Stock values predictions using deep learning based hybrid models

Abstract

Abstract Predicting the correct values of stock prices in fast fluctuating high‐frequency financial data is always a challenging task. A deep learning‐based model for live predictions of stock values is aimed to be developed here. The authors' have proposed two models for different applications. The first one is based on Fast Recurrent Neural Networks (Fast RNNs). This model is used for stock price predictions for the first time in this work. The second model is a hybrid deep learning model developed by utilising the best features of FastRNNs, Convolutional Neural Networks, and Bi‐Directional Long Short Term Memory models to predict abrupt changes in the stock prices of a company. The 1‐min time interval stock data of four companies for a period of one and three days is considered. Along with the lower Root Mean Squared Error (RMSE), the proposed models have low computational complexity as well, so that they can also be used for live predictions. The models' performance is measured by the RMSE along with computation time. The model outperforms Auto Regressive Integrated Moving Average, FBProphet, LSTM, and other proposed hybrid models on both RMSE and computation time for live predictions of stock values.

Keywords

• stock markets
• recurrent neural nets
• mean square error methods
• economic forecasting
• deep learning (artificial intelligence)
• financial data processing