Stock Market Prediction Using Recurrent Neural Network Based on Time Series Forecasting

Researcher: Neo Matsobane, University of the Witwatersrand, Johannesburg
Supervisor: Dr Wilbert Chagwiza, University of the Witwatersrand, Johannesburg

The idea of predicting stock prices has always appealed to both financial investors and researchers. The stock market is unforeseeable in nature whereby financial Investors consistently enquire if the cost of a stock will increase or not. New technologies like data mining, machine learning and deep learning helps to examine large information and build up a model that keeps away from human mistakes during stock predictions. The purpose of this study is to build a recurrent neural network (RNN), specifically long-short-term memory model (LSTM) that predict stock market.