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2019 | 14 | 29--43
Tytuł artykułu

A Fuzzy Multicriteria Approach for the Trading Systems on the Forex Market

Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper relates to the trading systems supporting traders making decision on the forex market. Typical trading systems using technical analysis generate a buy or sell signal when the technical indicator crosses a given oversell or overbought levels. The paper extends the approach in which the above strict crisp conditions are replaced by fuzzy relations. The indicators are treated not independently as it is in the typical systems but jointly. Currency pairs are compared in the muliticriteria space in which each criterion is defined by a membership function referring to a given indicator. New formulations of the membership functions for different indicators are proposed. General ideas of the algorithm generating non-dominated alternatives in the multicriteria space are presented. The algorithm has been implemented in an experimental system. Computational results for different time windows using real-world data from the forex market are presented and discussed. (original abstract)
Rocznik
Tom
14
Strony
29--43
Opis fizyczny
Twórcy
  • University of Economics in Katowice, Poland
autor
  • Polish Academy of Sciences
Bibliografia
  • Booth A., Gerding E., McGroarty F. (2014), Automated Trading with Performance Weighted Random Forests and Seasonality, Expert Systems with Applications, 41, 3651-3661.
  • Chaboud A.P., Chiquoine B., Hjalmarsson E., Vega C. (2014), Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market, The Journal of Finance, 69(5), 2045-2084.
  • Dourra H., Siy P. (2002), Investment Using Technical Analysis and Fuzzy Logic, Fuzzy Sets and Systems, 127, 221-240.
  • Gottschlich J., Hinz O. (2014), A Decision Support System for Stock Investment Recommendations Using Collective Wisdom, Decision Support Systems, 59, 52-62.
  • Juszczuk P., Kru_s L. (2017), Supporting Multicriteria Fuzzy Decisions on the Forex Market, Multiple Criteria Decision Making, 12, 60-74.
  • Kamruzzaman J., Sarker R.A. (2003), Comparing ANN Based Models with ARIMA for Prediction of Forex Rates, ASOR Bulletin, 22(2), 1-11.
  • Kirkpatrick II Ch. D., Dahlquist J.R. (2010), Technical Analysis: The Complete Resource for Financial Market Technicians, FT Press, Old Tappan, New Jersey.
  • Lu C.-C., Wu C.-H. (2009), Support Vector Machine Combined with GARCH Models for Call Option Price Prediction, 2009 International Conference on Arti_cial Intelligence and Computational Intelligence, Shanghai, China, 35-40.
  • Nassirtoussi A.E., Aghabozorgi S., Wah T.Y., Check D., Ngo L. (2015), Text Mining of News-headlines for FOREX Market Prediction: A Multi-layer Dimension Reduction Algorithm with Semantics and Sentiment, Expert Systems with Applications, 42(1), 306-324.
  • Neely C.J.,Weller P.A. (1999), Technical Trading Rules in the European Monetary System, Journal of International Money and Finance, 18, 429-458.
  • Neely C.J., Weller P.A., Dittmar R. (1997), Is Technical Analysis Pro_table in the Foreign Exchange Market? A Genetic Programming Approach, Journal of Financial and Quantitative Analysis, 32, 405-426.
  • Sermpinis G., Laws J., Karathanasopoulos A., Dunis C.L. (2012), Forecasting and Trading the EUR/USD Exchange Rate with Gene Expression and Psi Sigma Neural Networks, Expert Systems with Applications, 39, 8865-8877.
  • Sewell M.V., Yan W. (2008), Ultra High Frequency Financial Data, GECCO '08: Proceedings of the 10th Annual Conference Companion on Genetic and Evolutionary Computation, ACM Press, 1847-1849.
  • Wang Y.F. (2003), Mining Stock Price Using Fuzzy Rough Set System, Expert Systems with Applications, 24(1), 13-23.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171581776

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