Application of long-short term memory Deep Neural Network in financial forecasting
Date
2019
Authors
Wanyonyi, Watua Peter
Journal Title
Journal ISSN
Volume Title
Publisher
Strathmore University
Abstract
The goal of this research was to apply Long-Short Term Memory Deep Neural Networks in financial forecasting. In order to predict the financial data, we used long-short term model and we compared its performance to ARIMA-GARCH hybrid model. In the study we used the ARIMA-GARCH time series model and studied its limitations in time series forecasting. We then introduced Deep Neural Network model (DNN) so as to improve accuracy which was tested on different financial datasets. Lastly we compared the results of the models employed using the root mean square error (RMSE) and p-value; LSTM had RMSE of 0.09989178 while the ARIMA-GARCH had RMSE of 0.0178. It was then concluded that the long-short term memory (LSTM) model, which is one of the DNN models, had significantly better than the ARIMA-GARCH Hybrid model in prediction/forecasting financial returns on FTSEIOO and S&PSOO indices.
Description
A Dissertation submitted in partial fulfillment of the requirements for the Master of Science in Mathematical Finance (MSc.MF) at Strathmore University
Keywords
Financial forecasting, ARIMA-GARCH hybrid model, Deep Neural Network (DNN)