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Autor
Juszczuk Przemysław (University of Economics in Katowice, Poland), Kruś Lech (Polish Academy of Sciences)
Tytuł
Supporting Multicriteria Fuzzy Decisions on the Forex Market
Źródło
Multiple Criteria Decision Making / University of Economics in Katowice, 2017, vol. 12, s. 60-74, rys., tab., bibliogr. 19 poz.
Słowa kluczowe
Obrót dewizowy, Systemy transakcyjne, Zbiory rozmyte
Foreign exchange, Transaction systems, Fuzzy sets
Uwagi
summ.
Abstrakt
This paper deals with decisions made by a decision maker using technical analysis on the Forex market. For a number of currency pairs on the market the decision maker obtains buy or sell signals from transaction systems using technical analysis indicators. The signal is generated only when the assumed conditions are satised for a given indicator. The information characterizing every market situation and presented to the decision maker is binary: he either obtains the signal or does not. In this paper a fuzzy multicriteria approach is proposed to extend and valuate information for the analysis of the market situation. The traditional approach with binary characterization of the market situations, referred to as a crisp approach, is replaced by a fuzzy approach, in which the strict conditions for which the crisp signal was generated are fuzzy. The eciency of a given currency pair is estimated using values from the range <0, 1> and is dened by the membership function for each technical indicator. The values calculated for dierent indicators are treated as criteria. The eciency of a given currency pair can be analyzed jointly for several indicators. The currency pairs are compared in the multicriteria space in which domination relations, describing preferences of the decision maker, are introduced. An algorithm is proposed which generates Pareto-optimal variants of currency pairs presented to the decision maker. The method proposed allows to extend the number of analyzed currency pairs, without signicantly increasing the computation time.(original abstract)
Dostępne w
Biblioteka Główna Uniwersytetu Ekonomicznego w Krakowie
Biblioteka Szkoły Głównej Handlowej w Warszawie
Pełny tekst
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Bibliografia
Pokaż
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  2. Cheol-Ho P., Irwin S.H. (2007), What Do We Know about the Pro_tability of Technical Analysis? Journal of Economic Surveys, 21(4), 786-826.
  3. Greco S., Matarazzo B., S lowi_nski R. (2002), Multicriteria Classi_cation by Dominance-based Rough Set Approach, Handbook of Data Mining and Knowledge Discovery, Oxford University Press, New York.
  4. Hirabayashi A., Aranha C., Iba H. (2009), Optimization of the Trading Rule in Foreign Exchange Using Genetic Algorithm, Proceedings of the 11th Annual conference on Genetic and evolutionary computation GECCO '09, 1529-1536.
  5. Holt C.C. (2004), Forecasting Seasonals and Trends by Exponentially Weighted Moving Averages, International Journal of Forecasting, 20(1), 5-10.
  6. Juszczuk P., Kaliszewski I., Podkopaev D., Hsu-Shih S. (2016), Market Collective Wisdom Discovery for Portfolio Investments, International Journal of Information and Management Sciences, 27, 87-102.
  7. Kablan A. (2009), Adaptive Neuro Fuzzy Inference Systems for High Frequency Financial Trading and Forecasting, Advanced Engineering Computing and Applications in Sciences, ADVCOMP '09.
  8. Konarzewska-Guba la E. (1989), BIPOLAR: Multiple Criteria Decision Aid Using Bipolar Reference System, LAMSADE, Cashier et Documents, 56, Paris.
  9. Lai K.K., Yu L., Wang S. (2005), A Neural Network and Web-Based Decision Support System for Forex Forecasting and Trading, Data Mining and Knowledge Management, Lecture Notes in Computer Science, 3327, Springer.
  10. Liu Z., Xiao D. (2009), An Automated Trading System with Multi-indicator Fusion Based on D-S Evidence Theory in Forex Market, Fuzzy Systems and Knowledge Discovery, FSKD'09.
  11. McLeod G. (2014), Forex Market Size: A Traders Advantage, dailyFx, January 24, https://www.dailyfx.com/forex/education/trading (access: 25.04.2017).
  12. Nassirtoussi A.K., Aghabozorgi S., Wah T.Y., Ling Ngo D.C. (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.
  13. Patel M. (2010), Trading with Ichimoku Clouds: The Essential Guide to Ichimoku Kinko Hyo Technical Analysis, Wiley & Sons.
  14. Serbera J.P., Paumard P. (2016), The Fall of High-frequency Trading: A Survey of Competition and Pro_ts, Research in International Business and Finance, 36, 271-287.
  15. Slany K. (2009), Towards the Automatic Evolutionary Prediction of the FOREX Market Behaviour, Adaptive and Intelligent Systems, ICAIS '09.
  16. Wilder W. (1978), New Concepts in Technical Trading Systems, Trend Research.
  17. Yao J., Tan Ch.L. (2000), A Case Study on Using Neural Networks to Perform Technical Forecasting of Forex, Neurocomputing, 34(1-4), 79-98.
  18. Yu L., Lai K.K., Wang S. (2005), Designing a Hybrid AI System as a Forex Trading Decision Support Tool, Tools with Arti_cial Intelligence, ICTAI '05.
  19. (www 1) Investopedia, http://www.investopedia.com/articles/trading/05/041805.asp (access: 10.04.2017).
Cytowane przez
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ISSN
2084-1531
Język
eng
URI / DOI
http://dx.doi.org/10.22367/mcdm.2017.12.05
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