To solve low prediction accuracy of Elman in predicting stock closing price, the model of adaptive boosting (AdaBoost)-improved artificial fish swarm algorithm (AAFSA)-Elman based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is proposed. By adding different white noise to the original data, CEEMDAN algorithm is used to decompose attributes serial selected by Boruta algorithm and text mining. To optimize the weight and threshold of Elman, self-adaption step length and view scope are used to improve artificial fish swarm algorithm (AFSA). AdaBoost algorithm is used to compose 5 weak AAFSA-Elman predictors into a strong predictor by continuous iteration. Experiments show that the mean absolute percentage error (MAPE) of AdaBoost-AAFSA-Elman model reduces from 4.9423% to 1.2338%. This study provides an experimental method for the prediction of stock closing price based on network public opinio.
ZHU Changsheng1 (朱昶胜)，KANG Lianghe1.3* (康亮河)，FENG Wenfang2 (冯文芳)
. Predicting Stock Closing Price with Stock Network Public Opinion Based on AdaBoost-AAFSA-Elman Model and CEEMDAN Algorithm[J]. Journal of Shanghai Jiaotong University(Science), 2023
, 28(6)
: 809
-821
.
DOI: 10.1007/s12204-021-2337-x
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