Stock Market Prediction with Big Data Through Hybridization of Data Mining and Optimized Neural Network Techniques

Stock Market Prediction with Big Data Through Hybridization of Data Mining and Optimized Neural Network Techniques

The stock market is non-linear in nature, making forecasting a very complicated, challenging and uncertain process. Employing traditional methods may not ensure the reliability of stock prediction. In this paper, we have applied both data mining and optimized neural network in stock prediction with...

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Main Authors: Das, Debashish, Sadiq, Ali Safa, Noraziah, Ahmad, Lloret, Jaime
Format: Article
Language:English
Published: Old City Publishing 2017
Subjects:
QA75 Electronic computers. Computer science
Online Access:http://umpir.ump.edu.my/id/eprint/19909/
http://umpir.ump.edu.my/id/eprint/19909/
http://umpir.ump.edu.my/id/eprint/19909/1/Stock%20Market%20Prediction%20with%20Big%20Data%20Through%20Hybridization%20of%20Data%20Mining%20and%20Optimized%20Neural%20Network%20Techniques.pdf
id ump-19909
recordtype eprints
spelling ump-199092018-03-29T08:02:20Z http://umpir.ump.edu.my/id/eprint/19909/ Stock Market Prediction with Big Data Through Hybridization of Data Mining and Optimized Neural Network Techniques Das, Debashish Sadiq, Ali Safa Noraziah, Ahmad Lloret, Jaime QA75 Electronic computers. Computer science The stock market is non-linear in nature, making forecasting a very complicated, challenging and uncertain process. Employing traditional methods may not ensure the reliability of stock prediction. In this paper, we have applied both data mining and optimized neural network in stock prediction with big data. Data mining allows for useful information to be extracted from a huge data set whilst neural network is capable in predicting future trends from large databases; the hybridization of both these techniques can therefore achieve much reliable and robust prediction. This paper has attempted to make a better prediction result for a complicated stock market. In this research, we have collected data from IT Sector organizations of the Dhaka Stock Exchange, which is an emerging stock market and applied K-means clustering of data mining to select the highly increasing securities, Nonlinear autoregressive neural network technique is applied to predict the stock price. The prediction performance through the hybridization is evaluated and positive performance improvement of prediction is observed which is encouraging for investors. Old City Publishing 2017 Article PeerReviewed application/pdf en http://umpir.ump.edu.my/id/eprint/19909/1/Stock%20Market%20Prediction%20with%20Big%20Data%20Through%20Hybridization%20of%20Data%20Mining%20and%20Optimized%20Neural%20Network%20Techniques.pdf Das, Debashish and Sadiq, Ali Safa and Noraziah, Ahmad and Lloret, Jaime (2017) Stock Market Prediction with Big Data Through Hybridization of Data Mining and Optimized Neural Network Techniques. Journal of Multiple-Valued Logic and Soft Computing, 29 (1-2). pp. 157-181. ISSN 1542-3980(print); 1542-3999(online) http://www.oldcitypublishing.com/journals/mvlsc-home/mvlsc-issue-contents/mvlsc-volume-29-number-1-2-2017/mvlsc-29-1-2-p-157-181/
repository_type Digital Repository
institution_category Local University
institution Universiti Malaysia Pahang
building UMP Institutional Repository
collection Online Access
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Das, Debashish
Sadiq, Ali Safa
Noraziah, Ahmad
Lloret, Jaime
Stock Market Prediction with Big Data Through Hybridization of Data Mining and Optimized Neural Network Techniques
description The stock market is non-linear in nature, making forecasting a very complicated, challenging and uncertain process. Employing traditional methods may not ensure the reliability of stock prediction. In this paper, we have applied both data mining and optimized neural network in stock prediction with big data. Data mining allows for useful information to be extracted from a huge data set whilst neural network is capable in predicting future trends from large databases; the hybridization of both these techniques can therefore achieve much reliable and robust prediction. This paper has attempted to make a better prediction result for a complicated stock market. In this research, we have collected data from IT Sector organizations of the Dhaka Stock Exchange, which is an emerging stock market and applied K-means clustering of data mining to select the highly increasing securities, Nonlinear autoregressive neural network technique is applied to predict the stock price. The prediction performance through the hybridization is evaluated and positive performance improvement of prediction is observed which is encouraging for investors.
format Article
author Das, Debashish
Sadiq, Ali Safa
Noraziah, Ahmad
Lloret, Jaime
author_facet Das, Debashish
Sadiq, Ali Safa
Noraziah, Ahmad
Lloret, Jaime
author_sort Das, Debashish
title Stock Market Prediction with Big Data Through Hybridization of Data Mining and Optimized Neural Network Techniques
title_short Stock Market Prediction with Big Data Through Hybridization of Data Mining and Optimized Neural Network Techniques
title_full Stock Market Prediction with Big Data Through Hybridization of Data Mining and Optimized Neural Network Techniques
title_fullStr Stock Market Prediction with Big Data Through Hybridization of Data Mining and Optimized Neural Network Techniques
title_full_unstemmed Stock Market Prediction with Big Data Through Hybridization of Data Mining and Optimized Neural Network Techniques
title_sort stock market prediction with big data through hybridization of data mining and optimized neural network techniques
publisher Old City Publishing
publishDate 2017
url http://umpir.ump.edu.my/id/eprint/19909/
http://umpir.ump.edu.my/id/eprint/19909/
http://umpir.ump.edu.my/id/eprint/19909/1/Stock%20Market%20Prediction%20with%20Big%20Data%20Through%20Hybridization%20of%20Data%20Mining%20and%20Optimized%20Neural%20Network%20Techniques.pdf
first_indexed 2023-09-18T22:28:31Z
last_indexed 2023-09-18T22:28:31Z
_version_ 1777416129204977664

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