Publication Type:

Conference Paper

Source:

Proceedings - 2nd International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom 2010, Kottayam, p.243-247 (2010)

ISBN:

9780769542010

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-78651430213&partnerID=40&md5=913dd397b2cb69850174edc6c457d412

Keywords:

Adaptive neuro fuzzy system, Automation, Commerce, Data reduction, Data sets, Decision trees, Dimensionality, Dimensionality reduction, Dimensionality reduction techniques, Feature extraction, Feature selection, Feature sets, Finance, financial forecasting, Forecasting, Fuzzy logic, Fuzzy sets, Hybrid neuro-fuzzy system, Hybrid systems, Neuro-Fuzzy, Neurofuzzy system, Prediction accuracy, Prediction systems, Sensex, Sensitive index, Stand -alone, Stock exchange, Stock market, Stock market model, Stock market prediction, System use, Technical analysis

Abstract:

Stock market prediction is an important area of financial forecasting, which is of great interest to stock investors, 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 prediction system is proposed. The proposed system uses technical analysis (traditionally used by stock traders) for feature extraction and decision tree for feature selection. Dimensionality reduction is carried out using fifteen different dimensionality reduction techniques. The dimensionality reduction technique producing the best prediction accuracy is selected to produce the reduced dataset. The reduced dataset is then applied to the adaptive neuro-fuzzy system for the next-day stock market prediction. The neuro-fuzzy system forms the stock market model adaptively, based on the features present in the reduced dataset. The proposed system is tested on the Bombay Stock Exchange sensitive index (BSE-SENSEX). 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.

Notes:

cited By (since 1996)1; Conference of org.apache.xalan.xsltc.dom.DOMAdapter@14a7965 ; Conference Date: org.apache.xalan.xsltc.dom.DOMAdapter@4067d68c Through org.apache.xalan.xsltc.dom.DOMAdapter@6136a25c; Conference Code:83357

Cite this Research Publication

B. Sujithra, B.B. Nair, Minuvarthini, M., and Mohandas, V. P., “Stock market prediction using a hybrid neuro-fuzzy system”, in Proceedings - 2nd International Conference on Advances in Recent Technologies in Communication and Computing, ARTCom 2010, Kottayam, 2010, pp. 243-247.