Ultimate Prediction of Stock Market Price Movement
- 1 Sulaimani Polytechnic University, Iraq
- 2 University of Technology Malaysia, Malaysia
- 3 Iwate Prefectural University, Japan
Copyright: © 2020 Rebwar M. Nabi, Soran Ab. M. Saeed, Habibolah Bin Harron and Hamido Fujita. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Investment in the stock market is currently very popular due to its economic gain. Numerous researchers’ and academicians’ work is focused on financial time series prediction due to its data availability and profitability. Therefore, this study presents the design and implementation of a novel binary classification framework to predict stock market trends. The framework is composed of data preprocessing, feature engineering, feature selection and classification algorithms. The model is built on multiple sector stock market companies’ data collected from NASDAQ over a period of ten years. Various feature selection algorithms are applied in combination with several machine learning algorithms. Furthermore, as the new contribution, we have constructed two new features which have been found to be promising in terms of improving overall performance. Ultimately, a collaboration of feature selection and classification techniques is employed. The application of Principal Component Analysis (PCA) with Multilayer Perceptron and Support Vector Machine (SVM) to added featured datasets shows 100% accuracy on the majority of datasets. In summary, an intensive comparison is presented among the various feature selection and classification algorithms.
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- Stock Market Forecasting
- Feature Engineering
- Feature Selection
- Machine Learning Mechanism
- Predictive Analysis
- Predictable Movement
- Java and WEKA