Previous studies on forecasting Value-at-Risk mostly focus on using the information directly obtained from the market itself. In this paper, we propose a new method for predicting Value-at-Risk by incorporating both internal and external information using artificial neural network. We show that artificial neural network can provide us a flexible framework for including external information, and these additional factors can help to improve the accuracy of Value-at-Risk prediction.