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Deep learning as a tool in forecasting the phenomenon of financialization
Corresponding Author(s) : Zuzanna Korytnicka
Humanities & Social Sciences Reviews,
Vol. 11 No. 4 (2023): July
Abstract
Research objective: The aim of the article is to analyze the effectiveness and accuracy of deep learning in predicting trends and changes related to financialization.
Methodology: In preparing this scientific article, the focus was on conducting a literature review and analyzing existing research that utilized deep learning techniques to forecast the phenomenon of financialization. The principles, algorithms, and techniques applied in deep learning were discussed, with a particular emphasis on their potential applications in predicting financialization trends.
Main conclusions: The results indicate that deep learning can be a powerful tool for forecasting financialization, demonstrating high predictive accuracy.
Application of the study: The discoveries from this article can find practical application in the field of financialization, supporting better investment decision-making and risk management.
Originality/Novelty of the study: The work adds value by showcasing the potential of deep learning as an advanced tool for forecasting financialization, which can significantly impact the development of this domain.
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- Barra, S., Carta, S. M., Corriga, A., Podda, A. S., & Recupero, D. R. (2020). Deep learning and time series-to-image encoding for financial forecasting. IEEE/CAA Journal of Automatica Sinica, 7(3), 683-692. https://doi.org/10.1109/JAS.2020.1003132 DOI: https://doi.org/10.1109/JAS.2020.1003132
- Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K. U., & Kumar, A. (2021). The state of the art of deep learning models in medical science and their challenges. Multimedia Systems, 27(4), 599-613. https://doi.org/10.1007/s00530-020-00694-1 DOI: https://doi.org/10.1007/s00530-020-00694-1
- Boche, H., Fono, A., & Kutyniok, G. (2022). Limitations of deep learning for inverse problems on digital hardware. arXiv preprint arXiv:2202.13490.
- Google Trends (trends.google.pl)
- Guo, W., Che, L., Shahidehpour, M., & Wan, X. (2021). Machine-Learning based methods in short-term load forecasting. The Electricity Journal, 34(1), 106884. https://doi.org/10.1016/j.tej.2020.106884 DOI: https://doi.org/10.1016/j.tej.2020.106884
- Jabde, M., Patil, C., Mali, S., & Vibhute, A. (2023, April). Comparative Study of Machine Learning and Deep Learning Classifiers on Handwritten Numeral Recognition. In International Symposium on Intelligent Informatics: Proceedings of ISI 2022 (pp. 123-137). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-8094-7_10 DOI: https://doi.org/10.1007/978-981-19-8094-7_10
- Li, Y., & Pan, Y. (2022). A novel ensemble deep learning model for stock prediction based on stock prices and news. International Journal of Data Science and Analytics, 1-11. https://doi.org/10.1007/s41060-021-00279-9 DOI: https://doi.org/10.1007/s41060-021-00279-9
- Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A, 379(2194), 20200209. https://doi.org/10.1098/rsta.2020.0209 DOI: https://doi.org/10.1098/rsta.2020.0209
- Lin, S. L., & Huang, H. W. (2020). Improving deep learning for forecasting accuracy in financial data. Discrete Dynamics in Nature and Society, 2020, 1-12. https://doi.org/10.1155/2020/5803407 DOI: https://doi.org/10.1155/2020/5803407
- Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., & Mosavi, A. (2020). Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access, 8, 150199-150212. https://doi.org/10.1109/ACCESS.2020.3015966 DOI: https://doi.org/10.1109/ACCESS.2020.3015966
- Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied soft computing, 90, 106-181. https://doi.org/10.1016/j.asoc.2020.106181 DOI: https://doi.org/10.1016/j.asoc.2020.106181
- Tahir, N. M., Bature, U. I., Baba, M. A., Abubakar, K. A., & Yarima, S. M. (2020). Image recognition based autonomous driving: a deep learning approach. Int. J. Eng. Manuf, 10(6), 11-19. https://doi.org/10.5815/ijem.2020.06.02 DOI: https://doi.org/10.5815/ijem.2020.06.02
- Thompson, N. C., Greenewald, K., Lee, K., & Manso, G. F. (2020). The computational limits of deep learning. arXiv preprint arXiv:2007.05558.
- Xie, S., Yu, Z., & Lv, Z. (2021). Multi-Disease Prediction Based on Deep Learning: A Survey. CMES-Computer Modeling in Engineering & Sciences, 128(2), 489-523. https://doi.org/10.32604/cmes.2021.016728 DOI: https://doi.org/10.32604/cmes.2021.016728
- Zohuri, B., & Rahmani, F. M. (2023). Artificial intelligence driven resiliency with machine learning and deep learning components. Japan Journal of Research, 1(1), 1-7. https://doi.org/10.33425/2690-8077.1002 DOI: https://doi.org/10.33425/2690-8077.1002
References
Barra, S., Carta, S. M., Corriga, A., Podda, A. S., & Recupero, D. R. (2020). Deep learning and time series-to-image encoding for financial forecasting. IEEE/CAA Journal of Automatica Sinica, 7(3), 683-692. https://doi.org/10.1109/JAS.2020.1003132 DOI: https://doi.org/10.1109/JAS.2020.1003132
Bhatt, C., Kumar, I., Vijayakumar, V., Singh, K. U., & Kumar, A. (2021). The state of the art of deep learning models in medical science and their challenges. Multimedia Systems, 27(4), 599-613. https://doi.org/10.1007/s00530-020-00694-1 DOI: https://doi.org/10.1007/s00530-020-00694-1
Boche, H., Fono, A., & Kutyniok, G. (2022). Limitations of deep learning for inverse problems on digital hardware. arXiv preprint arXiv:2202.13490.
Google Trends (trends.google.pl)
Guo, W., Che, L., Shahidehpour, M., & Wan, X. (2021). Machine-Learning based methods in short-term load forecasting. The Electricity Journal, 34(1), 106884. https://doi.org/10.1016/j.tej.2020.106884 DOI: https://doi.org/10.1016/j.tej.2020.106884
Jabde, M., Patil, C., Mali, S., & Vibhute, A. (2023, April). Comparative Study of Machine Learning and Deep Learning Classifiers on Handwritten Numeral Recognition. In International Symposium on Intelligent Informatics: Proceedings of ISI 2022 (pp. 123-137). Singapore: Springer Nature Singapore. https://doi.org/10.1007/978-981-19-8094-7_10 DOI: https://doi.org/10.1007/978-981-19-8094-7_10
Li, Y., & Pan, Y. (2022). A novel ensemble deep learning model for stock prediction based on stock prices and news. International Journal of Data Science and Analytics, 1-11. https://doi.org/10.1007/s41060-021-00279-9 DOI: https://doi.org/10.1007/s41060-021-00279-9
Lim, B., & Zohren, S. (2021). Time-series forecasting with deep learning: a survey. Philosophical Transactions of the Royal Society A, 379(2194), 20200209. https://doi.org/10.1098/rsta.2020.0209 DOI: https://doi.org/10.1098/rsta.2020.0209
Lin, S. L., & Huang, H. W. (2020). Improving deep learning for forecasting accuracy in financial data. Discrete Dynamics in Nature and Society, 2020, 1-12. https://doi.org/10.1155/2020/5803407 DOI: https://doi.org/10.1155/2020/5803407
Nabipour, M., Nayyeri, P., Jabani, H., Shahab, S., & Mosavi, A. (2020). Predicting stock market trends using machine learning and deep learning algorithms via continuous and binary data; a comparative analysis. IEEE Access, 8, 150199-150212. https://doi.org/10.1109/ACCESS.2020.3015966 DOI: https://doi.org/10.1109/ACCESS.2020.3015966
Sezer, O. B., Gudelek, M. U., & Ozbayoglu, A. M. (2020). Financial time series forecasting with deep learning: A systematic literature review: 2005–2019. Applied soft computing, 90, 106-181. https://doi.org/10.1016/j.asoc.2020.106181 DOI: https://doi.org/10.1016/j.asoc.2020.106181
Tahir, N. M., Bature, U. I., Baba, M. A., Abubakar, K. A., & Yarima, S. M. (2020). Image recognition based autonomous driving: a deep learning approach. Int. J. Eng. Manuf, 10(6), 11-19. https://doi.org/10.5815/ijem.2020.06.02 DOI: https://doi.org/10.5815/ijem.2020.06.02
Thompson, N. C., Greenewald, K., Lee, K., & Manso, G. F. (2020). The computational limits of deep learning. arXiv preprint arXiv:2007.05558.
Xie, S., Yu, Z., & Lv, Z. (2021). Multi-Disease Prediction Based on Deep Learning: A Survey. CMES-Computer Modeling in Engineering & Sciences, 128(2), 489-523. https://doi.org/10.32604/cmes.2021.016728 DOI: https://doi.org/10.32604/cmes.2021.016728
Zohuri, B., & Rahmani, F. M. (2023). Artificial intelligence driven resiliency with machine learning and deep learning components. Japan Journal of Research, 1(1), 1-7. https://doi.org/10.33425/2690-8077.1002 DOI: https://doi.org/10.33425/2690-8077.1002