Neural Network and Principle Component Analysis Based Numerical Data Analysis for Stock Market Prediction with Machine Learning Techniques
| Autor: | Afidalina Tumian, Imad Fakhri Taha Alshaikhli, Mohammad Rabiul Islam, Rizal Mohd Nor |
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| Rok vydání: | 2019 |
| Předmět: |
Stock market prediction
Index (economics) Artificial neural network business.industry Computer science Financial market 02 engineering and technology General Chemistry 021001 nanoscience & nanotechnology Condensed Matter Physics Machine learning computer.software_genre Computational Mathematics Specification Benchmark (surveying) Principal component analysis General Materials Science Stock market Artificial intelligence Electrical and Electronic Engineering 0210 nano-technology business computer |
| Zdroj: | Journal of Computational and Theoretical Nanoscience. 16:806-812 |
| ISSN: | 1546-1955 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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