Computationally efficient FLANN-based intelligent stock price prediction system

We propose a computationally efficient and effective novel neural network for predicting the next-day´s closing price of US stocks in different sectors: technology, energy and finance. In this paper we used a computationally efficient functional link artificial neural network (FLANN) in making stock price prediction. We modeled the trend in stock price movement as a dynamic system and apply FLANN to predict the stock price behavior. In addition to historical pricing data, we considered other financial indicators such as the industrial indices and technical indicators, for better accuracy. We showed its superior performance by comparing with a multilayer perceptron (MLP)-based model through several experiments based on different performance metrics, namely, computational complexity, root mean square error, average percentage error and hit rate.