Paper Info
Title
An Experimental Study on the Effectiveness of Artificial Neural Network-Based Stock Index Prediction
Abstract
Artificial Neural Network (ANN) is a promising tool for solving many recognition problems and has been a popular choice for researchers during the last decade. Machine learning tools such as Multi-Layer Perceptron (MLP) have proven effective in solving classification problems. Long Short Term Memory (LSTM) has been deemed to be the state of the art of the ANN family, which is specialized in tracking time series related data. The capability of LSTM as a powerful tool for making profit has been reported, along with its reputation for stock market prediction. In this study, Keras was used as a neural network library on top of Tensorflow as a machine learning backend using the Dow Jones Index (DJI) as the data source for the MLP and LSTM analyses. Our experimental results reveal that the prediction ability of MLP and LSTM possesses similar accuracy to the benchmark when providing only trading price and volume as the input data. This paper further discusses some difficulties in training MLP and LSTM that may have reduced the system capability to reach its expected potential.
Year
DOI
Venue
2019
10.1109/ICMLC48188.2019.8949282
2019 International Conference on Machine Learning and Cybernetics (ICMLC)
Keywords
Field
DocType
Deep learning,Artificial Neural Network,Multilayer Perceptron (MLP),Long Short Term Memory (LSTM),Keras,Tensorflow,Stock Prediction,Index Prediction
Data source,Stock market index,Computer science,Long short term memory,Artificial intelligence,Deep learning,Artificial neural network,Stock market prediction,Perceptron,Machine learning,Reputation
Conference
ISSN
ISBN
Citations 
2160-133X
978-1-7281-2817-7
0
PageRank 
References 
Authors
0.34
6
2
Name
Order
Citations
PageRank
Yichi Tsai100.34
Qiangfu Zhao221462.36