Stock Price Trend Prediction using Artificial Neural Network and Derived Parameters

This thesis explores derived parameter optimization technique to optimize the performance of forecasting models. This study presents artificial neural network (ANN) based computational approach for predicting the stock market trend of companies from five different sectors such as:IT Sector (Infosys), Banking Sector (SBI), Consumer Goods Sector (Tata Motors), Industrial Goods Sector (BHEL) and Basic Material Sector (ONGC). A new approach using Derived Parameter (MRDD: Measure of R square value divided by standard deviation) is developed to find out the best forecasting model. Sixty three neural network models were designed and trained using backpropagation training algorithm. Forecasting performance of sixty three neural network models was optimized using derived parameter (MRDD). This paper concludes this research work by proposing new method called Hybrid Parameter Weighted Method using Derived Parameter (HPWMDP).