading efficiency (G14) |
| AI generates novel trading signals (C45) | reduced transaction costs (D23) |
| AI improves risk modeling and forecasting (G17) | accuracy of risk assessments (C52) |
| AI introduces new risks (C45) | model opacity (C67) |
| AI introduces new risks (C45) | potential systemic crashes (G01) |