Journal of Fuzzy Extension and Applications

Quarterly Publication

About Journal

Aims and Scope

Open Access Policy

Editorial Staff

Related Links

Journal Forms

Journal Metrics

Financial Policies

Crossmark Policy

Complaints

News

FAQ

Open Special Issues

Propose a Special Issue

Publication Ethics

Journal Policies

Editorial Board

Peer Review Policies

Guide for Reviewers

Reviewers (2022)

Reviewers (2021)

Reviewers (2020)

Join Us As Reviewer

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

Modeling fuzzimetric cognition of technical analysis decisions: reducing emotional trading

Document Type : Research Paper

Author

Department of Information Technology and Operations Management, Lebanese American University (LAU), Lebanon.

Abstract

Stock traders' forecasting strategies are mainly dependent on Technical Analysis (TA) indicators. However, some traders would follow their intuition and emotional aspects when trading instead of following the mathematically solid forecasting techniques of TA(s). The objective of this paper is to help traders to rationalize their choices by generating the maximum and minimum tolerances of possible prices (termed in this paper as "fuzzy spectrum") and hence reducing their "emotional" trading decisions. This would be an important aspect towards avoiding an undesired outcome. Fuzzy logic has been used in this paper to identify such tolerances based on the most popular TA(s). Fuzzification of these TA(s) was used via a modular approach of fuzzy logic and by adopting "fuzzimetric sets" described in this paper to achieve the "fuzzy spectrum" of forecasted price tolerances when buying and selling decisions. Experimental results show the success of developing the "fuzzy spectrum" based on the "fuzzy" tolerances discovered from the TA(s) outputs. As a result, this paper contributes towards a better "rationalized" decision making when it comes to buying and selling stocks in this kind of industry.

Keywords

Main Subjects

References
  1. Fama, E. F. (1965). The behavior of stock-market prices. The journal of business, 38(1), 34-105. https://www.jstor.org/stable/2350752
  2. Fama, E. F. (1970). Efficient capital markets: a review of theory and empirical work. The journal of finance, 25(2), 383-417. https://doi.org/10.2307/2325486
  3. Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47, 263–291.
  4. Chuang, W. I., & Lee, B. S. (2006). An empirical evaluation of the overconfidence hypothesis. Journal of banking & finance, 30(9), 2489-2515. https://doi.org/10.1016/j.jbankfin.2005.08.007
  5. Ahmad, M., & Shah, S. Z. A. (2020). Overconfidence heuristic-driven bias in investment decision-making and performance: mediating effects of risk perception and moderating effects of financial literacy. Journal of economic and administrative sciences, 38(1), 60-90. https://doi.org/10.1108/JEAS-07-2020-0116
  6. Chopra, R., & Sharma, G. D. (2021). Application of artificial intelligence in stock market forecasting: a critique, review, and research agenda. Journal of risk and financial management, 14(11), 526. https://doi.org/10.3390/jrfm14110526
  7. Khayamim, A., Mirzazadeh, A., & Naderi, B. (2018). Portfolio rebalancing with respect to market psychology in a fuzzy environment: a case study in Tehran Stock Exchange. Applied soft computing, 64, 244-259. https://doi.org/10.1016/j.asoc.2017.11.044
  8. Park, C. H., & Irwin, S. H. (2007). What do we know about the profitability of technical analysis?. Journal of economic surveys, 21(4), 786-826. https://doi.org/10.1111/j.1467-6419.2007.00519.x
  9. Gradojevic, N., & Gençay, R. (2013). Fuzzy logic, trading uncertainty and technical trading. Journal of banking & finance, 37(2), 578-586. https://doi.org/10.1016/j.jbankfin.2012.09.012
  10. Escobar, A., Moreno, J., & Múnera, S. (2013). A technical analysis indicator based on fuzzy logic. Electronic notes in theoretical computer science, 292, 27-37. https://doi.org/10.1016/j.entcs.2013.02.003
  11. Cremonesi, P., Francalanci, C., Poli, A., Pagano, R., Mazzoni, L., Maggioni, A., & Elahi, M. (2018). Social network based short-term stock trading system. Available at arXiv:1801.05295
  12. Hao, P. Y., Kung, C. F., Chang, C. Y., & Ou, J. B. (2021). Predicting stock price trends based on financial news articles and using a novel twin support vector machine with fuzzy hyperplane. Applied soft computing, 98, 106806. https://doi.org/10.1016/j.asoc.2020.106806
  13. Dong, Q., & Ma, X. (2021). Enhanced fuzzy time series forecasting model based on hesitant differential fuzzy sets and error learning. Expert systems with applications, 166, 114056. https://doi.org/10.1016/j.eswa.2020.114056
  14. Pertiwi, T., Yuniningsih, Y., & Anwar, M. (2019). The biased factors of investor’s behavior in stock exchange trading. Management science letters, 9(6), 835-842. DOI: 5267/j.msl.2019.3.005
  15. Vaščák, J. (2012). Adaptation of fuzzy cognitive maps by migration algorithms. Kybernetes, 41(3/4), 429-443. https://doi.org/10.1108/03684921211229505
  16. Richards, D. W., & Willows, G. D. (2019). Monday mornings: individual investor trading on days of the week and times within a day. Journal of behavioral and experimental finance, 22, 105-115. https://doi.org/10.1016/j.jbef.2019.02.009
  17. Rupande, L., Muguto, H. T., & Muzindutsi, P. F. (2019). Investor sentiment and stock return volatility: evidence from the johannesburg stock exchange. Cogent economics & finance, 7(1), 1600233.
  18. Ototsky, P., & Manenkov, S. (2011). Cognitive centres: technology for designing the future: methodology and implementation experience. Kybernetes, 40(3/4), 528-535. https://doi.org/10.1108/03684921111133728
  19. Marco, J. P., Arbeloa, F. J. S., & Bagdasari, E. C. (2017). Combining cognition and emotion in virtual agents. Kybernetes, 46(06), 933-946. https://doi.org/10.1108/K-11-2016-0340
  20. Kouatli, I. (2018). Emotions in the cloud: a framework architecture for managing emotions with an example of emotional intelligence management for cloud computing organisations. International journal of work organisation and emotion, 9(2), 187-208. https://www.inderscienceonline.com/doi/abs/10.1504/IJWOE.2018.093317
  21. Duxbury, D., Gärling, T., Gamble, A., & Klass, V. (2020). How emotions influence behavior in financial markets: a conceptual analysis and emotion-based account of buy-sell preferences. The european journal of finance, 26(14), 1417-1438. https://doi.org/10.1080/1351847X.2020.1742758
  22. Ge, Y., Qiu, J., Liu, Z., Gu, W., & Xu, L. (2020). Beyond negative and positive: exploring the effects of emotions in social media during the stock market crash. Information processing & management, 57(4), 102218. https://doi.org/10.1016/j.ipm.2020.102218
  23. Yuan, F. (2020). Psychological cognition and preference selection in the decision-making process of financial investment. Revista argentina de clínica psicológica, 29(1), 1222. DOI: 24205/03276716.2020.175
  24. Sun, J., Huang, Q., & Li, X. (2019). Determination of temporal stock investment styles via biclustering trading patterns. Cognitive computation, 11(6), 799-808. https://doi.org/10.1007/s12559-019-9626-9
  25. Maciel, L., & Ballini, R. (2019). Fuzzy rule-based modeling for interval-valued data: an application to high and low stock prices forecasting. In Predictive maintenance in dynamic systems(pp. 403-424). Springer, Cham. https://doi.org/10.1007/978-3-030-05645-2_14
  26. Osman, I. H., & Anouze, A. L. (2014). A cognitive analytics management framework (CAM-Part 3): critical skills shortage, higher education trends, education value chain framework, government strategy. In Handbook of research on strategic performance management and measurement using data envelopment analysis(pp. 190-234). IGI Global. DOI: 4018/978-1-4666-4474-8.ch003
  27. Osman, I. H., Anouze, A. L., Irani, Z., Lee, H., Medeni, T. D., & Weerakkody, V. (2019). A cognitive analytics management framework for the transformation of electronic government services from users’ perspective to create sustainable shared values. European journal of operational research, 278(2), 514-532. https://doi.org/10.1016/j.ejor.2019.02.018
  28. Cabrera-Paniagua, D., & Rubilar-Torrealba, R. (2021). A novel artificial autonomous system for supporting investment decisions using a Big Five model approach. Engineering applications of artificial intelligence, 98, 104107. https://doi.org/10.1016/j.engappai.2020.104107
  29. Kareem, S. A. (2020). Providing a model for predicting the financial behavior of investors in the iranian stock market. Eurasian journal of management & social sciences,1(3), 20-33. DOI: 23918/ejmss. v1i3p20
  30. Kouatli, I., & Arayssi, M. (2021, August). A fuzzimetric predictive analytics model to reduce emotional stock trading. International conference on intelligent and fuzzy systems(pp. 482-489). Springer, Cham. https://doi.org/10.1007/978-3-030-85577-2_57
  31. Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
  32. Kouatli, I. (2018). Fuzzimetric sets: an integrated platform for both types of fuzzy sets. Frontiers in artificial intelligence and applications (FAIA), 309. DOI: 3233/978-1-61499-927-0-150
  33. Kouatli, I. (2018). Fuzzimetric employee evaluations system (FEES): a multivariable-modular approach. Journal of intelligent & fuzzy systems, 35(4), 4717-4729.
  34. Kouatli, I. (2019, June). Fuzziness control of fuzzimetric sets. 2019 IEEE international conference on fuzzy systems (FUZZ-IEEE)(pp. 1-5). IEEE. DOI: 1109/FUZZ-IEEE.2019.8858828
  35. García-Vico, Á. M., González, P., Carmona, C. J. & Del Jesus, M. J. (2019). A big data approach for the extraction of fuzzy emerging patterns. Cogn comput. https://doi.org/10.1007/s12559-018-9612-7
  36. Liu, P. & Qin, X. Cogn Comput (2019). A new decision-making method based on interval-valued linguistic intuitionistic fuzzy information. Cogn comput. https://doi.org/10.1007/s12559-018-9597-2
  37. Kouatli, I. M. (1994). A simplified fuzzy multivariable structure in a manufacturing environment. Journal of intelligent manufacturing, 5(6), 365-387. https://doi.org/10.1007/BF00123657
  38. Kouatli, I., & Yunis, M. (2021, December). A guide to stock-trading decision making based on popular technical indicators. 2021 international conference on decision aid sciences and application (DASA)(pp. 283-287). IEEE. DOI: 1109/DASA53625.2021.9682337
  39. Mamdani, E. H., & Assilian, S. (1975). An experiment in linguistic synthesis with a fuzzy logic controller. International journal of man-machine studies, 7(1), 1-13. https://doi.org/10.1016/S0020-7373(75)80002-2
  40. Sugeno, M. (1985). Industrial applications of fuzzy control. Elsevier Science Inc..
  41. Babuska, R. (2007). [Review of the book Complexity management in fuzzy systems: a rule base compression approach]. IEEE computational intelligence magazine, 2(4), 42-43. DOI: 1109/MCI.2007.906693
  42. Kouatli, I. (2008). Definition and selection of fuzzy sets in genetic‐fuzzy systems using the concept of fuzzimetric arcs. Kybernetes, 37(1), 166-181. https://doi.org/10.1108/03684920810851069
  43. Carrera, D. A., & Mayorga, R. V. (2008). Supply chain management: a modular fuzzy inference system approach in supplier selection for new product development. Journal of intelligent manufacturing, 19(1), 1-12. https://doi.org/10.1007/s10845-007-0041-9
  44. Junior, F. R. L., Osiro, L., & Carpinetti, L. C. R. (2013). A fuzzy inference and categorization approach for supplier selection using compensatory and non-compensatory decision rules. Applied soft computing, 13(10), 4133-4147. https://doi.org/10.1016/j.asoc.2013.06.020
  45. Amindoust, A., Ahmed, S., Saghafinia, A., & Bahreininejad, A. (2012). Sustainable supplier selection: a ranking model based on fuzzy inference system. Applied soft computing, 12(6), 1668-1677. https://doi.org/10.1016/j.asoc.2012.01.023
  46. Lin, L. Z., & Hsu, T. H. (2012). A modular fuzzy inference system approach in integrating qualitative and quantitative analysis of store image. Quality & quantity, 46(6), 1847-1864. https://doi.org/10.1007/s11135-011-9561-7
  47. Melin, P., Sánchez, D., & Castillo, O. (2012). Genetic optimization of modular neural networks with fuzzy response integration for human recognition. Information sciences, 197, 1-19. https://doi.org/10.1016/j.ins.2012.02.027
  48. Kouatli, I. (2014, August). Complexity avoidance using biological resemblance of modular multivariable structure. 2014 10th international conference on natural computation (ICNC)(pp. 508-513). IEEE. DOI: 1109/ICNC.2014.6975887
  49. Saaty, T. L. (1994). How to make a decision: the analytic hierarchy process. Interfaces, 24(6), 19-43. https://doi.org/10.1287/inte.24.6.19
  50. Pounder, G. A., Ellis, R. L., & Fernandez-Lopez, G. (2017). Cognitive function synthesis: preliminary results. Kybernetes, 46(2), 272-290. https://doi.org/10.1108/K-01-2015-0038
  51. Mella, P. (2017). The unexpected cybernetics life of collectivities: the combinatory systems approach. Kybernetes, 46(7), 1086-1111. https://doi.org/10.1108/K-02-2017-0058
  52. Lepskiy, V. (2017). Evolution of cybernetics: philosophical and methodological analysis. Kybernetes, 47(2), 249-261. https://doi.org/10.1108/K-03-2017-0120
  53. Gehrig, T., & Menkhoff, L. (2006). Extended evidence on the use of technical analysis in foreign exchange. International journal of finance & economics, 11(4), 327-338. https://doi.org/10.1002/ijfe.301
  54. Czerwonka, M. (2019). Cultural, cognitive and personality traits in risk-taking behaviour: evidence from Poland and the United States of America. Economic research-ekonomska istraživanja, 32(1), 894-908. https://doi.org/10.1080/1331677X.2019.1588766
  55. Bollinger, J. (2001). Bollinger on bollinger bands. McGraw Hill Education.
Journal of Fuzzy Extension and Applications
Volume 3, Issue 1
March 2022
Pages 45-63
Files
Share
How to cite
Statistics