Gated Recurrent Unit Networks and Discrete Wavelet Transforms Applied to Forecasting and Trading in the Stock Market

Abstract

Trading in the stock market always comes with the challenge of deciding the best action 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. In this work we propose a novel model called Discrete Wavelet Transform Gated Recurrent Unit Network (DWT-GRU). The model learns from the data to choose between buying, holding and selling, and when to execute them. The proposed model was compared to other recurrent neural networks, with and without wavelets preprocessing, and the buy and hold strategy. The results shown that the DWT-GRU outperformed all the set baselines in the analysed stocks of the Brazilian stock market.

Predicting Wavelet-Transformed Stock Prices Using a Vanishing Gradient Resilient Optimized Gated Recurrent Unit with a Time Lag

“…The wavelet transform was also used with the Gated Recurrent Unit (GRU) neural network. [21] developed the DWT Gated Recurrent Unit Network model (DWT-GRU) for stock exchange data. The DWT-GRU consisted of combining the DWT's denoising and decomposition capacity with pre-processed data to be trained by an RNN based primarily on the Gated Recurrent Unit Neural Net-work (GRUNN).…”