Investment portfolio optimization with supervised learning and attention mechanism

Portfolio optimization is a process that involves distribution of capital with the purpose of maximizing returns and at the same time minimizing risks. The current paper discusses the use of Transformer networks in supervised learning for portfolio optimization which can set new standards for machine learning-based investment strategies. The experiments show that the portfolio management method that utilizes attention mechanisms goes beyond traditional optimization methods with a substantial difference. The performance of the recommended model in terms of average annualized return and Sharpe ratio was 24.8% and 1.69 respectively over the 14 test cases. These are considerable improvements over the benchmark strategies like equal-weighted portfolios (Sharpe ratio: 0.54), market capitalization-weighted portfolios (Sharpe ratio: 0.43), and traditional index portfolios (Sharpe ratio: 0.37). The attention mechanism is what makes the model able to dynamically adjust the portfolio weights according to the changing market forces, thus, it can blend active and passive investments efficiently. Moreover, it managed to maintain a very good risk control capacity with a Sortino ratio of 2.45 while its performance during market volatility was still quite good. So, this research serves to provide both quantitative finance and machine learning with a proof that the novel deep learning architectures can easily beat the conventional portfolio optimization methods, even in the case of small asset pools.