The Influence of Artificial Intelligence in Portfolio Management and Risk Optimization
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The current paper discusses how artificial intelligence (AI) has created a paradigm shift in the field of portfolio management and risk optimization and critically evaluates how machine learning, deep learning, reinforcement learning, and evolutionary computing can overcome the structural limitations of classical quantitative models. The traditional construction of a portfolio, using the Markowitz (1952) mean-variance maximization model, is limited by the assumptions of return normalization, covariance stationarity, and good parameter estimates (which are systematically broken in actual markets). Based on groundbreaking empirical research, such as Gu, Kelly, and Xiu (2020) on machine learning to price assets, Fischer and Krauss (2018) on LSTM to predict markets, and Jiang, Xu, and Liang (2017) on deep reinforcement learning to manage portfolios, the paper will synthesize the reported empirical findings to assess the practicality of AI-based Findings suggest that machine learning models can substantially outperform out-of-sample return predictions as compared to linear factor models; Gu et al. (2020) find that in some specifications, machine learning forecasts can be roughly two times greater than the predictive alpha of regression-based strategies. The authors show that LSTM networks trained on S&P 500 constituents can obtain daily returns of 0.46% in advance of costs and a Sharpe ratio of 5.8, which is much higher than memory-free classifiers (Lutz, 2018). The reinforcement learning agents also have dynamic, cost sensitive rebalancing of portfolios that are more than the fixed benchmark allocations. The paper finds that AI is a true paradigm shift in quantitative investment management, with better risk-adjusted returns and ability to manage tail-risk, even though overfitting, limitations on interpretability, and sensitivity to transaction costs still pose critical issues in practice.
Keywords
Artificial Intelligence, Conditional Value-At-Risk, Deep Learning, Factor Investing, Genetic Algorithms, Long Short-Memory, Machine Learning, Mean-Variance Optimization, Portfolio Optimization, Quantitative Finance, Reinforcement Learning, Risk Management, Sharpe Ratio