This paper investigates the accuracy of Feedforward Neural Network (FFNN) with different external parameters in predicting the closing price of a particular stock. Specifically, the feedforward neural network was trained using Levenberg-Marquardt backpropagation algorithm to forecast the CIMB stocks closing price in the Kuala Lumpur Stock exchange (KLSE). The results indicate that the use of external parameters can improve the accuracy of the stocks closing price.