| dc.contributor.author | Nyavanandi, Deepika | |
| dc.description.abstract | Forecasting stock market prices has been a challenging task due to its volatile nature and nonlinearity. Recently, artificial neural networks (ANNs) have become popular in solving a variety of scientific and financial problems including stock market price forecasting. ANNs have the ability to capture the underlying nonlinearity and complex relationship between the dependent and independent variables. This paper aims to compare the performance of various neural networks including Feed Forward Neural Networks (FNN), Vanilla Recurrent Neural Network (RNN), and Long Short-Term Memory (LSTM) neural networks by forecasting stock market prices of three different companies. Empirical results show that the LSTM neural network performed well in forecasting stock market prices compared to both vanilla RNN and FNN. | en_US |
| dc.publisher | North Dakota State University | en_US |
| dc.rights | NDSU Policy 190.6.2 | |
| dc.title | Predictive Performance Evaluation of Different Neural Networks Using Stock Prices | en_US |
| dc.type | Master's paper | en_US |
| dc.date.accessioned | 2019-05-10T20:39:26Z | |
| dc.date.available | 2019-05-10T20:39:26Z | |
| dc.date.issued | 2019 | en_US |
| dc.identifier.uri | https://hdl.handle.net/10365/29731 | |
| dc.subject.lcsh | Stock price forecasting -- Mathematical models. | |
| dc.subject.lcsh | Neural networks (Computer science) | |
| dc.rights.uri | https://www.ndsu.edu/fileadmin/policy/190.pdf | |
| ndsu.degree | Master of Science (MS) | en_US |
| ndsu.college | Engineering | en_US |
| ndsu.department | Computer Science | en_US |
| ndsu.program | Computer Science | en_US |
| ndsu.advisor | Ludwig, Simone | |
