Treffer: Developing an approach to evaluate stocks by forecasting effective features with data mining methods
Title:
Developing an approach to evaluate stocks by forecasting effective features with data mining methods
Authors:
Source:
Expert systems with applications. 42(3):1325-1339
Publisher Information:
Amsterdam: Elsevier, 2015.
Publication Year:
2015
Physical Description:
print, 3/4 p
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Recherche operationnelle. Gestion, Operational research. Management science, Recherche opérationnelle et modèles formalisés de gestion, Operational research and scientific management, Sélection et gestion de portefeuilles, Portfolio theory, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Informatique théorique, Theoretical computing, Algorithmique. Calculabilité. Arithmétique ordinateur, Algorithmics. Computability. Computer arithmetics, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Traitement des données. Listes et chaînes de caractères, Data processing. List processing. Character string processing, Telecommunications et theorie de l'information, Telecommunications and information theory, Théorie de l'information, du signal et des communications, Information, signal and communications theory, Théorie du signal et des communications, Signal and communications theory, Signal, bruit, Signal, noise, Détection, estimation, filtrage, égalisation, prédiction, Detection, estimation, filtering, equalization, prediction, Amas, Cluster, Montón, Analyse amas, Cluster analysis, Analisis cluster, Analyse donnée, Data analysis, Análisis datos, Bourse valeurs, Stock exchange, Bolsa valores, Classification, Clasificación, Critère sélection, Selection criterion, Criterio selección, Echange donnée informatisé, Electronic data interchange, Intercambio electrónico de datos, Filtre numérique, Digital filter, Filtro numérico, Fouille donnée, Data mining, Busca dato, Modèle hybride, Hybrid model, Modelo híbrido, Prévision, Forecasting, Previsión, Rendement, Yield, Rendimiento, Retour sur investissement, Return on investment, Retorno inversión, Marché valeurs, Stock markets, Mercado de valores, Classification algorithm, Feature selection, Function-based clustering method, Stock market
Document Type:
Fachzeitschrift
Article
File Description:
text
Language:
English
Author Affiliations:
Young Researcher Club Ardebil Branch, Islamic Azad University, Ardebil, Iran, Islamic Republic of
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran, Islamic Republic of
Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran, Islamic Republic of
ISSN:
0957-4174
Rights:
Copyright 2015 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems
Operational research. Management
Telecommunications and information theory
Operational research. Management
Telecommunications and information theory
Accession Number:
edscal.28928457
Database:
PASCAL Archive
Weitere Informationen
In this research, a novel approach is developed to predict stocks return and risks. In this three stage method, through a comprehensive investigation all possible features which can be effective on stocks risk and return are identified. Then. in the next stage risk and return are predicted by applying data mining techniques for the given features. Finally, we develop a hybrid algorithm, on the basis of filter and function-based clustering; the important features in risk and return prediction are selected then risk and return re-predicted. The results show that the proposed hybrid model is a proper tool for effective feature selection and these features are good indicators for the prediction of risk and return. To illustrate the approach as well as to train data and test, we apply it to Tehran Stock Exchange (TSE) data from 2002 to 2011.