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Finans Alanında Makine ve Derin Öğrenmenin Kullanılması: Lisansüstü Tezlerde Sistematik Literatür Taraması

Year 2023, Volume: 12 Issue: 3, 2187 - 2209, 30.09.2023

İsmail Fatih Ceyhan

https://doi.org/10.15869/itobiad.1329889

Abstract

İnsanoğlu makinelerin insanlar gibi düşünebildiği ve hareket edebildiği bir çağın başlangıcında bulunuyor. Bu durum her ne kadar ürkütücü görünse de, akademide ilgi gören ve üzerinde artan miktarda çalışmalar gerçekleşmeye başlanan bir konudur. Makine öğrenmesi ve derin öğrenmeyle oluşturulan yapay zeka pek çok alanda olduğu gibi, finans alanında da çokça kullanılmaktadır. Bu çalışmalar içerisinde yurt içinde ve yurt dışında yayınlanan makale, kitap, kitap bölümleri, sempozyum bildirileri olduğu gibi, gerçekleştirilen yüksek lisans ve doktora tezleri de yer almaktadır. Bu tür çalışmalarda gelinen son durumu tespit etmek ve literatürdeki boşlukları ortaya çıkarmak amacıyla sistematik literatür taramaları yapılmaktadır. Bu çalışmada, Türkiye’de gerçekleştirilen ve uygulama bölümlerinde finans alanında makine öğrenmesi ve derin öğrenme tekniklerinin kullanıldığı lisansüstü tezler sistematik literatür taraması ile incelenmektedir. Araştırma, 2018-2023 yılları arasındaki dönemde yapılan çalışmaları kapsamaktadır. Araştırmanın sonucunda, konuyla ilgili yapılan tezlerde makine ve derin öğrenme yöntemlerinin en çok finansal enstrümanların gelecekteki fiyatlarının tahminlemesinde, ardından sırasıyla finansal risklerin tespit edilmesinde, kurumsal finansal başarısızlık ve iflas tahmininde ve ayrıca portföy optimizasyon modellerinde kullanıldığı belirlenmiştir. İlgili dönem boyunca, yapılan tez çalışmalarının sayılarında artan bir grafik bulunmaktadır. Bu çalışmalarda genellikle birden fazla algoritmanın uygulamadaki başarıları karşılaştırılarak en başarılı sonuçlar belirlenmeye çalışılmıştır. En çok çalışılan tez konusunun makine öğrenmesiyle kredi riskinin analizi olduğu, ardından makine öğrenmesiyle hisse senedi fiyat tahmininin geldiği ortaya çıkmıştır. En çok kullanılan algoritmaların karar ormanı, karar ağacı ve uzun-kısa dönem hafıza algoritmaları olduğu tespit edilmiştir. Lisansüstü tez konusu olarak sosyal bilimlerden daha çok, fen bilimleri temel alanında tercih edildiği ve yazılan tezlerin en çok bilgisayar mühendisliği ana bilim dalında hazırlandığı, ardından işletme ana bilim dalında hazırlanan tezlerin geldiği ortaya çıkmıştır.

Keywords

Derin Öğrenme Finans Makine Öğrenmesi Meta Analiz Sistematik Literatür Taraması

Supporting Institution

Yoktur

Project Number

Yoktur

Thanks

Sayın editör ve hakemlere teşekkür ederim

References

  • Adosoglou, G., Lombardo, G., Pardalos, P.M. (2021). Neural Network Embeddings on Corporate Annual Flings for Portfolio Selection. Expert Syst Appl., 164, 114053. https://doi.org/10.1016/j.eswa.2020.114053
  • Akgöz, S., Ercan İ. & Kan, İ. (2004). Meta-analizi, Uludağ Üniversitesi Tıp Fakültesi Dergisi, 30, 107-112. Barboza, F., Kimura, H. & Altman, E. (2017). Machine Learning Models and Bankruptcy Prediction. Expert Syst. Appl., 83, 405–417.
  • Baysal Z.N., Çarıkçı, S., & Yaşar, E.B. (2016). Sınıf Öğretmenlerinin Düşünme Becerileri Öğretimine Yönelik Farkındalıkları. Eğitimde Nitel Araştırmalar Dergisi, 5(1), 7-28. DOI: 10.14689/issn.2148-2624.1.5c1s1m
  • Bhatore, S., Mohan, L. & Reddy, Y.R. (2020). Machine Learning Techniques For Credit Risk Evaluation: A Systematic Literature Review. J. Bank Fınanc. Technol., 4, 111–138. https://doi.org/10.1007/s42786-020-00020-3
  • Bustos, O. & Pomares-Quimbaya, A. (2020), Stock Market Movement Forecast: A Systematic Review. Expert Systems with Applications, 156, 113464. https://doi.org/10.1016/j.eswa.2020.113464.
  • Cybermag, 05.07.2023, https://www.cybermagonline.com/finans-sektorunde-yapay-zeka-devri
  • de Jesus D.P. & Besarria C.D.N. (2023). Machine Learning and Sentiment Analysis: Projecting Bank Insolvency Risk. Research in Economics, 77(2), 226-238. DOI: 10.1016/j.rie.2023.03.001
  • de Prado, M.L. (2018). Advances in fnancial machine learning, 1st edn. Wiley, New York.
  • Demirci, F. (2023), Finansta Yapay Zekâ ve Makine Öğrenme Üzerine Bibliyometrik Bir Araştırma. Yapay Zekâ Alan Uygulamaları-1, (Ed. E. B. Ceyhan & İ. F. Ceyhan), Nobel, Ankara.
  • Higgins, J.P.T., Thomas, J., Chandler, J., et al (2019) eds. Cochrane Handbook for Systematic Reviews of Interventions: Version 6.0. Cochrane. https://training.cochrane.org/handbook
  • Huang, J., Chai, J. & Cho, S. (2020). Deep Learning in Finance and Banking: A Literature Review and Classification. Front. Bus. Res. China, 14, 13. https://doi.org/10.1186/s11782-020-00082-6
  • Karaçam, Z. (2013). Sistematik Derleme Metodolojisi: Sistematik Derleme Hazırlamak İçin Bir Rehber. Dokuz Eylül Üniversitesi Hemşirelik Fakültesi Elektronik Dergisi, 6(1), 26-33.
  • Karklius G. (2018). The Effect of Informal Central Bank Communication: Machine Learning Approach. Atlantic Economic Journal, 46(2), 241-242. DOI: 10.1007/s11293-018-9577-7
  • Ketsetsis, A.P., Kourounis, C., Spanos, G., et al., (2020). Deep Learning Techniques for Stock Market Prediction in the European Union: A Systematic Review. 2020 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 605-610, doi: 10.1109/CSCI51800.2020.00107.
  • Kim H, Cho H, & Ryu D. (2020). Corporate Default Predictions Using Machine Learning: Literature Review. Sustainability, 12(16), 6325. https://doi.org/10.3390/su12166325
  • Kristóf T. & Virág (2022). EU-27 Bank Failure Prediction With C5.0 Decision Trees And Deep Learning Neural Networks. Research in International Business and Finance, 61, 101644. DOI: 10.1016/j.ribaf.2022.101644
  • Le H.H. & Viviani J.-L. (2018). Predicting Bank Failure: An Improvement By Implementing A Machine-Learning Approach To Classical Financial Ratios. Research in International Business and Finance, 44, 16-25. DOI: 10.1016/j.ribaf.2017.07.104
  • Li, A.W. & Bastos, G.S. (2020) Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review. IEEE Access, 8, 185232-185242. DOI: 10.1109/ACCESS.2020.3030226.
  • Lin B. & Bai R. (2022). Machine Learning Approaches for Explaining Determinants of The Debt Financing in Heavy-Polluting Enterprises. Finance Research Letters, 44, 102094. DOI: 10.1016/j.frl.2021.102094
  • Marne S., Churi S., Correia D., & Gomes J., (2021), Predicting Price of Cryptocurrency – A Deep Learning Approach. International Journal of Engineering Research & Technology, 9(3), 387-393.
  • Mitchell, T. (1997). Machine Learning. McGraw Hill: New York, NY, USA.
  • Mohapatra S., Mukherjee R., Roy A., Sengupta A. & Puniyani A. (2022). Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators? Journal of Risk and Financial Management, 15(8), 350. DOI: 10.3390/jrfm15080350
  • Niţoi M., Pochea M.-M. & Radu C. (2023). Unveiling the Sentiment Behind Central Bank Narratives: A Novel Deep Learning Index. Journal of Behavioral and Experimental Finance, 38, 100809. DOI: 10.1016/j.jbef.2023.100809
  • Olorunnimbe, K., & Viktor, H. (2023) Deep Learning in The Stock Market—A Systematic Survey of Practice, Backtesting, and Applications. Artif Intell Rev, 56, 2057–2109. https://doi.org/10.1007/s10462-022-10226-0
  • Owusu A. & Gupta A. (2023). Identifying The Risk Culture of Banks Using Machine Learning. International Journal of Managerial Finance. DOI: 10.1108/IJMF-09-2022-0422
  • Ozbayoglu A.M., Gudelek M.U. & Sezer O.B. (2020) Deep Learning for Financial Applications: A Survey. Appl Soft Comput, 93, 106384. https://doi.org/10.1016/j.asoc.2020.106384
  • Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., & Moher, D. (2021). Updating Guidance for Reporting Systematic Reviews: Development of The PRISMA 2020 Statement. Journal of Clinical Epidemiology, 134, 103-112. https://doi.org/10.1016/j.jclinepi.2021.02.003
  • Park J., Shin M. & Heo W. (2021). Estimating The Bis Capital Adequacy Ratio for Korean Banks Using Machine Learning: Predicting By Variable Selection Using Random Forest Algorithms. Risks, 9(2), 32, 1-19. DOI: 10.3390/risks9020032
  • Petropoulos A. & Siakoulis V. (2021). Can Central Bank Speeches Predict Financial Market Turbulence? Evidence From An Adaptive Nlp Sentiment Index Analysis Using XGBoost Machine Learning Technique. Central Bank Review, 21(4), 141-153. DOI: 10.1016/j.cbrev.2021.12.002
  • Prisznyák A. (2022). Bankrobotics: Artificial Intelligence and Machine Learning Powered Banking Risk Management Prevention of Money Laundering and Terrorist Financing. Public Finance Quarterly, 67(2), 288-303. DOI: 10.35551/PFQ_2022_2_8
  • Sak, R., Şahin Sak, İ.T., Öneren Şendil, Ç. & Nas, E. (2021). Bir Araştırma Yöntemi Olarak Doküman Analizi. Kocaeli Üniversitesi Eğitim Dergisi, 4(1), 227-250. 10.33400/kuje.843306
  • Samuel, A.L. (1959). Some Studies in Machine Learning Using The Game of Checkers. IBM J. Res. Dev., 3, 210–229.
  • Sezer, O.B., Gudelek, M.U., & Ozbayoglu, A.M. (2019). Financial Time Series Forecasting With Deep Learning: A Systematic Literature Review: 2005–2019. Applied soft computing, 90, 106181.
  • Toker, A. (2022). Bir Araştırma Metodolojisi Olarak Sistematik Literatür İncelemesi: Meta-Sentez Yöntemi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 22(Özel Sayı 2), 313-340.
  • Tuan L.Q., Lin C.-Y. & Teng H.-W. (2023). Machine Learning Methods for Predicting Failures of Us Commercial Bank. Applied Economics Letters, DOI: 10.1080/13504851.2023.2186353 Wordclouds, https://www.wordclouds.com/, 10.07.2023
  • Yavuz, N. (2022). Sosyal Bilimlerde Sistematik Literatür Analizi. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 51, 347-360.
  • Yıldız, A. (2022). Finans Alanında Yapay Zekâ Teknolojisinin Kullanımı: Sistematik Literatür İncelemesi. Pamukkale Sosyal Bilimler Enstitüsü Dergisi, 52, 47-66.

Using Machine and Deep Learning in Finance: A Systematic Literature Review of Graduate Theses

Year 2023, Volume: 12 Issue: 3, 2187 - 2209, 30.09.2023

İsmail Fatih Ceyhan

https://doi.org/10.15869/itobiad.1329889

Abstract

Humanity is at the beginning of an era in which machines can think and act like humans. As daunting as this may seem, it is gaining attention in academia, and more work is being carried out on it. Artificial intelligence created with machine learning and deep learning is widely used in finance and many other fields. These studies include articles, books, book chapters, symposium proceedings, and master's and doctoral theses published in Türkiye and abroad. Systematic literature reviews are conducted to determine the state of the art in such studies and identify literature gaps. In this study, a systematic literature review is conducted to examine the postgraduate theses conducted in Türkiye in which machine learning and deep learning techniques are used in the field of finance in the application departments. The research covers the studies conducted in the period between 2018 and 2023. As a result of the research, it is determined that machine and deep learning methods are primarily used in predicting future prices of financial instruments, followed by the detection of financial risks, corporate financial failure, bankruptcy prediction, and portfolio optimization models. During the relevant period, there is an increasing trend in the number of thesis studies. In these studies, the success of more than one algorithm in practice is usually compared to determine the most successful results. The most studied thesis topic was credit risk analysis with machine learning, followed by stock price prediction with machine learning. The most commonly used algorithms are decision forest, decision tree, and long-short term memory algorithms. It has been revealed that science is preferred as the subject of a graduate thesis rather than social sciences. The theses written are primarily prepared in the computer engineering major, followed by those prepared in the business administration major.

Keywords

Deep Learning Finance Machine Learning Meta Analysis Systematic Literature Review

Project Number

Yoktur

References

  • Adosoglou, G., Lombardo, G., Pardalos, P.M. (2021). Neural Network Embeddings on Corporate Annual Flings for Portfolio Selection. Expert Syst Appl., 164, 114053. https://doi.org/10.1016/j.eswa.2020.114053
  • Akgöz, S., Ercan İ. & Kan, İ. (2004). Meta-analizi, Uludağ Üniversitesi Tıp Fakültesi Dergisi, 30, 107-112. Barboza, F., Kimura, H. & Altman, E. (2017). Machine Learning Models and Bankruptcy Prediction. Expert Syst. Appl., 83, 405–417.
  • Baysal Z.N., Çarıkçı, S., & Yaşar, E.B. (2016). Sınıf Öğretmenlerinin Düşünme Becerileri Öğretimine Yönelik Farkındalıkları. Eğitimde Nitel Araştırmalar Dergisi, 5(1), 7-28. DOI: 10.14689/issn.2148-2624.1.5c1s1m
  • Bhatore, S., Mohan, L. & Reddy, Y.R. (2020). Machine Learning Techniques For Credit Risk Evaluation: A Systematic Literature Review. J. Bank Fınanc. Technol., 4, 111–138. https://doi.org/10.1007/s42786-020-00020-3
  • Bustos, O. & Pomares-Quimbaya, A. (2020), Stock Market Movement Forecast: A Systematic Review. Expert Systems with Applications, 156, 113464. https://doi.org/10.1016/j.eswa.2020.113464.
  • Cybermag, 05.07.2023, https://www.cybermagonline.com/finans-sektorunde-yapay-zeka-devri
  • de Jesus D.P. & Besarria C.D.N. (2023). Machine Learning and Sentiment Analysis: Projecting Bank Insolvency Risk. Research in Economics, 77(2), 226-238. DOI: 10.1016/j.rie.2023.03.001
  • de Prado, M.L. (2018). Advances in fnancial machine learning, 1st edn. Wiley, New York.
  • Demirci, F. (2023), Finansta Yapay Zekâ ve Makine Öğrenme Üzerine Bibliyometrik Bir Araştırma. Yapay Zekâ Alan Uygulamaları-1, (Ed. E. B. Ceyhan & İ. F. Ceyhan), Nobel, Ankara.
  • Higgins, J.P.T., Thomas, J., Chandler, J., et al (2019) eds. Cochrane Handbook for Systematic Reviews of Interventions: Version 6.0. Cochrane. https://training.cochrane.org/handbook
  • Huang, J., Chai, J. & Cho, S. (2020). Deep Learning in Finance and Banking: A Literature Review and Classification. Front. Bus. Res. China, 14, 13. https://doi.org/10.1186/s11782-020-00082-6
  • Karaçam, Z. (2013). Sistematik Derleme Metodolojisi: Sistematik Derleme Hazırlamak İçin Bir Rehber. Dokuz Eylül Üniversitesi Hemşirelik Fakültesi Elektronik Dergisi, 6(1), 26-33.
  • Karklius G. (2018). The Effect of Informal Central Bank Communication: Machine Learning Approach. Atlantic Economic Journal, 46(2), 241-242. DOI: 10.1007/s11293-018-9577-7
  • Ketsetsis, A.P., Kourounis, C., Spanos, G., et al., (2020). Deep Learning Techniques for Stock Market Prediction in the European Union: A Systematic Review. 2020 International Conference on Computational Science and Computational Intelligence (CSCI), Las Vegas, NV, USA, 605-610, doi: 10.1109/CSCI51800.2020.00107.
  • Kim H, Cho H, & Ryu D. (2020). Corporate Default Predictions Using Machine Learning: Literature Review. Sustainability, 12(16), 6325. https://doi.org/10.3390/su12166325
  • Kristóf T. & Virág (2022). EU-27 Bank Failure Prediction With C5.0 Decision Trees And Deep Learning Neural Networks. Research in International Business and Finance, 61, 101644. DOI: 10.1016/j.ribaf.2022.101644
  • Le H.H. & Viviani J.-L. (2018). Predicting Bank Failure: An Improvement By Implementing A Machine-Learning Approach To Classical Financial Ratios. Research in International Business and Finance, 44, 16-25. DOI: 10.1016/j.ribaf.2017.07.104
  • Li, A.W. & Bastos, G.S. (2020) Stock Market Forecasting Using Deep Learning and Technical Analysis: A Systematic Review. IEEE Access, 8, 185232-185242. DOI: 10.1109/ACCESS.2020.3030226.
  • Lin B. & Bai R. (2022). Machine Learning Approaches for Explaining Determinants of The Debt Financing in Heavy-Polluting Enterprises. Finance Research Letters, 44, 102094. DOI: 10.1016/j.frl.2021.102094
  • Marne S., Churi S., Correia D., & Gomes J., (2021), Predicting Price of Cryptocurrency – A Deep Learning Approach. International Journal of Engineering Research & Technology, 9(3), 387-393.
  • Mitchell, T. (1997). Machine Learning. McGraw Hill: New York, NY, USA.
  • Mohapatra S., Mukherjee R., Roy A., Sengupta A. & Puniyani A. (2022). Can Ensemble Machine Learning Methods Predict Stock Returns for Indian Banks Using Technical Indicators? Journal of Risk and Financial Management, 15(8), 350. DOI: 10.3390/jrfm15080350
  • Niţoi M., Pochea M.-M. & Radu C. (2023). Unveiling the Sentiment Behind Central Bank Narratives: A Novel Deep Learning Index. Journal of Behavioral and Experimental Finance, 38, 100809. DOI: 10.1016/j.jbef.2023.100809
  • Olorunnimbe, K., & Viktor, H. (2023) Deep Learning in The Stock Market—A Systematic Survey of Practice, Backtesting, and Applications. Artif Intell Rev, 56, 2057–2109. https://doi.org/10.1007/s10462-022-10226-0
  • Owusu A. & Gupta A. (2023). Identifying The Risk Culture of Banks Using Machine Learning. International Journal of Managerial Finance. DOI: 10.1108/IJMF-09-2022-0422
  • Ozbayoglu A.M., Gudelek M.U. & Sezer O.B. (2020) Deep Learning for Financial Applications: A Survey. Appl Soft Comput, 93, 106384. https://doi.org/10.1016/j.asoc.2020.106384
  • Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., & Moher, D. (2021). Updating Guidance for Reporting Systematic Reviews: Development of The PRISMA 2020 Statement. Journal of Clinical Epidemiology, 134, 103-112. https://doi.org/10.1016/j.jclinepi.2021.02.003
  • Park J., Shin M. & Heo W. (2021). Estimating The Bis Capital Adequacy Ratio for Korean Banks Using Machine Learning: Predicting By Variable Selection Using Random Forest Algorithms. Risks, 9(2), 32, 1-19. DOI: 10.3390/risks9020032
  • Petropoulos A. & Siakoulis V. (2021). Can Central Bank Speeches Predict Financial Market Turbulence? Evidence From An Adaptive Nlp Sentiment Index Analysis Using XGBoost Machine Learning Technique. Central Bank Review, 21(4), 141-153. DOI: 10.1016/j.cbrev.2021.12.002
  • Prisznyák A. (2022). Bankrobotics: Artificial Intelligence and Machine Learning Powered Banking Risk Management Prevention of Money Laundering and Terrorist Financing. Public Finance Quarterly, 67(2), 288-303. DOI: 10.35551/PFQ_2022_2_8
  • Sak, R., Şahin Sak, İ.T., Öneren Şendil, Ç. & Nas, E. (2021). Bir Araştırma Yöntemi Olarak Doküman Analizi. Kocaeli Üniversitesi Eğitim Dergisi, 4(1), 227-250. 10.33400/kuje.843306
  • Samuel, A.L. (1959). Some Studies in Machine Learning Using The Game of Checkers. IBM J. Res. Dev., 3, 210–229.
  • Sezer, O.B., Gudelek, M.U., & Ozbayoglu, A.M. (2019). Financial Time Series Forecasting With Deep Learning: A Systematic Literature Review: 2005–2019. Applied soft computing, 90, 106181.
  • Toker, A. (2022). Bir Araştırma Metodolojisi Olarak Sistematik Literatür İncelemesi: Meta-Sentez Yöntemi. Anadolu Üniversitesi Sosyal Bilimler Dergisi, 22(Özel Sayı 2), 313-340.
  • Tuan L.Q., Lin C.-Y. & Teng H.-W. (2023). Machine Learning Methods for Predicting Failures of Us Commercial Bank. Applied Economics Letters, DOI: 10.1080/13504851.2023.2186353 Wordclouds, https://www.wordclouds.com/, 10.07.2023
  • Yavuz, N. (2022). Sosyal Bilimlerde Sistematik Literatür Analizi. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 51, 347-360.
  • Yıldız, A. (2022). Finans Alanında Yapay Zekâ Teknolojisinin Kullanımı: Sistematik Literatür İncelemesi. Pamukkale Sosyal Bilimler Enstitüsü Dergisi, 52, 47-66.
There are 37 citations in total.

Details

Primary Language Turkish
Subjects Financial Economy
Journal Section Articles
Authors

İsmail Fatih Ceyhan 0000-0002-4314-7374

Project Number Yoktur
Early Pub Date September 29, 2023
Publication Date September 30, 2023
Published in Issue Year 2023 Volume: 12 Issue: 3

Cite

APA Ceyhan, İ. F. (2023). Finans Alanında Makine ve Derin Öğrenmenin Kullanılması: Lisansüstü Tezlerde Sistematik Literatür Taraması. İnsan Ve Toplum Bilimleri Araştırmaları Dergisi, 12(3), 2187-2209. https://doi.org/10.15869/itobiad.1329889
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