Mutatószám-halmaz kialakítása autóipari vállalatok tőzsdei előrejelzéséhez

Hivatkozások

• Ababou, R., Côme, 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
• Ajha, O., Das, S., Dutta, T., Das, S., Mishra, S., & Abualigah, L. (2024). A LSTM Based Intelligent Framework for Financial Stock Prediction. Lecture Notes in Networks and Systems,
• Alekseeva, E. (2020). The opposition of symbolism and connectionism in the history of artificial intelligence development [Review]. Istoriya, 11(11). https://doi.org/10.18254/S207987840013021-2
• Alsubaie, Y., Hindi, K. E., & Alsalman, H. (2019). Cost-Sensitive Prediction of Stock Price Direction: Selection of Technical Indicators [Article]. IEEE Access, 7, 146876-146892, Article 8861031. https://doi.org/10.1109/ACCESS.2019.2945907
• Anurag, A. S. (2024). Early beginnings of AI: The field of research in computer science. In Cases on AI Ethics in Business (pp. 1-20). https://doi.org/10.4018/9798369326435.ch001
• 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,
• Audibert, R. B., Lemos, H., Avelar, P., Tavares, A. R., & Lamb, L. C. (2023). On the Evolution of A.I. and Machine Learning: Towards a Meta-level Measuring and Understanding Impact, Influence, and Leadership at Premier A.I. Conferences [Article]. Journal of Applied Logics, 10(5), 693-817. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203359595&partnerID=40&md5=c25ec3b5092d305c4a731b0740c9a145
• 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,
• Banik, S., Chanchary, F. H., Rouf, R. A., & Khan, K. (2007). Modeling chaotic behavior of Dhaka Stock Market Index values using the neuro-fuzzy model. 2007 10th International Conference on Computer and Information Technology, ICCIT,
• Barak, S., Dahooie, J. H., & Tichý, 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. Expert Systems with Applications, 37(12), 7908-7912. https://doi.org/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
• Cappello, G., Defeudis, A., Giannini, V., Mazzetti, S., & Regge, D. (2023). Artificial intelligence in oncologic imaging. In Multimodality Imaging and Intervention in Oncology (pp. 585-597). https://doi.org/10.1007/978-3-031-28524-0_24
• 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,
• Chandar, S. K. (2021). Forecasting intraday stock price using ANFIS and bio-inspired algorithms [Article]. International Journal of Networking and Virtual Organisations, 25(1), 29-47. https://doi.org/10.1504/IJNVO.2021.117754
• 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
• 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
• Duy, T. H., & Van Cuong, N. (2014). Enhancement and FPGA implementation of ANFIS algorithm in digital image processing. 2014 IEEE 5th International Conference on Communications and Electronics, IEEE ICCE 2014,
• Farhat, A., & Cheok, K. C. (2017). Improving adaptive network fuzzy inference system with Levenberg-Marquardt algorithm. 11th Annual IEEE International Systems Conference, SysCon 2017 – Proceedings,
• 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
• Galimberti, C., & Repetto, M. (2023). Neural Networks and Deep Learning. In Impact of Artificial Intelligence in Business and Society: Opportunities and Challenges (pp. 58-81). https://doi.org/10.4324/9781003304616-5
• 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
• Haq, S. S., Lenine, D., & Lalitha, S. V. N. L. (2021). Performance Enhancement of UPQC Using Takagi–Sugeno Fuzzy Logic Controller [Article]. International Journal of Fuzzy Systems, 23(6), 1765-1774. https://doi.org/10.1007/s40815-021-01095-w
• 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
• 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,
• Kollmannsberger, S., D’Angella, D., Jokeit, M., & Herrmann, L. (2021). Neural Networks. In Studies in Computational Intelligence (Vol. 977, pp. 19-45). https://doi.org/10.1007/978-3-030-76587-3_3
• 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), 157-179. https://doi.org/10.1016/j.asoc.2004.06.004
• 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
• Maltoudoglou, L., Boutalis, Y., & Loukeris, N. (2016). A fuzzy system model for financial assessment of listed companies. IISA 2015 – 6th International Conference on Information, Intelligence, Systems and Applications,
• 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
• Morley, J., Floridi, L., Kinsey, L., & Elhalal, A. (2020). From What to How: An Initial Review of Publicly Available AI Ethics Tools, Methods and Research to Translate Principles into Practices [Article]. Science and Engineering Ethics, 26(4), 2141-2168. https://doi.org/10.1007/s11948-019-00165-5
• 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
• 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., & Akçayol, 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
• Ontiveros-Robles, E., Melin, P., & Castillo, O. (2020). Study of the Relevance of Polynomial Order in Takagi-Sugeno Fuzzy Inference Systems Applied in Diagnosis Problems. In Studies in Computational Intelligence (Vol. 827, pp. 19-33). https://doi.org/10.1007/978-3-030-34135-0_2
• Postolache, F., Elena, T. C., Ariton, D., & Constantin, F. A. (2010). Assessment of exchange transactions in AUDUSD parity by technical and fundamental analysis. Knowledge Management and Innovation: A Business Competitive Edge Perspective – Proceedings of the 15th International Business Information Management Association Conference, IBIMA 2010,
• Prohaska, Z., Uroda, I., & Suljić, 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,
• Rout, A. K., & Muppidi, S. (2019). Adoptive trend following strategy in financial time series with multi-objective function [Article]. International Journal of Scientific and Technology Research, 8(12), 3746-3755. https://www.scopus.com/inward/record.uri?eid=2-s2.0-85076907379&partnerID=40&md5=25c57385afafd5320f3c18cc8f41022f
• 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
• Sloane, E. B., & Silva, R. J. (2019). Artificial intelligence in medical devices and clinical decision support systems. In Clinical Engineering Handbook, Second Edition (pp. 556-568). https://doi.org/10.1016/B978-0-12-813467-2.00084-5
• 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,
• Suryana, N., & Anggadini, S. (2020). Analysis of Stock Prices Affected by Current Ratio. International Journal of Science, Technology & Management, 1, 190-197. https://doi.org/10.46729/ijstm.v1i3.44
• 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,
• Uçkan, 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
• Varga, J. Z. (2021). A mintavételezés hatása a pénzügyi adatok statisztikai tulajdonságaira és alkalmazása a kockázatkezelésben= Effect of various sampling methods on the statistical properties of financial data and their application in risk management. STATISZTIKAI SZEMLE, 99(3), 233-252.
• 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, L., Pan, Y., Wu, X., & Skibniewski, M. J. (2021). Introduction to Artificial Intelligence. In Lecture Notes in Civil Engineering (Vol. 163, pp. 1-15). https://doi.org/10.1007/978-981-16-2842-9_1