Document Type : Research Paper
Department of Information Technology and Operations Management, Lebanese American University (LAU), Lebanon.
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.
- Cognitive modelling
- Fuzzy system
- Technical analysis
- Trading systems
- Stock trading optimization
- Fuzzimetric sets
- Fama, E. F. (1965). The behavior of stock-market prices. The journal of business, 38(1), 34-105. https://www.jstor.org/stable/2350752
- 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
- Kahneman, D., & Tversky, A. (1979). Prospect theory: an analysis of decision under risk. Econometrica, 47, 263–291.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Vaščák, J. (2012). Adaptation of fuzzy cognitive maps by migration algorithms. Kybernetes, 41(3/4), 429-443. https://doi.org/10.1108/03684921211229505
- 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
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
- 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
- Kouatli, I. (2018). Fuzzimetric employee evaluations system (FEES): a multivariable-modular approach. Journal of intelligent & fuzzy systems, 35(4), 4717-4729.
- 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
- 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
- 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
- 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
- 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
- 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
- Sugeno, M. (1985). Industrial applications of fuzzy control. Elsevier Science Inc..
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Lepskiy, V. (2017). Evolution of cybernetics: philosophical and methodological analysis. Kybernetes, 47(2), 249-261. https://doi.org/10.1108/K-03-2017-0120
- 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
- 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
- Bollinger, J. (2001). Bollinger on bollinger bands. McGraw Hill Education.