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New Approaches for Stock Index Prediction Using Artifical Neural Networks

Navin S. Patel1 , Y.T. Krishne Gowda2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-4 , Page no. 1137-1141, Apr-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i4.11371141

Online published on Apr 30, 2019

Copyright © Navin S. Patel, Y.T. Krishne Gowda . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: Navin S. Patel, Y.T. Krishne Gowda, “New Approaches for Stock Index Prediction Using Artifical Neural Networks,” International Journal of Computer Sciences and Engineering, Vol.7, Issue.4, pp.1137-1141, 2019.

MLA Style Citation: Navin S. Patel, Y.T. Krishne Gowda "New Approaches for Stock Index Prediction Using Artifical Neural Networks." International Journal of Computer Sciences and Engineering 7.4 (2019): 1137-1141.

APA Style Citation: Navin S. Patel, Y.T. Krishne Gowda, (2019). New Approaches for Stock Index Prediction Using Artifical Neural Networks. International Journal of Computer Sciences and Engineering, 7(4), 1137-1141.

BibTex Style Citation:
@article{Patel_2019,
author = {Navin S. Patel, Y.T. Krishne Gowda},
title = {New Approaches for Stock Index Prediction Using Artifical Neural Networks},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {4 2019},
volume = {7},
Issue = {4},
month = {4},
year = {2019},
issn = {2347-2693},
pages = {1137-1141},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=4178},
doi = {https://doi.org/10.26438/ijcse/v7i4.11371141}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v7i4.11371141}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=4178
TI - New Approaches for Stock Index Prediction Using Artifical Neural Networks
T2 - International Journal of Computer Sciences and Engineering
AU - Navin S. Patel, Y.T. Krishne Gowda
PY - 2019
DA - 2019/04/30
PB - IJCSE, Indore, INDIA
SP - 1137-1141
IS - 4
VL - 7
SN - 2347-2693
ER -

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Abstract

Stock index prediction is a continually evolving field. Prediction is used for technical and fundamental analysis by both short term traders and long terms investors. There are a multitude of theories and methodologies that exist in the area of stock index prediction. These range from older time-series econometric models to newer Artificial Neural Network (ANN) models. The scope and applicability of ANN is widening rapidly with newer and powerful architectures being proposed in the past few years. Significant amount of work has been done using ANN for stock index prediction, but most of it is done on a class of network architecture called Multi Layered Perceptron (MLP). In this work, we make use of two relatively newer ANN architectures – Long-Short Term Memory (LSTM) and Convolutional Neural Network (CNN) for stock index prediction. We find that these two models offer better forecast accuracy compared to the MLP model for the chosen stock index.

Key-Words / Index Term

Stock index prediction, MLP, LSTM, CNN

References

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