A tanulmány célja a tőzsdei előrejelzési módszerek fejlődésének bemutatása, különös tekintettel az ANFIS (Adaptive Neuro-Fuzzy Inference System) modellek alkalmazhatóságára. Az elemzés rávilágít arra, hogy a klasszikus technikai és fundamentális elemzési megközelítések korlátai miatt egyre nagyobb szerep jut a mesterséges intelligencia alapú rendszereknek. Az ANFIS modellek a nemlineáris és zajos pénzügyi adatok kezelésében kiemelkedő teljesítményt nyújtanak, miközben képesek ötvözni az emberi szakértelem és az adatalapú tanulás előnyeit. A kutatás alátámasztja, hogy a gondosan kiválasztott bemeneti változók mellett az ANFIS versenyképes előrejelzési pontosságot kínál, ugyanakkor a modell gyakorlati alkalmazása során elengedhetetlen a piaci realitások, mint például a kereskedési költségek figyelembevétele.
XIII. ÉVF. 2025. 4. SZÁM 39-46
DOI: 10.24387/CI.2025.4.7
A cikk megtekinthető: http://controllerinfo.hu/wp-content/uploads/2026/01/CI_2025_4_07.pdf
IRODALMI FELDOLGOZÁS
ABADOU, R. – COME, J. M. – CHASTANET, J. – MARCOUX, M. & QUINTARD, M. (2023): Fuzzy Set Characterization of Uncertainty (Fuzzy Variables). In SpringerBriefs in Applied Sciences and Technology (Vol. Part F1978, pp. 35-51). https://doi.org/10.1007/978-981-99-6241-9_4
AINSWORTH, M. & SHIN, Y. (2021): Plateau phenomenon in gradient descent training of relu networks: Explanation, quantification, and avoidance [Article]. SIAM Journal on Scientific Computing, 43(5), A3438-A3468. https://doi.org/10.1137/20M1353010
ASIF, R. – HU, Y. F. – ALI, M. – LI, J. P. & ABDO, K. (2021): Signal Classification for Safety Critical Aeronautical Communications for Anti-Jamming using Artificial Intelligence. AIAA/IEEE Digital Avionics Systems Conference – Proceedings,
ASWIN KUMER, S. V. – KANAKARAJA, P. – SAIRAM NADIPALLI, L. S. P. – RAMESH, N. V. K. & KOTAMRAJU, S. K. (2021): The Categorization of Artificial Intelligence (AI) Based on the Autonomous Vehicles and Its Other Applications. Lecture Notes in Networks and Systems,
BANDARA, M. N. RANASINGHE, R. M. – ARACHCHI, R. W. M. – SOMATHILAKA, C. G. – PERERA, S. & WIMALASURIYA, D. C. (2015): A Complex Event Processing Toolkit for Detecting Technical Chart Patterns. Proceedings – 2015 IEEE 29th International Parallel and Distributed Processing Symposium Workshops, IPDPSW 2015,
BARAK, S. – DAHOOIE, J. H. & TICHY, T. (2015): Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick [Article]. Expert Systems with Applications, 42(23), 9221-9235. https://doi.org/10.1016/j.eswa.2015.08.010
BARLYBAYEV, A. – ZHETKENBAY, L. – KARIMOV, D. & YERGESH, B. (2023): DEVELOPMENT NEURO-FUZZY MODEL TO PREDICT THE STOCKS OF COMPANIES IN THE ELECTRIC VEHICLE INDUSTRY [Article]. Eastern-European Journal of Enterprise Technologies, 4(4(124)), 72-87. https://doi.org/10.15587/1729-4061.2023.281138
BOYACIOGLU, M. A. & AVCI, D. (2010): An adaptive network-based fuzzy inference system (ANFIS) for the prediction of stock market return: The case of the Istanbul stock exchange [Article]. Expert Systems with Applications, 37(12), 7908-7912. https://doi.org/10.1016/j.eswa.2010.04.045
CAM, A. V. – TOSUNOGLU, B. & GURTAY, E. (2017): The role of financial ratios on evaluation of stock values: An application in Bist. In Research on Humanities and Social Sciences: Communication, Social Sciences, Arts (pp. 263-274). https://doi.org/10.3726/978-3-631-69829-7
CARBONNELLE, S. & De VLEESCHOUWER, C. (2019): Experimental study of the neuron-level mechanisms emerging from backpropagation. ESANN 2019 – Proceedings, 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning,
CHEN, Y. S. – CHENG, C. H. – CHIU, C. L. & HUANG, S. T. (2016): A study of ANFIS-based multi-factor time series models for forecasting stock index [Article]. Applied Intelligence, 45(2), 277-292. https://doi.org/10.1007/s10489-016-0760-8
CHIU, Y. J. – CHEN, K. C. & CHE, H. C. (2020): Does Patent Help to Build Investment Portfolio of China A-Shares under China-US Trade Conflict? [Article]. Mathematical Problems in Engineering, 2020, Article 7317480. https://doi.org/10.1155/2020/7317480
D’ANGELO, T. – CAUDO, D. – BLANDINO, A. – ALBRECHT, M. H. – VOGL, T. J. – GRUENEWALD, L. D. – GAETA, M. – YEL, I. – KOCH, V. – MARTIN, S. S. – LENGA, L. – MUSCOGIURI, G. – SIRONI, S. – MAZZIOTTI, S. & BOOZ, C. (2022): Artificial intelligence, machine learning and deep learning in musculoskeletal imaging: Current applications [Review]. Journal of Clinical Ultrasound, 50(9), 1414-1431. https://doi.org/10.1002/jcu.23321
GAI, F. (2021):When Artificial Intelligence Meets Daoism. In Intelligence and Wisdom: Artificial Intelligence Meets Chinese Philosophers (pp. 83-100). https://doi.org/10.1007/978-981-16-2309-7_6
GÁL, Z. (2016): Pénzügyi piacok a globális térben. https://doi.org/10.1556/9789630598118
GJERSTAD, S. & SMITH, V. L. (2009): MONETARY POLICY, CREDIT EXTENSION, AND HOUSING BUBBLES: 2008 AND 1929. Critical Review, 21(2-3), 269-300. https://doi.org/10.1080/08913810902934117
HASABALLAH, A. S. – ZENAD, Y. S. & SHLAKA, J. K. (2019): The role of fundamental analysis in determining the market value of hospitality, tourism and company shares [Article]. African Journal of Hospitality, Tourism and Leisure, 8(4). https://www.scopus.com/inward/record.uri?eid=2-s2.0-85071593715&partnerID=40&md5=daa38c2fbe4c83f07e18582e1ce9232f
HOSSAIN, S. (2022): High-Frequency Trading (HFT) and Market Quality Research: An Evaluation of the Alternative HFT Proxies. Journal of Risk and Financial Management, 15(2), 54. https://www.mdpi.com/1911-8074/15/2/54
INSAIDOO, M. – BRAFU-INSAIDOO, W. G. – PEPRAH, J. A. & CANTAH, W. G. (2024): The role of financial globalization in the long-run volatility between forex and stock markets during COVID-19: Evidence from Africa. Research in Globalization, 9, 100242. https://doi.org/https://doi.org/10.1016/j.resglo.2024.100242
KABIR, M. & KABIR, M. M. J. (2021): Fuzzy membership function design: An adaptive neuro-fuzzy inference system (ANFIS) based approach. 2021 International Conference on Computer Communication and Informatics, ICCCI 2021,
KANDI, V. S. P. – KAMAL, P. V. R. & PAVAN, B. N. L. (2023): Fundamental Analysis of Selected Companies in the Pharma Sector in India. AIP Conference Proceedings,
KEBRIAEE-ZADEH, A. – ZARTAB, S. – FATEMI, S. F. & RADMANESH, R. (2014): Fundamentals and stock return in pharmaceutical companies: A panel data model of Iranian industry [Article]. Iranian Journal of Pharmaceutical Sciences, 9(1), 55-60. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84897055997&partnerID=40&md5=bf491e5909170521570425bef808c1a4
KOULOURIOTIS, D. E. – DIAKOULAKIS, I. E. – EMIRIS, D. M. & ZOPOUNIDIS, C. D. (2005): Development of dynamic cognitive networks as complex systems approximators: Validation in financial time series [Article]. Applied Soft Computing Journal, 5(2), 157-
KUPPENHEIMER, G. – SHELLY, S. & STRAUSS, J. (2023): Can machine learning identify sector-level financial ratios that predict sector returns? [Article]. Finance Research Letters, 57, Article 104241. https://doi.org/10.1016/j.frl.2023.104241
LEE, M. C. – CHANG, J. W. – YEH, S. C. – CHIA, T. L. – LIAO, J. S. & CHEN, X. M. (2022): Applying attention-based BiLSTM and technical indicators in the design and performance analysis of stock trading strategies [Article]. Neural Computing and Applications, 34(16), 13267-13279. https://doi.org/10.1007/s00521-021-06828-4
LEWANDOWSKI, S. (2008): Basic elements of the artificial intelligence and examples of their applications in the textile industry. Part Ic: Kinds and characteristics of the artificial intelligence elements [Article]. Przeglad Wlokienniczy, 62(7), 33-35+32. https://www.scopus.com/inward/record.uri?eid=2-s2.0-49649087941&partnerID=40&md5=7d980d6bcd4565e7269f94177b5d79a0
LI, Z. – DENG, G. & CHE, H. (2021): Patent-Based Predictive EPS on Increasing Investment Performance of China Stock Market. Proceedings of 2021 IEEE International Conference on Power Electronics, Computer Applications, ICPECA 2021,
MASOOD, T. (2004): Applications of artificial intelligence in industrial engineering. Proceedings of the 5th International Conference on Quality, Reliability, and Maintenance, QRM 2004,
MATKARIMOV, B. – BARLYBAYEV, A. & KARIMOV, D. (2024): Enhancing Analytical Precision in Company Earnings Reports through Neurofuzzy System Development: A Comprehensive Investigation. Journal of Electrical and Computer Engineering, 2024.
MOHAMED, S. N. F. – AZLI, N. A. – SALAM, Z. & AYOB, S. M. (2008): Sugeno-type Fuzzy Logic Controller (SFLC) for a Modular Structured Multilevel Inverter (MSMI). PECon 2008 – 2008 IEEE 2nd International Power and Energy Conference,
MICHIE, R. C. (1997): Friend or foe? Information technology and the London Stock Exchange since 1700. Journal of Historical Geography, 23(3), 304-326. https://doi.org/https://doi.org/10.1006/jhge.1996.0056
MOSAVI, M. R. – AYATOLLAHI, A. & AFRAKHTEH, S. (2021): An efficient method for classifying motor imagery using CPSO-trained ANFIS prediction [Article]. Evolving Systems, 12(2), 319-336. https://doi.org/10.1007/s12530-019-09280-x
MURAKÖZY, L. (2016): Államok kora. https://doi.org/10.1556/9789630597692
NAIR, R. K. R. & RAVICHANDRAN, J. (2020): A vlsi optimization technique for general neural network architecture and deep learning network using xilinx [Article]. International Journal of Advanced Science and Technology, 29(9 Special Issue), 34-44. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85084482145&partnerID=40&md5=b46841a0f1406aa57462990b201699b2
NGUYEN, T. D. – TRAN, Q. B. – TRAN, D. A. – THAN, T. D. & TRAN, Q. D. (2023): Object Detection Approach for Stock Chart Patterns Recognition in Financial Markets. ACM International Conference Proceeding Series,
OK, Y. – ATAK, M. & AKCAYOL, M. A. (2011): A simple neuro fuzzy model for ISE 100 index prediction [Article]. Journal of the Faculty of Engineering and Architecture of Gazi University, 26(4), 897-904. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84855362245&partnerID=40&md5=40b4478e4c9ed2504b4ca242fd7af37c
OMRAN, M. F. C. (2009): Price Earnings Multiples as Forecasters of Short Term Stock Returns in Egypt. Journal of Economic and Administrative Sciences, 25(1), 47-66. https://doi.org/10.1108/10264116200900003
PROHASKA, Z. – URODA, I. & SULJIC, S. (2011): SP A computer program for fundamental analysis of stocks. MIPRO 2011 – 34th International Convention on Information and Communication Technology, Electronics and Microelectronics – Proceedings,
RIAD, B. – El-ARKAM, M. – MAROUA, K. – WIAM, K. & YOUCEF, Z. (2023): Design of Type 2 Fuzzy Logic Controller for FESTO Process Workstation † [Article]. Engineering Proceedings, 29(1), Article 6. https://doi.org/10.3390/engproc2023029006
RUBÓCZKY, I. (1999): Tőzsdei fogalomtár/összeáll. Czékus Mihály (Könyvismertetés). Vezetéstudomány-Management and Business Journal, 30(4), 52-53.
SAMPURNANINGSIH, S. R. & HANIFAH, A. (2017): Determinant of stock price : A empirical study at state owned enterprises in Indonesia [Article]. International Journal of Applied Business and Economic Research, 15(20), 1-10. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85032576119&partnerID=40&md5=2644971bfa18cc1703a2f6655fb6b18d
SAXENA, R. – SHOBE, J. L. & McNAUGHTON, B. L. (2022): Learning in deep neural networks and brains with similarity-weighted interleaved learning [Article]. Proceedings of the National Academy of Sciences of the United States of America, 119(27), Article e2115229119. https://doi.org/10.1073/pnas.2115229119
SHUKLA, A. – TIWARI, R. & KALA, R. (2010): Fuzzy logic [Article]. Studies in Computational Intelligence, 307, 83-108. https://doi.org/10.1007/978-3-642-14344-1_4
SILPA, K. S. – ARYA MOL, J. & AMBILY, A. S. (2017): A study on fundamental analysis of selected IT companies listed at NSE [Article]. Journal of Advanced Research in Dynamical and Control Systems, 9(Special Issue 5), 1-10. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85030679648&partnerID=40&md5=9b7195a47403afa06c5c24747afad93c
SILVA, A. – NEVES, R. & HORTA, N. (2015): A hybrid approach to portfolio composition based on fundamental and technical indicators [Article]. Expert Systems with Applications, 42(4), 2036-2048. https://doi.org/10.1016/j.eswa.2014.09.050
SON, P. V. H. & DUONG, L. T. (2024): Research on applying machine learning models to predict and assess return on assets (ROA). Asian Journal of Civil Engineering, 25, 4269-4279.
SPRITZER, A. S. & FREITAS, C. M. D. S. (2006): A visual tool to support technical analysis of stock market data. Proceedings of the Workshop on Advanced Visual Interfaces,
TALAMATI, M. R. & PANGEMANAN, S. S. (2015): The effect of earnings per share (eps) & return on equity (roe) on stock price of banking company listed in indonesia stock exchange (idx) 2010-2014. Jurnal EMBA: Jurnal Riset Ekonomi, Manajemen, Bisnis dan Akuntansi, 3(2).
TAMIR, D. E. – RISHE, N. D. & KANDEL, A. (2015): Complex fuzzy sets and complex fuzzy logic an overview of theory and applications [Article]. Studies in Fuzziness and Soft Computing, 326, 661-681. https://doi.org/10.1007/978-3-319-19683-1_31
TUTCU, B. – KAYAKUŞ, M. – TERZIOĞLU, M. – ÜNAL UYAR, G. F. – TALAŞ, H. & YETIZ, F. (2024): Predicting Financial Performance in the IT Industry with Machine Learning: ROA and ROE Analysis. Applied Sciences, 14, 7459.
TRIPATHI, A. – MATHURE, J. – DEOTARSE, S. – RAI, D. & GADHIKAR, L. (2023): Linear Regression Approach for Stock Chart Pattern Recognition. 5th Biennial International Conference on Nascent Technologies in Engineering, ICNTE 2023,
UCKAN, T. (2024): Integrating PCA with deep learning models for stock market Forecasting: An analysis of Turkish stocks markets [Article]. Journal of King Saud University – Computer and Information Sciences, 36(8), Article 102162. https://doi.org/10.1016/j.jksuci.2024.102162
VASANTHA, S. – DHANRAJ, V. & VARADHARAJAN, R. (2012): Stock price movement through technical analysis: Empirical evidence from the information technology sector [Article]. Indian Journal of Finance, 6(10), 4-17. https://www.scopus.com/inward/record.uri?eid=2-s2.0-84919681936&partnerID=40&md5=33f209829963623fffb728abdf840164
VELLA, V. & NG, W. L. (2014): Enhancing risk-adjusted performance of stock market intraday trading with Neuro-Fuzzy systems [Article]. Neurocomputing, 141, 170-187. https://doi.org/10.1016/j.neucom.2014.03.026
WEI, L. Y. – CHENG, C. H. & WU, H. H. (2014): A hybrid ANFIS based on n-period moving average model to forecast TAIEX stock [Article]. Applied Soft Computing Journal, 19, 86-92. https://doi.org/10.1016/j.asoc.2014.01.022
WIGGINS, R. Z. – PIONTEK, T. & METRICK, A. (2019): The Lehman brothers bankruptcy a: overview. Journal of financial crises, 1(1), 39-62.
WIIAVA, A. Y. – FATICHAH, C. & SAIKHU, A. (2022): Stock Price Prediction with Golden Cross and Death Cross on Technical Analysis Indicators Using Long Short Term Memory. ICOIACT 2022 – 5th International Conference on Information and Communications Technology: A New Way to Make AI Useful for Everyone in the New Normal Era, Proceeding,
ZHANG, Z. – ZHOU, H. – LIU, S. & HARRINGTON, P. d. B. (2006): An application of Takagi-Sugeno fuzzy system to the classification of cancer patients based on elemental contents in serum samples [Article]. Chemometrics and Intelligent Laboratory Systems, 82(1-2 SPEC. ISS), 294-299. https://doi.org/10.1016/j.chemolab.2005.05.007
ZOLTÁN, V. J. (2021): A mintavételezés hatása a pénzügyi adatok statisztikai tulajdonságaira és alkalmazása a kockázatkezelésben. Hungarian Statistical Review/Statisztikai Szemle, 99(3).
