Title :
The Influence of Volume and Volatility on Predicting Shanghai Stock Exchange Trends
Author :
Pierrot, Romain ; Liu, Hongyan
Author_Institution :
Sch. of Econ. & Manage., Tsinghua Univ., Beijing
Abstract :
Most of the previous studies concerning mining association rules from stock time series simply use confidence and support thresholds. In this paper we introduce two new thresholds - trading volume and stock volatility- that suit stock time series behaviour better. In this study, we test the influence of volatility and volume on share price weekly trends. Various experimental results yield the strong correlation between trading volume and classifying accuracy. We use the mined rules to classify and predict future trends. A new method, namely weighted confidence, is proposed for carrying out associative classification/prediction. Its accuracy is equivalent to other traditional measures.
Keywords :
data mining; share prices; stock markets; time series; Shanghai stock exchange trend prediction; association rule mining; share price weekly trend; stock time series behaviour; stock volatility; Association rules; Data mining; Data preprocessing; Economic forecasting; Fuzzy systems; Neural networks; Share prices; Stock markets; Testing; Transaction databases; Associative Classification; Associative Rule Mining; Stock Data Mining; Time Series forecasting;
Conference_Titel :
Fuzzy Systems and Knowledge Discovery, 2008. FSKD '08. Fifth International Conference on
Conference_Location :
Shandong
Print_ISBN :
978-0-7695-3305-6
DOI :
10.1109/FSKD.2008.88
