Enhancing Trading Strategies: Mandani Fuzzy Logic Forecasting for Borsa Istanbul Stocks Using Important Indicators
DOI:
https://doi.org/10.22399/ijcesen.695Keywords:
Financial Forecast, Fuzzy Logic, Technical Analysis, MamdaniAbstract
Recent years have seen significant financial market advancements, predicting stock or crypto exchange prices is a complex and risky process. Developments in the financial world are becoming increasingly interesting, especially for traders and investors who want to maximise profits. Nowadays, financial forecasting analysis is changing as conditions change and popular methods are preferred instead of traditional methods. Current changes and developments in the markets have become very important with the fuzzy logic method and the selection of indicators. In this study, contrary to the existing indicators, significant success was achieved with the 6 most popular indicators (RSI, SO, MACD, OBV, BB, CCI). Since each indicator has its pros and cons, these aspects are balanced with the mandani fuzzy logic method. This study provides forecasting analysis with mandani fuzzy logic method to facilitate the operation of 655 companies listed in Borsa Istanbul (BIST). FROTO stock data belonging to Ford Otosan company on BIST is used as data. This study aims to enable traders and investors to maximize their profits or increase their portfolios. The most accurate results were obtained using membership functions created for the indicators and 34 rules created using the Mamdani fuzzy method.
References
Vanstone, Bruce; Hahn, Tobias Designing stock market trading systems. With and without soft computing , 2010. ISBN 1906659583, 9781906659585
BROCK, WILLIAM; LAKONISHOK, JOSEF; LeBARON, BLAKE (1992): Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance 47 (5), pp. 1731–1764. DOI: 10.1111/j.1540- 6261.1992.tb04681.x.
Mallikarjuna, M.; Rao, R. Prabhakara (2019): Evaluation of forecasting methods from selected stock market returns. Financ Innov 5 (1). DOI: 10.1186/s40854-019-0157-x.
Dourra, H., & Siy, P. (2002). Investment using technical analysis and fuzzy logic. Fuzzy sets and systems, 127(2), 221-240.
Ahmad, M., Soeparno, H., & Napitupulu, T. A. (2020, October). Stock Trading Alert: With fuzzy knowledge-based systems and technical analysis. In 2020 International Conference on Information Technology Systems and Innovation (ICITSI) (pp. 155-160). IEEE.
D. Bernardo, H. Hagras, and E. Tsang, (2013). A genetic type-2 fuzzy logic based system for the generation of summarized linguistic predictive models for financial applications, Soft Computing, 17(12);2185-2201.
W. J. Banks and P. L. Abad, (1994). On the performance of linear programming heuristics applied on a quadratic transformation in the classification problem, European Journal of Operational Research, 72(1);23-28.
J. H. Min and Y.-C. Lee, (2005). Bankruptcy prediction using support vector machine with optimal choice of kernel function parameters, Expert systems with applications, 28(4);603-614,
K. Y. Tam, (1991). Neural network models and the prediction of bank bankruptcy, Omega, 19(5),429-445.
P. R. Kumar and V. Ravi, (2007) Bankruptcy prediction in banks and firms via statistical and intelligent techniques–A review, European journal of operational research, 180(1);1-28, 2007.
L.A. Zadeh, (1965). Fuzzy sets, Information and Control, 8(3);338-353, https://doi.org/10.1016/S0019-9958(65)90241-X.
W. Chen, Z. Fei, X. Zhao and S. Ren, (2022). Event–triggered asynchronous control for switched T–S fuzzy systems based on looped functionals, J Franklin Inst 359 (12), 6311–6335.
Gradojevic, N., & Gençay, R. (2013). Fuzzy logic, trading uncertainty and technical trading. Journal of Banking & Finance, 37(2), 578-586.
E. Özer, N. Sevinçkan and E. Demiroğlu, "Comparative Analysis of Computational Intelligence Techniques in Financial Forecasting: A Case Study on ANN and ANFIS Models," 2024 32nd Signal Processing and Communications Applications Conference (SIU), Mersin, Turkiye, 2024, pp. 1-4, doi: 10.1109/SIU61531.2024.10600769.
Pei-Chann Chang, Jheng-Long Wu, Jyun-Jie Lin, (2016). A Takagi–Sugeno fuzzy model combined with a support vector regression for stock trading forecasting, Applied Soft Computing, 8,831-842, https://doi.org/10.1016/j.asoc.2015.10.030.
Silvia Muzzioli & Bernard De Baets, (2011). Assessing the information content of option-based volatility forecasts using fuzzy regression methods, Department of Economics 0669, University of Modena and Reggio E., Faculty of Economics "Marco Biagi".
Vanstone, Bruce; Hahn, Tobias (2010): Designing stock market trading systems. With and without soft computing / by Bruce Vanstone, Tobias Hahn. Petersfield: Harriman House.
BROCK, WILLIAM; LAKONISHOK, JOSEF; LeBARON, BLAKE (1992): Simple Technical Trading Rules and the Stochastic Properties of Stock Returns. The Journal of Finance 47 (5), pp. 1731–1764. DOI: 10.1111/j.1540- 6261.1992.tb04681.x.
Mallikarjuna, M.; Rao, R. Prabhakara (2019): Evaluation of forecasting methods from selected stock market returns. Financ Innov 5 (1). DOI: 10.1186/s40854-019-0157-x.
V. Suthiponpisal and D. Tancharoen, (2024) A Comparative Evaluation of Noise Reduction Versus Data Normalization Techniques in Stock Market Prediction Using Transformer Models, 8th International Conference on Information Technology (InCIT), Chonburi, Thailand, 2024, pp. 775-780, doi: 10.1109/InCIT63192.2024.10810587.
A. Das, S. De, T. Mukherjee, M. Dey and K. Das Ghosh, (2024) Forecasting Bank ROE with Gradient Boosting: A Machine Learning Approach, 2024 5th International Conference on Data Intelligence and Cognitive Informatics (ICDICI), Tirunelveli, India, pp.448-452, doi:10.1109/ICDICI62993.2024.1 0810845.
S. Premsundar, V. Prabhu H and V. N Bahadurdesai, (2024) Deep Learning Model for Option Pricing - Review, 2024 8th International Conference on Computational System and Information Technology for Sustainable Solutions (CSITSS), Bengaluru, India, , pp. 1-6, doi: 10.1109/CSITSS64042.2024.10816734.
T. Islam and K. Kapinchev, (2024). Evaluation of the Effectiveness of Technical Indicators, 2024 17th International Conference on Signal Processing and Communication System (ICSPCS), Surfers Paradise, Australia, 2024, pp. 1-6, doi: 10.1109/ICSPCS63175.2024.10815753.
R. Logesh Babu, K. Tamilselvan, N. Purandhar, Tatiraju V. Rajani Kanth, R. Prathipa, & Ponmurugan Panneer Selvam. (2025). Adaptive Computational Intelligence Algorithms for Efficient Resource Management in Smart Systems. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.836
S.D.Govardhan, Pushpavalli, R., Tatiraju.V.Rajani Kanth, & Ponmurugan Panneer Selvam. (2024). Advanced Computational Intelligence Techniques for Real-Time Decision-Making in Autonomous Systems. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.591
Bhanu Sekhar OBBU, & Zamrooda JABEEN. (2024). Integrated Fuzzy Cognitive Map and Chaotic Particle Swarm Optimization for Risk Assessment of Ischemic Stroke. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.540
S. Krishnaveni, Devi, R. R., Ramar, S., & S.S.Rajasekar. (2025). Novel Architecture For EEG Emotion Classification Using Neurofuzzy Spike Net. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.829
D. Neguja, & A. Senthilrajan. (2024). An improved Fuzzy multiple object clustering in remodeling of roofs with perceptron algorithm. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.773
M. Venkateswarlu, K. Thilagam, R. Pushpavalli, B. Buvaneswari, Sachin Harne, & Tatiraju.V.Rajani Kanth. (2024). Exploring Deep Computational Intelligence Approaches for Enhanced Predictive Modeling in Big Data Environments. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.676
S.P. Lalitha, & A. Murugan. (2024). Performance Analysis of Priority Generation System for Multimedia Video using ANFIS Classifier. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.707
RamaKishore K., Ramprasad C.H., & Varma P.L.N. (2024). Description of Regular m-Bipolar Fuzzy Graphs. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.605
Hadi Athab Hamed, & Ahmed Kareem ABDULLAH. (2025). M-ary Pulse Ampplitude Modulation Recognition Using Discrete Meyer Wavelet and Reverse Biorthogonal Wavelet. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.749
Nasti, S. M., Najar, Z. A., & Chishti, M. A. (2024). A Comprehensive Review of Path Planning Techniques for Mobile Robot Navigation in Known and Unknown Environments. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.797
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 International Journal of Computational and Experimental Science and Engineering

This work is licensed under a Creative Commons Attribution 4.0 International License.