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Applied Economics Letters Volume 14, 2007 - Issue 1
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Original Articles

A neurofuzzy model for stock market trading

Stelios D. Bekiros Department of Accounting & Finance, Athens University of Economics & Business, 76 Patission str, GR 104 34 Athens, Greece E-mail: [email protected]
Pages 53-57 | Published online: 13 Dec 2006

Citations (7)

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AJ Hoffman. (2012) Stock return anomalies: Evidence from the Johannesburg Stock Exchange. Investment Analysts Journal 41:75, pages 21-41.
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Articles from other publishers (6)

Mohammad Arashi & Mohammad Mahdi Rounaghi. (2022) Analysis of market efficiency and fractal feature of NASDAQ stock exchange: Time series modeling and forecasting of stock index using ARMA-GARCH model. Future Business Journal 8:1.
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Aurthur Vimalachandran Thomas Jayachandran. (2022) The financial crash of 2020 and the retail trader’s boon: a correlation between sentiment and technical analysis. SN Business & Economics 2:6.
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Ritika Chopra & Gagan Deep Sharma. (2021) Application of Artificial Intelligence in Stock Market Forecasting: A Critique, Review, and Research Agenda. Journal of Risk and Financial Management 14:11, pages 526.
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George S. Atsalakis, Emmanouil M. Dimitrakakis & Constantinos D. Zopounidis. (2011) Elliott Wave Theory and neuro-fuzzy systems, in stock market prediction: The WASP system. Expert Systems with Applications 38:8, pages 9196-9206.
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Jieh-Haur Chen, Li-Ren Yang, Mu-Chun Su & Jia-Zheng Lin. (2010) A rule extraction based approach in predicting derivative use for financial risk hedging by construction companies. Expert Systems with Applications 37:9, pages 6510-6514.
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John Seiffertt & Donald Wunsch. (2008) Intelligence in Markets: Asset Pricing, Mechanism Design, and Natural Computation [Technology Review]. IEEE Computational Intelligence Magazine 3:4, pages 27-30.
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