Application of long-short term memory Deep Neural Network in financial forecasting

Simple item page
dc.contributor.authorWanyonyi, Watua Peter
dc.date.accessioned2021-02-22T10:05:25Z
dc.date.available2021-02-22T10:05:25Z
dc.date.issued2019
dc.descriptionA Dissertation submitted in partial fulfillment of the requirements for the Master of Science in Mathematical Finance (MSc.MF) at Strathmore Universityen_US
dc.description.abstractThe 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.en_US
dc.identifier.urihttp://hdl.handle.net/11071/10160
dc.language.isoenen_US
dc.publisherStrathmore Universityen_US
dc.subjectFinancial forecastingen_US
dc.subjectARIMA-GARCH hybrid modelen_US
dc.subjectDeep Neural Network (DNN)en_US
dc.titleApplication of long-short term memory Deep Neural Network in financial forecastingen_US
dc.typeThesisen_US
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Application of long-short term memory Deep Neural Network in financial forecasting.pdf
Size:
15.9 MB
Format:
Adobe Portable Document Format
Description:
Fulltext thesis
Download
License bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
MSc.MF Theses and Dissertations (2019)
Load more