This study, proposes a novel neural network and fuzzy-neural network approach for predicting the closing index of the stock market. It strives to adapt the number of hidden neurons of a Multi Layer Feed Forward Neural Network (MLFFNN) and Fuzzy Time Series Multi Layer Feed Forward Neural Network (FTS-MLFFNN) model. It uses the Tracking Signal (TS) and rejects all models which result in values outside the interval of [-4, +4]. The effectiveness of the proposed approach is verified with one step ahead of Bombay Stock Exchange (BSE100) closing stock index of Indian stock market. This novel approach reduces the over-fitting problem, reduces the neural network structure and improves prediction accuracy. In addition, the result of MLFFNN with TS approach is compared to FTS-MLFFNN with TS approach. The result indicates that the FTS-MLFFNN with TS approach outperforms the MLFFNN with TS approach.