Stock Price Trend Prediction Based on RBF Neural Network and Artificial Fish Swarm Algorithm
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Wei Yanming, Gan Xusheng, Lei Lei
Stock market is a complex nonlinear dynamic system, the traditional stock price prediction method is difficult to reveal its inherent law, and the prediction error is larger. Based on this, a prediction method based on artificial Fish Swarm Algorithm (FSA) and RBF Neural Network (RBFNN) is proposed to predict the stock price trend. In method, firstly, a dynamic adjustment method to the algorithm parameters: visual field and movement step is introduced to improve the search capability of AFS, and then modified FSA is used to train the RBFNN model. The simulation shows that, the proposed method is better than BPNN and RBFNN in prediction accuracy for stock price trend. It provides an effective and feasible method for stock price prediction.
Prediction, Artificial Fish Swarm Algorithm, RBF Neural Network, Stock Price Trend.