Enhancing Profit by Predicting Stock Prices using Deep Neural Networks

Abstract

Financial time series forecasting is a challenging task, which has attracted the interest of several researchers and is immensely important for investors. In this paper, we present a deep learning system, which uses a variety of data for a subset of the stocks on the NASDAQ exchange to forecast the stock price. The prediction model is trained on the minutely data for a specific stock ticker and predicts the closing price of that stock ticker for multi-step-ahead. Our deep learning framework consists of a Variational Autoencoder for removing noise and uses time-series data engineering to combine the higher-level features with the original features. This new set of features is fed to a Stacked LSTM Autoencoder for multi-step-ahead prediction of the stock closing price. Besides, this prediction is used by a profit-maximization strategy to provide advice on the appropriate time for buying and selling a specific stock. Results show that the proposed framework outperforms the state-of-the-art time series forecasting approaches with respect to predictive accuracy and profitability.