%0 Journal Article %A FENG Wenfang %A KANG Lianghe %A ZHU Changsheng %T Stock closing price prediction algorithm using adaptive whale optimization algorithm and Elman neural network %D 2020 %R 10.11772/j.issn.1001-9081.2019091678 %J Journal of Computer Applications %P 1501-1509 %V 40 %N 5 %X

Focused on the issue that Elman neural network has slow convergence speed and low prediction accuracy in the closing price prediction based on the network public opinion of the stock market, a prediction model combining Improved Whale Optimization Algorithm (IWOA) and Elman neural network was proposed, which is based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)algorithm. Firstly, text mining technology was used to mine and quantify the network public opinions of Shanghai Stock Exchange (SSE) 180 shares, and in order to reduce the complexity of attribute set, Boruta algorithm was used to select the important attributes. Then, CEEMDAN algorithm was used to add a certain number of white noises with specific variances in order to realize the decomposition and noise reduction of the attribute sequence. At the same time, in order to enhance the global search and local mining capabilities, adaptive weight was used to improve Whale Optimization Algorithm (WOA). Finally, the initial weights and thresholds of Elman neural network were optimized by WOA in the iterative process. The results show that, compared to Elman neural network, the proposed model has the Mean Absolute Error (MAE) reduced from 358.812 0 to 113.055 3; compared to the original dataset without CEEMDAN algorithm, the proposed model has the Mean Absolute Percentage Error (MAPE) reduced from 4.942 3% to 1.445 31%, demonstrating that the model effectively improves the prediction accuracy and provides an effective experimental method for predicting the network public opinion of stock market.

%U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2019091678