Publication Type : Conference Paper
Publisher : ARTCom 2010
Source : Proceedings - 2nd International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom 2010, Kottayam, p.381-385 (2010)
Url : http://www.scopus.com/inward/record.url?eid=2-s2.0-78651448991&partnerID=40&md5=2080cad2395a0c6769a8c6f93d518e40
ISBN : 9780769542010
Keywords : Adaptive neuro fuzzy system, Automation, Commerce, Data sets, Decision trees, Dimensionality, Dimensionality reduction, Feature extraction, Feature selection, Feature sets, Finance, Forecasting, Fuzzy logic, Fuzzy sets, Hybrid systems, International stock markets, Neuro-Fuzzy, Neurofuzzy system, Prediction accuracy, Prediction systems, Stand -alone, Stock, Stock market, Stock market prediction, System use, Technical analysis
Campus : Coimbatore
School : School of Engineering
Department : Electronics and Communication
Year : 2010
Abstract : Stock market prediction is of great interest to stock traders and applied researchers. Main issues in developing a fully automated stock market prediction system are: feature extraction from the stock market data, feature selection for highest prediction accuracy, the dimensionality reduction of the selected feature set and the accuracy and robustness of the prediction system. In this paper, an automated decision tree-adaptive neuro-fuzzy hybrid automated stock market trend prediction system is proposed. The proposed system uses technical analysis (traditionally used by stock traders) for feature extraction and decision tree for feature selection. Selected features are then subjected to dimensionality reduction and the reduced dataset is then applied to the adaptive neuro-fuzzy system for the next-day stock market trend prediction. The proposed system is tested on four major international stock markets. The results show that the proposed hybrid system produces much higher accuracy when compared to stand-alone decision tree based system and ANFIS based system without feature selection and dimensionality reduction. © 2010 IEEE.
Cite this Research Publication : Dr. Binoy B. Nair, Dharini, N. M., and Mohandas, V. P., “A stock market trend prediction system using a hybrid decision tree-neuro-fuzzy system”, in Proceedings - 2nd International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom 2010, Kottayam, 2010, pp. 381-385.