A Predictive Stock Data Analysis with SVM-PCA Model

Abstract

In this paper the properties of Support Vector Machines on the financial time series data has been analyzed. The high dimensional stock data consists of many features or attributes. Most of the attributes of features are uninformative for classification. Detecting trends of stock market data is a difficult task as they have complex, nonlinear, dynamic and chaotic behaviour. To improve the forecasting of stock data performance different models can be combined to increase the capture of different data patterns. The uninformative attributes from the stock data are eliminated using the dimensionality reduction technique: Principal Component Analysis. The classification accuracy measure is compared when all the attributes are being considered and only when informative attributes are being considered.