Trading in the stock market always comes with the challenge of choosing the best decision to take on each time step. The problem is intensified by the theory that it is not possible to predict stock market time series as all information related to the stock price is already contained in it, which theory is known as Efficient Market Hypotesis. Although the market, in general, has no distinguishable tendencies, thus being consistent to the EMH, there are several time windows where there is some predictability, to some extent in the data, if we consider the use of technical indicators. In this work, a novel model is proposed to seek benefit from said periods operating to choose its actions and waiting for the best moment to execute them. This model, called Discrete Wavelet Transform Gated Recurrent Unit Network) (DWT-GRU), is divided in three modules, them being, the preprocessing of the data by the wavelet transform, the training and prediction of the closing price two days in the future and the decision making based on the evaluation of the gradient of the closing price. The proposed model was compared to other RNN architectures, with and without the use of wavelet preprocessing, and the "buy-and-hold" strategy. The results shown that the proposed model surpassed all the statistical metrics of accuracy, precision, recall, F1 and financial return of all the estabilshed comparisson models in the analysed stocks of the Brazilian Stock Market. The analysed stocks as the base for the study were the blue-chips of the IBOVESPA index, them being, PETR4, VALE3, ITUB4, ABEV3, and the ETF that mirrors the index itself, BOVA11. As training data the analysed period was since 2001 for the stocks and 2008 for the Fundo de Índice Negociado em Bolsa - ExchangeTraded Fund (ETF) BOVA11. At last, it is presented the financial results of the application of the algorithm in real time swing-trade operations validating its efficiency and winning over the buy-and-hold strategy
Gated Recurrent Unit Networks and Discrete Wavelet Transforms Applied to Forecasting and Trading in the Stock Market
