Optimisation of hybrid neural network techniques used for stock market predictions

Researcher: Mohammad Rehman, University of the Witwatersrand, Johannesburg
Supervisor: Dr Wilbert Chagwiza, University of the Witwatersrand, Johannesburg

The combination of traditional technical and fundamental analysis techniques and machine learning techniques has become a common practice for informed stock price prediction.

The focus of this research was to stochastically optimise a long short term memory (LSTM) prediction model using a genetic algorithm (GA) and test its viability for predicting next day stock prices.

The hybrid GA optimised LSTM model created was able to achieve an RMSE of 247.30, an MAE of 190.22 and an MAPE of 1.52% indicating that a viable prediction model was constructed.