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Neural network and principle component analysis based numerical data analysis for stock market prediction with machine learning techniques

Islam, Mohammad Rabiul and Taha Alshaikhli, Imad Fakhri and Mohd Nor, Rizal and Tumian, Afidalina (2019) Neural network and principle component analysis based numerical data analysis for stock market prediction with machine learning techniques. Journal of Computational and Theoretical Nanoscience, 16 (3). pp. 806-812. ISSN 1546-1955 E-ISSN 1546-1963

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Abstract

Financial market prediction is gaining attention throughout the market phenomena since various applicable techniques within soft-computational methods have been analyzed to define the optimization. The study of this experimental research focused on two benchmark numerical stock market dataset (S&P 500 index dataset and OHLCV dataset). This structural dataset is analyzed through two main applicable techniques such as Feed-forward Neural Network and Principle Component Analysis for stock market prediction where the remarkable Machine Learning technique hold a variant of features. The architectural neural network is rebuilt based on four layers with neurons that influence on high-dimensional dataset with the performance of popular ReUL activation function. Model specification also embodies the result of precision, recall and “F-score” within the number of twenty epochs. An overall picture of this developing model approaches the maximum level of accuracy which impacts on the academical research philosophy for financial market prediction.

Item Type: Article (Journal)
Additional Information: 6534/76579
Uncontrolled Keywords: Neural Network, Principle Component Analysis, Numerical Data Analysis, Stock Prediction.
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Information and Communication Technology > Department of Computer Science
Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User: Professor Imad Taha
Date Deposited: 27 Nov 2019 20:34
Last Modified: 27 Nov 2019 20:34
URI: http://irep.iium.edu.my/id/eprint/76579

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