Predicting stock index movement using twin support vector machine as an integral part of enterprise system

Borong Zou, Hong Wang, Hui Li, Ling Li and Yuhan Zhao

Systems Research and Behavioral Science, 2022, vol. 39, issue 3, 428-439

Abstract: In order to improve the predicting performance of stock index movement, this study proposes a new predicting model called Twin Support Vector Machines (TWSVM), which will be used to predict the trend of Shanghai Securities Composite Index (SSCI) and Standard and Poor's 500 Index (S&P500 Index), respectively. Thirteen indicators constructed by stock index historical data are selected as input features of the predicting model. The predicting target is the stock index daily movement, up or down. The decision tree (DT), Naive‐Bayes (NB), random forests (RF), probabilistic neural network (PNN) and support vector machine (SVM) are set as contrast experiments. The experiment results indicate that the TWSVM predicting model has a better predicting performance on both stock price and index daily movement.

Date: 2022
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