Financial Trading System using Combination of Textual and Numerical Data

Abstract

There is large amount of financial data that are generated and evaluated at a high speed. These financial data is coming continuously, changing with time and may be unpredictable. Therefore there is a critical need for automated approaches to effective and efficient utilization of large amount of data to support companies and individuals for decision-making. Data mining techniques can be used to uncover hidden patterns, to discover the behavior of the stock market, to find out the trends in financial markets and so on. For predicting stock trends and making financial trading decisions, a new model is presented. It is based on combination of data and text mining techniques which takes the textual contents of time-stamped web documents along with numerical time series data and performs the future prediction. By using this model, we will show that the accuracy of result will be improved.