Date of Award
2019
Publisher
North Dakota State University
Document Type
Master's Paper
Degree Awarded
Master of Science (MS)
Program
Computer Science
Department
Computer Science
College
Engineering
Faculty Advisor
Ludwig, Simone
Description
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.
Recommended Citation
Nyavanandi, Deepika, "Predictive Performance Evaluation of Different Neural Networks Using Stock Prices" (2019). Computer Science. 168.
https://digitalcommons.ndsu.edu/computer-sci/168
Rights
NDSU Policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/29731