Forecasting Stock Price Using Conditional Inference Tree

Now a days trading in stock market for the purpose of gaining huge profits is becoming more lucrative for people across the globe. Any attempts that are able to predict the financial markets productively and with precision have always enthralled the curiosity and attention of economist, bankers and scientist. Stock market prediction can be defined as the process of determination of future value of the stocks of particular company or any other financial exchange. Precise predictions in this field are of great importance for a variety of reasons. Among all the reasons the one that stand out the most is the need for and investor to protect against all the potential risks and the opportunities for mediators and speculators to earn money and huge profits by trading indexes. Stock market can be defined as a place where the trading and issuing of shares occurs. The trade of the shares occurs mainly through stock exchanges in digital or in physical form. Data mining has its own significance in financial modeling because of its ability to reveal useful patterns and correlations. Data mining, can be defined as process that deals with the discovery of knowledge, data analysis along with full and semi-autonomous decision making in the field of computational intelligence. This very phenomenon of stock market prediction is gaining a lot of attention in recent times. This paper presents a system based on decision tree which is classified as c tree. This paper will be using the same model to predict the future trends of certain stocks based on the data from the past and their respective behavior.