DELAFO: An Efficient Portfolio Optimization Using Deep Neural Networks

Portfolio optimization has been broadly investigated during the last decades and had a lot of applications in finance and economics. In this paper, we study the portfolio optimization problem in the Vietnamese stock market by using deep-learning methodologies and one dataset collected from the Ho Chi Minh City Stock Exchange (VN-HOSE) from the beginning of the year 2013 to the middle of the year 2019. We aim to construct an efficient algorithm that can find the portfolio having the highest Sharpe ratio in the next coming weeks. To overcome this challenge, we propose a novel loss function and transform the original problem into a supervised problem. The input data can be determined as a 3D tensor, while the predicted output is the unnormalized weighted proportion for each ticker in the portfolio to maximize the daily return Y of the stock market after a given number of days. We compare different deep learning models, including Residual Networks (ResNet), Long short-term memory (LSTM), Gated Recurrent Unit (GRU), Self-Attention (SA), Additive Attention (AA), and various combinations: SA + LSTM, SA + GRU, AA + LSTM, and AA + GRU. The experimental results show that the AA + GRU outperforms the rest of the methods on the Sharpe ratio and provides promising results for the portfolio optimization problem not only in Vietnam but also in other countries.