摘要
Stock trend prediction is a challenging problem because it involves many variables.Aiming at the problem that some existing machine learning techniques, such as random forest(RF), probabilistic random forest(PRF), k-nearest neighbor(KNN), and fuzzy KNN(FKNN), have difficulty in accurately predicting the stock trend(uptrend or downtrend) for a given date, a generalized Heronian mean(GHM) based FKNN predictor named GHM-FKNN was proposed.GHM-FKNN combines GHM aggregation function with the ideas of the classical FKNN approach.After evaluation, the comparison results elucidated that GHM-FKNN outperformed the other best existing methods RF, PRF, KNN and FKNN on independent test datasets corresponding to three stocks, namely AAPL, AMZN and NFLX.Compared with RF, PRF, KNN and FKNN, GHM-FKNN achieved the best performance with accuracy of 62.37% for AAPL, 58.25% for AMZN, and 64.10% for NFLX.
作者
吴振峰
WANG Mengmeng
LAN Tian
ZHANG Anyuan
WU Zhenfeng;WANG Mengmeng;LAN Tian;ZHANG Anyuan(Institute of Scientific and Technical Information of China,Beijing 100038,P.R.China;School of Economics,Renmin University of China,Beijing 100872,P.R.China;Shandong Provincial Center for Quality Control of Feed and Veterinary Drug,Jinan 250022,P.R.China)
基金
Supported by the National Key Research and Development Program (No.2019YFA0707201)
the Key Work Program of Institute of Scientific and Technical Information of China (No.ZD2022-01,ZD2023-07)。
