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

• Main Authors: Islam, Mohammad Rabiul, Taha Alshaikhli, Imad Fakhri, Mohd Nor, Rizal, Tumian, Afidalina
• Format: Article
• Language: English; English
• Published: American Scientific Publishers 2019
• Subjects: QA75 Electronic computers. Computer science
• Online Access: http://irep.iium.edu.my/76579/12/76579_Neural%20Network%20and%20Principle%20Component_article%20for%20MYRA%20new.pdf; http://irep.iium.edu.my/76579/7/76579_Neural%20network%20and%20principle%20component%20analysis%20based%20numerical%20data%20analysis%20for%20stock%20market%20prediction%20with%20machine%20learning%20techniques_Scopus.pdf; http://irep.iium.edu.my/76579/; http://www.aspbs.com/ctn/
• Institution: Universiti Islam Antarabangsa Malaysia

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.