Treffer: Stock trading rule discovery with an evolutionary trend following model

Title:
Stock trading rule discovery with an evolutionary trend following model
Authors:
YONG HU, BIN FENG, XIANGZHOU ZHANG, NGAI, E. W. T, MEI LIU
Source:
Expert systems with applications. 42(1):212-222
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, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Intelligence artificielle, Artificial intelligence, Apprentissage et systèmes adaptatifs, Learning and adaptive systems, Algorithme apprentissage, Learning algorithm, Algoritmo aprendizaje, Algorithme évolutionniste, Evolutionary algorithm, Algoritmo evoluciónista, Analyse conceptuelle, Conceptual analysis, Análisis conceptual, Analyse tendance, Trend analysis, Análisis tendencia, Apprentissage renforcé, Reinforcement learning, Aprendizaje reforzado, Arbre décision, Decision tree, Arbol decisión, Bourse valeurs, Stock exchange, Bolsa valores, Court terme, Short term, Corto plazo, Coût transaction, Transaction cost, Coste transacción, Découverte connaissance, Knowledge discovery, Descubrimiento conocimiento, Dérive génétique, Genetic drift, Deriva genética, Economie, Economy, Economía, Efficacité, Efficiency, Eficacia, Fouille donnée, Data mining, Busca dato, Indice économique, Economic index, Indice económico, Intelligence artificielle, Artificial intelligence, Inteligencia artificial, Investissement, Investment, Inversión, Long terme, Long term, Largo plazo, Marché financier, Financial market, Mercado financiero, Marché à la baisse, Bear market, Mercado bajista, Marché à la hausse, Bull market, Mercado alcista, Modèle hybride, Hybrid model, Modelo híbrido, Modélisation, Modeling, Modelización, Poursuite modèle, Model following, Seguimiento modelo, Recherche et développement, Research and development, Investigación desarrollo, Réseau neuronal, Neural network, Red neuronal, Résultat expérimental, Experimental result, Resultado experimental, Transmission en continu, Streaming, Transmisión fluyente, Algorithme sans mémoire, Oblivious algorithm, Algoritmo sin Memória, Concept drift, Evolutionary trend following algorithm (eTrend), Trading rule discovery, eXtended Classifier System (XCS)
Document Type:
Fachzeitschrift Article
File Description:
text
Language:
English
Author Affiliations:
Institute of Business Intelligence and Knowledge Discovery, Guangdong University of Foreign Studies, Sun Yat-sen University, Guangzhou 510006, China
School of Management, Guangdong University of Foreign Studies, Higher Education Mega Center, Guangzhou 510006, China
School of Business, Sun Yat-sen University, No. 135, Xingang Xi Road, Guangzhou 510275, China
Department of Management and Marketing, The Hong Kong Polytechnic University, Kowloon, Hong-Kong
University of Kansas Medical Center, Kansas City, KS 66160, United States
ISSN:
0957-4174
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=28843395
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
Notes:
Computer science; theoretical automation; systems

Operational research. Management
Accession Number:
edscal.28843395
Database:
PASCAL Archive

Weitere Informationen

Evolutionary learning is one of the most popular techniques for designing quantitative investment (QI) products. Trend following (TF) strategies, owing to their briefness and efficiency, are widely accepted by investors. Surprisingly, to the best of our knowledge, no related research has investigated TF investment strategies within an evolutionary learning model. This paper proposes a hybrid long-term and short-term evolutionary trend following algorithm (eTrend) that combines TF investment strategies with the extended Classifier Systems (XCS). The proposed eTrend algorithm has two advantages: (1) the combination of stock investment strategies (i.e., TF) and evolutionary learning (i.e., XCS) can significantly improve computation effectiveness and model practicability, and (2) XCS can automatically adapt to market directions and uncover reasonable and understandable trading rules for further analysis, which can help avoid the irrational trading behaviors of common investors. To evaluate eTrend, experiments are carried out using the daily trading data stream of three famous indexes in the Shanghai Stock Exchange. Experimental results indicate that eTrend outperforms the buy-and-hold strategy with high Sortino ratio after the transaction cost. Its performance is also superior to the decision tree and artificial neural network trading models. Furthermore, as the concept drift phenomenon is common in the stock market, an exploratory concept drift analysis is conducted on the trading rules discovered in bear and bull market phases. The analysis revealed interesting and rational results. In conclusion, this paper presents convincing evidence that the proposed hybrid trend following model can indeed generate effective trading guidance for investors.