Forecasting Stock Market Trend using Prototype Generation Classifiers

摘要: Currently, stock price forecasting is carried out using either time series prediction methods or trend classifiers. The classifiers are designed to predict the behaviour of price's movement. Recently, soft computing methods, like support vector machines, have shown promising results in realization this particular problem. In paper, we apply several prototype generation NASDAQ Composite index. We demonstrate that outperform machines and neural networks considering hit ratio correctly predicted directions.