Stock Price Prediction using Bi-Directional LSTM based Sequence to Sequence Modeling and Multitask Learning

Siddartha Mootha, Sashank Sridhar, Rahul Seetharaman, S. Chitrakala

Abstract: The stock market is a dynamic and volatile platform which provides an environment for traders to invest and trade in shares. The price of a stock is dependent on numerous static and dynamic features. Predicting the future price of a particular company’s stock can be extremely beneficial for traders. Seq2Seq modelling helps map an input sequence to an output sequence. In this paper, we propose a system to predict the future Open, High, Close, Low (OHCL) value of a stock using a Bi-Directional LSTM based Sequence to Sequence Modelling. Each OHCL price is an independent sequence and multitask learning helps map the interrelations between them. A multitask system is also proposed which uses sub tasks and shared tasks to model the prices. Stock prices of Tata Consumer Products Limited from the National Stock Exchange (NSE) of India is used. To evaluate the efficiency of the proposed systems, they are compared against various machine learning algorithms. The proposed Seq2Seq and multitask systems comfortably outperform the existing algorithms with RMSE values of 3.98 and 7.87 respectively.