Neural Network and Principle Component Analysis Based Numerical Data Analysis for Stock Market Prediction with Machine Learning Techniques

摘要

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.