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Application of machine learning for financialization modeling
Corresponding Author(s) : Zuzanna Korytnicka
Humanities & Social Sciences Reviews,
Vol. 11 No. 4 (2023): July
Abstract
Research objective: The objective of this article is to present the application of machine learning techniques in modeling the phenomenon of financialization and analyze their effectiveness in predicting and understanding this phenomenon.
Methodology: The methodology is based on data collection and processing from various sources. Subsequently, machine learning techniques such as regression, classification, decision trees, and neural networks were applied to train predictive models and analyze the phenomenon of financialization.
Main conclusions: Data analysis using machine learning techniques allowed for the identification of key factors and patterns related to financialization. It has been demonstrated that machine learning models can effectively predict financialization trends and provide insight into the mechanisms and factors influencing this phenomenon.
Application of the study: The study has significant implications for various fields, such as economics, finance, and economic policy. The application of machine learning techniques in modeling financialization can aid in making better investment decisions, assessing risk, monitoring financial stability, and developing more effective regulatory strategies.
Originality/Novelty of the study: This article contributes an original perspective to the scientific literature by focusing on the application of machine learning techniques in the context of financialization. The work presents a new insight into this phenomenon and provides evidence of the effectiveness of machine learning-based models in analyzing and forecasting financialization.
Keywords
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- Aggarwal, P. K., Jain, P., Mehta, J., Garg, R., Makar, K., & Chaudhary, P. (2021). Machine learning, data mining, and big data analytics for 5G-enabled IoT. Blockchain for 5G-Enabled IoT: The new wave for Industrial Automation, 351-375. https://doi.org/10.1007/978-3-030-67490-8_14 DOI: https://doi.org/10.1007/978-3-030-67490-8_14
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- Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2021). Machine learning towards intelligent systems: applications, challenges, and opportunities. Artificial Intelligence Review, 54, 3299-3348. https://doi.org/10.1007/s10462-020-09948-w DOI: https://doi.org/10.1007/s10462-020-09948-w
- Ippoliti, E. (2021). Mathematics and finance: Some philosophical remarks. Topoi, 40, 771-781. https://doi.org/10.1007/s11245-020-09706-1 DOI: https://doi.org/10.1007/s11245-020-09706-1
- Kaur, H., & Kumari, V. (2022). Predictive modelling and analytics for diabetes using a machine learning approach. Applied computing and informatics, 18(1/2), 90-100. https://doi.org/10.1016/j.aci.2018.12.004 DOI: https://doi.org/10.1016/j.aci.2018.12.004
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- Mehta, P., Pandya, S., & Kotecha, K. (2021). Harvesting social media sentiment analysis to enhance stock market prediction using deep learning. PeerJ Computer Science, 7, e476. https://doi.org/10.7717/peerj-cs.476 DOI: https://doi.org/10.7717/peerj-cs.476
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- Mišić, V. V., & Perakis, G. (2020). Data analytics in operations management: A review. Manufacturing & Service Operations Management, 22(1), 158-169. https://doi.org/10.1287/msom.2019.0805 DOI: https://doi.org/10.1287/msom.2019.0805
- Oucheikh, R., Fri, M., Fedouaki, F., & Hain, M. (2020). Deep real-time anomaly detection for connected autonomous vehicles. Procedia Computer Science, 177, 456-461. https://doi.org/10.1016/j.procs.2020.10.062 DOI: https://doi.org/10.1016/j.procs.2020.10.062
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- Rouf, N., Malik, M. B., Arif, T., Sharma, S., Singh, S., Aich, S., & Kim, H. C. (2021). Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions. Electronics, 10(21), 2717. https://doi.org/10.3390/electronics10212717 DOI: https://doi.org/10.3390/electronics10212717
- Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x DOI: https://doi.org/10.1007/s42979-021-00592-x
- Semieniuk, G., Campiglio, E., Mercure, J. F., Volz, U., & Edwards, N. R. (2021). Lowâ€carbon transition risks for finance. Wiley Interdisciplinary Reviews: Climate Change, 12(1), e678. https://doi.org/10.1002/wcc.678 DOI: https://doi.org/10.1002/wcc.678
- Silva, D. B., & Silla, C. N. (2020, October). Evaluation of students programming skills on a computer programming course with a hierarchical clustering algorithm. In 2020 IEEE Frontiers in Education Conference (FIE) (pp. 1-9). IEEE. https://doi.org/10.1109/FIE44824.2020.9274130 DOI: https://doi.org/10.1109/FIE44824.2020.9274130
- Song, Y., & Wu, R. (2022). The impact of financial enterprises’ excessive financialization risk assessment for risk control based on data mining and machine learning. Computational Economics, 60(4), 1245-1267. https://doi.org/10.1007/s10614-021-10135-4 DOI: https://doi.org/10.1007/s10614-021-10135-4
- Truby, J., Brown, R., & Dahdal, A. (2020). Banking on AI: mandating a proactive approach to AI regulation in the financial sector. Law and Financial Markets Review, 14(2), 110-120. https://doi.org/10.1080/17521440.2020.1760454 DOI: https://doi.org/10.1080/17521440.2020.1760454
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Alexiou, C., Trachanas, E., & Vogiazas, S. (2022). Income inequality and financialization: A not so straightforward relationship. Journal of Economic Studies, 49(1), 95-111. https://doi.org/10.1108/JES-05-2020-0202 DOI: https://doi.org/10.1108/JES-05-2020-0202
Aqab, S., & Tariq, M. U. (2020). Handwriting recognition using artificial intelligence neural network and image processing. International Journal of Advanced Computer Science and Applications, 11(7). https://doi.org/10.14569/IJACSA.2020.0110719 DOI: https://doi.org/10.14569/IJACSA.2020.0110719
Augustine, B. C., Royle, J. A., Linden, D. W., & Fuller, A. K. (2020). Spatial proximity moderates genotype uncertainty in genetic tagging studies. Proceedings of the National Academy of Sciences, 117(30), 17903-17912. https://doi.org/10.1073/pnas.2000247117 DOI: https://doi.org/10.1073/pnas.2000247117
Bandara, K., Bergmeir, C., & Smyl, S. (2020). Forecasting across time series databases using recurrent neural networks on groups of similar series: A clustering approach. Expert systems with applications, 140, 112896. https://doi.org/10.1016/j.eswa.2019.112896 DOI: https://doi.org/10.1016/j.eswa.2019.112896
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Caccamisi, A., Jørgensen, L., Dalianis, H., & Rosenlund, M. (2020). Natural language processing and machine learning to enable automatic extraction and classification of patients’ smoking status from electronic medical records. Upsala journal of medical sciences, 125(4), 316-324. https://doi.org/10.1080/03009734.2020.1792010 DOI: https://doi.org/10.1080/03009734.2020.1792010
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Chen, Y., Kumara, E. K., & Sivakumar, V. (2021). Investigation of finance industry on risk awareness model and digital economic growth. Annals of Operations Research, 1-22. https://doi.org/10.1007/s10479-021-04287-7 DOI: https://doi.org/10.1007/s10479-021-04287-7
Creamer, G., Kazantsev, G., & Aste, T. (Eds.). (2021). Machine Learning and AI in Finance. Routledge. https://doi.org/10.4324/9781003145714 DOI: https://doi.org/10.4324/9781003145714
Dara, S., Dhamercherla, S., Jadav, S. S., Babu, C. M., & Ahsan, M. J. (2022). Machine learning in drug discovery: a review. Artificial Intelligence Review, 55(3), 1947-1999. https://doi.org/10.1007/s10462-021-10058-4 DOI: https://doi.org/10.1007/s10462-021-10058-4
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Golbayani, P., Florescu, I., & Chatterjee, R. (2020). A comparative study of forecasting corporate credit ratings using neural networks, support vector machines, and decision trees. The North American Journal of Economics and Finance, 54, 101251. https://doi.org/10.1016/j.najef.2020.101251 DOI: https://doi.org/10.1016/j.najef.2020.101251
Goodell, J. W., Kumar, S., Lim, W. M., & Pattnaik, D. (2021). Artificial intelligence and machine learning in finance: Identifying foundations, themes, and research clusters from bibliometric analysis. Journal of Behavioral and Experimental Finance, 32, 100577. https://doi.org/10.1016/j.jbef.2021.100577 DOI: https://doi.org/10.1016/j.jbef.2021.100577
Google Trends, trends.google.pl
Hassaballah, M., & Awad, A. I. (Eds.). (2020). Deep learning in computer vision: principles and applications. CRC Press. https://doi.org/10.1201/9781351003827 DOI: https://doi.org/10.1201/9781351003827
Huang, J., Chai, J., & Cho, S. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China, 14(1), 1-24. https://doi.org/10.1186/s11782-020-00082-6 DOI: https://doi.org/10.1186/s11782-020-00082-6
Injadat, M., Moubayed, A., Nassif, A. B., & Shami, A. (2021). Machine learning towards intelligent systems: applications, challenges, and opportunities. Artificial Intelligence Review, 54, 3299-3348. https://doi.org/10.1007/s10462-020-09948-w DOI: https://doi.org/10.1007/s10462-020-09948-w
Ippoliti, E. (2021). Mathematics and finance: Some philosophical remarks. Topoi, 40, 771-781. https://doi.org/10.1007/s11245-020-09706-1 DOI: https://doi.org/10.1007/s11245-020-09706-1
Kaur, H., & Kumari, V. (2022). Predictive modelling and analytics for diabetes using a machine learning approach. Applied computing and informatics, 18(1/2), 90-100. https://doi.org/10.1016/j.aci.2018.12.004 DOI: https://doi.org/10.1016/j.aci.2018.12.004
Kowalczyk, D., & Woźniak, H. (2020). Procesy finansjalizacji gospodarki światowej. Wybrane zagadnienia. Sopot: Centrum Myśli Strategicznych.
Lee, I., & Shin, Y. J. (2020). Machine learning for enterprises: Applications, algorithm selection, and challenges. Business Horizons, 63(2), 157-170. https://doi.org/10.1016/j.bushor.2019.10.005 DOI: https://doi.org/10.1016/j.bushor.2019.10.005
Li, M., & Li, H. (2020). Application of deep neural network and deep reinforcement learning in wireless communication. Plos one, 15(7), e0235447. https://doi.org/10.1371/journal.pone.0235447 DOI: https://doi.org/10.1371/journal.pone.0235447
MacEachern, S. J., & Forkert, N. D. (2021). Machine learning for precision medicine. Genome, 64(4), 416-425. https://doi.org/10.1139/gen-2020-0131 DOI: https://doi.org/10.1139/gen-2020-0131
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Mahesh, B. (2020). Machine learning algorithms-a review. International Journal of Science and Research (IJSR).[Internet], 9, 381-386.
Mehta, P., Pandya, S., & Kotecha, K. (2021). Harvesting social media sentiment analysis to enhance stock market prediction using deep learning. PeerJ Computer Science, 7, e476. https://doi.org/10.7717/peerj-cs.476 DOI: https://doi.org/10.7717/peerj-cs.476
Mehtab, S., Sen, J., & Dutta, A. (2021). Stock price prediction using machine learning and LSTM-based deep learning models. In Machine Learning and Metaheuristics Algorithms, and Applications: Second Symposium, SoMMA 2020, Chennai, India, October 14–17, 2020, Revised Selected Papers 2 (pp. 88-106). Springer Singapore. https://doi.org/10.1007/978-981-16-0419-5_8 DOI: https://doi.org/10.1007/978-981-16-0419-5_8
Mhlanga, D. (2021). Financial inclusion in emerging economies: The application of machine learning and artificial intelligence in credit risk assessment. International Journal of Financial Studies, 9(3), 39. https://doi.org/10.3390/ijfs9030039 DOI: https://doi.org/10.3390/ijfs9030039
Mišić, V. V., & Perakis, G. (2020). Data analytics in operations management: A review. Manufacturing & Service Operations Management, 22(1), 158-169. https://doi.org/10.1287/msom.2019.0805 DOI: https://doi.org/10.1287/msom.2019.0805
Oucheikh, R., Fri, M., Fedouaki, F., & Hain, M. (2020). Deep real-time anomaly detection for connected autonomous vehicles. Procedia Computer Science, 177, 456-461. https://doi.org/10.1016/j.procs.2020.10.062 DOI: https://doi.org/10.1016/j.procs.2020.10.062
Panwar, B., Dhuriya, G., Johri, P., Yadav, S. S., & Gaur, N. (2021, March). Stock market prediction using linear regression and svm. In 2021 International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE) (pp. 629-631). IEEE. https://doi.org/10.1109/ICACITE51222.2021.9404733 DOI: https://doi.org/10.1109/ICACITE51222.2021.9404733
Poskart, R. (2022). The emergence and development of the cryptocurrency as a sign of global financial markets financialisation. Central European Review of Economics & Finance, 36(1), 53-66. https://doi.org/10.24136/ceref.2022.004 DOI: https://doi.org/10.24136/ceref.2022.004
Rouf, N., Malik, M. B., Arif, T., Sharma, S., Singh, S., Aich, S., & Kim, H. C. (2021). Stock market prediction using machine learning techniques: a decade survey on methodologies, recent developments, and future directions. Electronics, 10(21), 2717. https://doi.org/10.3390/electronics10212717 DOI: https://doi.org/10.3390/electronics10212717
Sarker, I. H. (2021). Machine learning: Algorithms, real-world applications and research directions. SN computer science, 2(3), 160. https://doi.org/10.1007/s42979-021-00592-x DOI: https://doi.org/10.1007/s42979-021-00592-x
Semieniuk, G., Campiglio, E., Mercure, J. F., Volz, U., & Edwards, N. R. (2021). Lowâ€carbon transition risks for finance. Wiley Interdisciplinary Reviews: Climate Change, 12(1), e678. https://doi.org/10.1002/wcc.678 DOI: https://doi.org/10.1002/wcc.678
Silva, D. B., & Silla, C. N. (2020, October). Evaluation of students programming skills on a computer programming course with a hierarchical clustering algorithm. In 2020 IEEE Frontiers in Education Conference (FIE) (pp. 1-9). IEEE. https://doi.org/10.1109/FIE44824.2020.9274130 DOI: https://doi.org/10.1109/FIE44824.2020.9274130
Song, Y., & Wu, R. (2022). The impact of financial enterprises’ excessive financialization risk assessment for risk control based on data mining and machine learning. Computational Economics, 60(4), 1245-1267. https://doi.org/10.1007/s10614-021-10135-4 DOI: https://doi.org/10.1007/s10614-021-10135-4
Truby, J., Brown, R., & Dahdal, A. (2020). Banking on AI: mandating a proactive approach to AI regulation in the financial sector. Law and Financial Markets Review, 14(2), 110-120. https://doi.org/10.1080/17521440.2020.1760454 DOI: https://doi.org/10.1080/17521440.2020.1760454