Felipe Henrique Rubim
Master's – Deep learning in the Brazilian stock market: factors that enable consistent predictions for risky decision making
Advisor: Prof. Dr. José Roberto Securato
Comission: Profs. Drs. Daniel Reed Bergmann, Renato Vicente and Ricardo Humberto Rocha
Class: 217, FEA-5
The design and operation of stock buying and selling strategies by artificial intelligence mechanisms has gained scale over the past decade aiming at above-average market outcomes for its users, generally hedge funds, institutional investors and large investment banks. Machine learning has become the central framework in the development of these processes, due to its extraordinary ability to reveal patterns hidden in a dark universe of data, which until then only hindered the traditional decision-making process undertaken by market players. In particular, this work aims to understand if in the Brazilian stock market it is possible to make consistent decisions, in a daily stock selection process, with the aid of some type of recurrent neural networks called Long Short-Term Memory (LSTM), which at certain depth levels and connectivity are known as deep learning. Models based on the LSTM architecture are lucky to extracting relevant information from long data strings, so they are naturally applicable to the problem of predicting future stock returns from historical financial time series, such as daily stock prices, the general market indexes, the dollar quotation, among others. Thus, a methodology was proposed that uses LSTM neural networks, together with such time series, as predictive models to support decision making in the Brazilian stock market. Several experiments were performed to find the best architecture and verify its economic potential, including the actions that composed the Bovespa Index (Ibovespa) in years between 2007 and 2018. In line with the evidences pointed out in previous studies, the models presented excellent results, even in relatively simple configurations. Large LSTM structures performed slightly lower than smaller architectures. In addition, it was observed that the complementary set of stock price information improved the learning and accuracy of the models, especially the market indexes and the exchanges rates. In economic terms, the average annual return on a portfolio that simulates daily purchases of 10 shares reached 22.3% annually, excluding fees and taxes. This result exceeds by more than 100% the average return of Ibovespa's constituent shares in the same period. Withal, the portfolio yield curve was consistently higher than the Ibovespa curve. Therefore, there is positive evidence on the temporal consistency of the proposed methodology, on its ability to reveal strong predictive variables, and thereafter potential application of LSTM neural networks in the Brazilian market.
*Abstract provided by the author