Financial Trends prediction using the Back Propagation Neural Network and YQL

In this world a amount of things and procedures are impulsive, among the stock market value is an entity which is impulsive. The stock market and their prices are haphazard in impulsive manner that can be depends on amount of external associations such as politics, terror, social and others. But a number of researchers are building efforts to forecast the stock market values. To forecast the upcoming stock market trends the data mining and machine intelligence based techniques are employed to forecast precisely the worth. It works too precisely estimate the stock value a back propagation neural network based system is used. The proposed system is an enhanced version of the traditionally available back propagation neural network. The back propagation neural network is a supervised learning advance therefore the training and testing both are required. To prepare the proposed data model the financial records of the companies are required. Thus for finding the historical financial market trends the YQL (yahoo query language) is proposed for use. This YQL make available the historical records of the target company between two dates. Using the acquired data from YQL is used to train the BPN network. After training the implemented classifier is used for forecasting the upcoming stock