Stock Market Forecasting Model From Multi News Data Source Using a Two-Level Learning Algorithm

Abstract

Stock prediction retains the attention of a large part of the community. The emergence of new indicators mostly extracted from the web makes this domain of research challenging and in a continuous evolution. The present work tries to address the question of how to model financial news from multi-data source for the purpose of forecasting stock movement. We combined different news sources to enhance the accuracy of stock movement prediction. Data are collected from four financial news websites and proceeded individually by Support Vector Machine (SVM) algorithm then we aggregate outputs using an Artificial Neural Network (ANN) algorithm. Experiments were conducted and the results have shown that the designed two-level learning SVC&ANN algorithm has achieved better accuracy than simple news analysis models using a single information source.