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USING OF ARTIFICIAL INTELLIGENCE-BASED LEARNING METHODS IN FINANCE: A BIBLIOMETRIC ANALYSIS

Year 2025, Volume: 34 Issue: Uygarlığın Dönüşümü - Sosyal Bilimlerin Bakışıyla Yapay Zekâ, 303 - 321, 20.07.2025

Erdi Bayram , Gökhan Berk Özbek

Abstract

The study presents a bibliometric analysis of research in finance focusing on machine learning, deep learning, and reinforcement learning. The analysis covers the period from 2001 to 2025 and includes only articles written in English and indexed in the Web of Science and Scopus databases. Using custom code in Rstudio environment, articles from both databases were merged, duplicates were removed, and a final dataset was prepared for analysis.The studies were examined bibliographically in terms of authors, journals, keywords, thematic areas, citation counts, and author affiliations. For visual analysis, the Biblioshiny software was used. The findings reveal a significant increase in the number of publications in this field, particularly after 2018. Key research themes identified include stock price prediction, volatility forecasting, sentiment analysis, neural networks, and optimization. The Journal Expert Systems with Applications was found to have the highest number of publications in the field. Researchers from the People’s Republic of China contributed the largest share, accounting for 29.8% of all publications. The most frequently occurring terms in article titles include stock, learning, prediction, market, forecasting, analysis, sentiment, trading, and portfolio. This study is considered important for identifying the current state, academic impact, and future research directions of AI-based methods and models within the finance literature.

Keywords

Machine Learning Deep Learning Reinforcement Learning Bibliometric Analysis Quantitative Finance

References

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  • Aria, M. ve Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
  • Arslankaya, S. ve Toprak, Ş. (2021). Makine öğrenmesi ve derin öğrenme algoritmalarını kullanarak hisse senedi fiyat tahmini. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 13(1), 178-192. https://doi.org/10.29137/umagd.771671
  • Aslancı, S. (2022). Araştırma sorgulamaya dayalı öğrenme: bibliyometrik bir analiz. Scientific Educational Studies, 6(1), 1-25. https://doi.org/10.1016/j.joi.2017.08.007
  • Aydın, N. (2024). Bibliyometrik analiz nasıl yapılır: Genel bir bakış. Balkan ve Yakın Doğu Sosyal Bilimler Dergisi. 19(Özel Sayı), 153-160.
  • Baek, S., Mohanty, S. K., ve Glambosky, M. (2020). COVID-19 and stock market volatility: An industry level analysis. Finance Research Letters, 37, 101748. https://doi.org/10.1016/j.frl.2020.101748
  • Baek, Y. ve Kim, H. Y. (2018). ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Systems with Applications, 113, 457-480. https://doi.org/10.1016/j.eswa.2018.07.019
  • Borovkova, S. ve Tsiamas, I. (2019). An ensemble of LSTM neural networks for high‐frequency stock market classification. Journal of Forecasting, 38(6), 600-619. https://doi.org/10.1002/for.2585
  • Brock, W. A., Hommes, C. H., ve Wagener, F. O. (2009). More hedging instruments may destabilize markets. Journal of Economic Dynamics and Control, 33(11), 1912-1928. https://doi.org/10.1016/j.jedc.2009.05.004
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  • Chong, E., Han, C. ve Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187-205. https://doi.org/10.1016/j.eswa.2017.04.030
  • Dargan, S., Kumar, M., Ayyagari, M. R. ve Kumar, G. (2020). A survey of deep learning and its applications: A new paradigm to machine learning. Archives of Computational Methods in Engineering, 27(4), 1071-1092. https://doi.org/10.1007/s11831-019-09344-w
  • De Spiegeleer, J., Madan, D. B., Reyners, S. ve Schoutens, W. (2018). Machine learning for quantitative finance: fast derivative pricing, hedging and fitting. Quantitative Finance, 18(10), 1635-1643. https://doi.org/10.1080/14697688.2018.1495335
  • Dirik, D., Erhan, T. ve Eryılmaz, İ. (2024). Yapay zeka ve örgüt temelli araştırmaların potansiyel eğilimleri üzerine bibliyometrik bir analiz. Bulletin of Economic Theory and Analysis, 9(3), 669-698. https://doi.org/10.25229/beta.1487924
  • Fischer, T. ve Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669. https://doi.org/10.1016/j.ejor.2017.11.054
  • Gogas, P. ve Papadimitriou, T. (2021). Machine learning in economics and finance. Computational Economics, 57, 1-4. https://doi.org/10.1007/s10614-021-10094-w
  • Gu, S., Kelly, B., ve Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273. https://doi.org/10.1093/rfs/hhaa009
  • Hambly, B., Xu, R., ve Yang, H. (2023). Recent advances in reinforcement learning in finance. Mathematical Finance, 33(3), 437-503. https://doi.org/10.1111/mafi.12382
  • Helmbold, D. P., Schapire, R. E., Singer, Y., ve Warmuth, M. K. (1998). On‐line portfolio selection using multiplicative updates. Mathematical Finance, 8(4), 325-347. https://doi.org/10.1111/1467-9965.00058
  • Hoang, D. ve Wiegratz, K. (2023). Machine learning methods in finance: Recent applications and prospects. European Financial Management, 29(5), 1657-1701. https://doi.org/10.1111/eufm.12408
  • Hoseinzade, E. ve Haratizadeh, S. (2019). CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Systems with Applications, 129, 273-285. https://doi.org/10.1016/j.eswa.2019.03.029
  • Huang, J., Chai, J., ve Cho, S. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China, 14(1), 13. https://doi.org/10.1186/s11782-020-00082-6
  • Kim, H. Y. ve Won, C. H. (2018). Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models. Expert Systems with Applications, 103, 25-37. https://doi.org/10.1016/j.eswa.2018.03.002
  • Krauss, C., Do, X. A. ve Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689-702. https://doi.org/10.1016/j.ejor.2016.10.031
  • Leippold, M., Wang, Q., ve Zhou, W. (2022). Machine learning in the Chinese stock market. Journal of Financial Economics, 145(2), 64-82. https://doi.org/10.1016/j.jfineco.2021.08.017
  • Li, X., Shang, W. ve Wang, S. (2019). Text-based crude oil price forecasting: A deep learning approach. International Journal of Forecasting, 35(4), 1548-1560. https://doi.org/10.1016/j.ijforecast.2018.07.006
  • Lin, Y., Yan, Y., Xu, J., Liao, Y., ve Ma, F. (2021). Forecasting stock index price using the CEEMDAN-LSTM model. The North American Journal of Economics and Finance, 57, 101421. https://doi.org/10.1016/j.najef.2021.101421 Manela, A. ve Moreira, A. (2017). News implied volatility and disaster concerns. Journal of Financial Economics, 123(1), 137-162. https://doi.org/10.1016/j.jfineco.2016.01.032
  • Manogna, R. L. ve Anand, A. (2024). A bibliometric analysis on the application of deep learning in finance: Status, development and future directions. Kybernetes, 53(12), 5951-5971. https://doi.org/10.1108/K-04-2023-0637
  • Meng, C., Chen, C., Xu, H. ve Li, T. (2024). Asset pricing and portfolio investment management using machine learning: Research trend analysis using scientometrics. Economics, 18(1), 20220108. https://doi.org/10.1515/econ-2022-0108
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YAPAY ZEKA TEMELLİ ÖĞRENME YÖNTEMLERİNİN FİNANS ALANINDA KULLANIMI: BİBLİYOMETRİK BİR ANALİZ

Year 2025, Volume: 34 Issue: Uygarlığın Dönüşümü - Sosyal Bilimlerin Bakışıyla Yapay Zekâ, 303 - 321, 20.07.2025

Erdi Bayram , Gökhan Berk Özbek

Abstract

Çalışmada Makine Öğrenimi, Derin Öğrenme ve Pekiştirmeli Öğrenme ile ilgili finans alanındaki çalışmaların bibliyometrik incelemesi yapılmıştır. 2001-2025 periyodunu kapsayan araştırmada, Web of Science ve Scopus veri tabanlarında indekslenen, İngilizce yazılmış, yalnızca makale türündeki çalışmalar analiz edilmiştir. Her iki veri tabanındaki çalışmalar Rstudio ortamında özel bir kod dizini kullanılarak birleştirilmiş, tekrarlayanlar ayıklanmış ve tek bir veri setiyle çalışılmıştır. Çalışmalar yazar, dergi, anahtar kelimeler, tematik konular, atıf sayıları ve yazar ülkeleri bağlamında bibliyografik olarak araştırılmıştır. Çalışmaların görsel analizinde ise Biblioshiny programından yararlanılmıştır. Bu araştırmanın sonuçları olarak ilgili alandaki yayın sayısında özellikle 2018 sonrası ciddi bir artış olduğu izlenmektedir. Pay senedi fiyat tahminlemesi, volatilite öngörümlemesi, duygu analizi, sinir ağları ve optimizasyon gibi konuların ilgili alandaki temel temaları oluşturduğu tespit edilmiştir. En fazla yayının Expert Systems with Applications dergisinde yer aldığı ve Çin Halk Cumhuriyeti’nden araştırmacıların %29,8’lik bir oranla ilgili alanlarda en yüksek yayın üretme oranına sahip olduğu görülmektedir. Çalışılan veri setinde yer alan çalışmaların başlıklarında geçen en yaygın kelimeler ise pay senedi, öğrenme, tahmin, piyasa, öngörümleme, analiz, duygu, alım-satım ve portföydür. Araştırmanın yapay zekanın ilgili alt disiplinlerine dayalı modellerin finans literatürü açısından mevcut durumunu, etkisini ve potansiyel araştırma konularını ortaya koyması bakımından önemli olduğu olduğu düşünülmektedir.

Keywords

Makine Öğrenimi Derin Öğrenme Pekiştirmeli Öğrenme Bibliyometrik Analiz Kantitatif Finans

Thanks

Araştırma sürecinde fikirlerinden ve yönlendirmelerinden faydalandığımız Manisa Celal Bayar Üniversitesi İİBF İşletme Bölümü Öğretim Üyesi Doç. Dr. Deniz DİRİK'e değerli katkıları için teşekkür ediyoruz.

References

  • Akay, E. C., Soydan, N. T. Y. ve Gacar, B. K. (2020). Makine öğrenmesi ve ekonomi: Bibliyometrik analiz. PressAcademia Procedia, 12(1), 104-109. https://doi.org/10.17261/Pressacademia.2020.1367
  • Aria, M. ve Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007
  • Arslankaya, S. ve Toprak, Ş. (2021). Makine öğrenmesi ve derin öğrenme algoritmalarını kullanarak hisse senedi fiyat tahmini. Uluslararası Mühendislik Araştırma ve Geliştirme Dergisi, 13(1), 178-192. https://doi.org/10.29137/umagd.771671
  • Aslancı, S. (2022). Araştırma sorgulamaya dayalı öğrenme: bibliyometrik bir analiz. Scientific Educational Studies, 6(1), 1-25. https://doi.org/10.1016/j.joi.2017.08.007
  • Aydın, N. (2024). Bibliyometrik analiz nasıl yapılır: Genel bir bakış. Balkan ve Yakın Doğu Sosyal Bilimler Dergisi. 19(Özel Sayı), 153-160.
  • Baek, S., Mohanty, S. K., ve Glambosky, M. (2020). COVID-19 and stock market volatility: An industry level analysis. Finance Research Letters, 37, 101748. https://doi.org/10.1016/j.frl.2020.101748
  • Baek, Y. ve Kim, H. Y. (2018). ModAugNet: A new forecasting framework for stock market index value with an overfitting prevention LSTM module and a prediction LSTM module. Expert Systems with Applications, 113, 457-480. https://doi.org/10.1016/j.eswa.2018.07.019
  • Borovkova, S. ve Tsiamas, I. (2019). An ensemble of LSTM neural networks for high‐frequency stock market classification. Journal of Forecasting, 38(6), 600-619. https://doi.org/10.1002/for.2585
  • Brock, W. A., Hommes, C. H., ve Wagener, F. O. (2009). More hedging instruments may destabilize markets. Journal of Economic Dynamics and Control, 33(11), 1912-1928. https://doi.org/10.1016/j.jedc.2009.05.004
  • Bucci, A. (2020). Realized volatility forecasting with neural networks. Journal of Financial Econometrics, 18(3), 502-531. https://doi.org/10.1093/jjfinec/nbaa008
  • Cao, J., Li, Z. ve Li, J. (2019). Financial time series forecasting model based on CEEMDAN and LSTM. Physica A: Statistical Mechanics and Its Applications, 519, 127-139. https://doi.org/10.1016/j.physa.2018.11.061
  • Carmona, P., Climent, F., ve Momparler, A. (2019). Predicting failure in the US banking sector: An extreme gradient boosting approach. International Review of Economics & Finance, 61, 304-323. https://doi.org/10.1016/j.iref.2018.03.008
  • 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
  • Charpentier, A., Elie, R. ve Remlinger, C. (2021). Reinforcement learning in economics and finance. Computational Economics, 62, 425-462. https://doi.org/10.1007/s10614-021-10119-4
  • Chen, S. ve Ge, L. (2019). Exploring the attention mechanism in LSTM-based Hong Kong stock price movement prediction. Quantitative Finance, 19(9), 1507-1515. https://doi.org/10.1080/14697688.2019.1622287
  • Chen, W., Zhang, H., Mehlawat, M. K. ve Jia, L. (2021). Mean–variance portfolio optimization using machine learning-based stock price prediction. Applied Soft Computing, 100, 106943. https://doi.org/10.1016/j.asoc.2020.106943
  • Cheng, C., Sa-Ngasoongsong, A., Beyca, O., Le, T., Yang, H., Kong, Z., ve Bukkapatnam, S. T. (2015). Time series forecasting for nonlinear and non-stationary processes: a review and comparative study. Iie Transactions, 47(10), 1053-1071. https://doi.org/10.1080/0740817X.2014.999180
  • Chong, E., Han, C. ve Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187-205. https://doi.org/10.1016/j.eswa.2017.04.030
  • Dargan, S., Kumar, M., Ayyagari, M. R. ve Kumar, G. (2020). A survey of deep learning and its applications: A new paradigm to machine learning. Archives of Computational Methods in Engineering, 27(4), 1071-1092. https://doi.org/10.1007/s11831-019-09344-w
  • De Spiegeleer, J., Madan, D. B., Reyners, S. ve Schoutens, W. (2018). Machine learning for quantitative finance: fast derivative pricing, hedging and fitting. Quantitative Finance, 18(10), 1635-1643. https://doi.org/10.1080/14697688.2018.1495335
  • Dirik, D., Erhan, T. ve Eryılmaz, İ. (2024). Yapay zeka ve örgüt temelli araştırmaların potansiyel eğilimleri üzerine bibliyometrik bir analiz. Bulletin of Economic Theory and Analysis, 9(3), 669-698. https://doi.org/10.25229/beta.1487924
  • Fischer, T. ve Krauss, C. (2018). Deep learning with long short-term memory networks for financial market predictions. European Journal of Operational Research, 270(2), 654-669. https://doi.org/10.1016/j.ejor.2017.11.054
  • Gogas, P. ve Papadimitriou, T. (2021). Machine learning in economics and finance. Computational Economics, 57, 1-4. https://doi.org/10.1007/s10614-021-10094-w
  • Gu, S., Kelly, B., ve Xiu, D. (2020). Empirical asset pricing via machine learning. The Review of Financial Studies, 33(5), 2223-2273. https://doi.org/10.1093/rfs/hhaa009
  • Hambly, B., Xu, R., ve Yang, H. (2023). Recent advances in reinforcement learning in finance. Mathematical Finance, 33(3), 437-503. https://doi.org/10.1111/mafi.12382
  • Helmbold, D. P., Schapire, R. E., Singer, Y., ve Warmuth, M. K. (1998). On‐line portfolio selection using multiplicative updates. Mathematical Finance, 8(4), 325-347. https://doi.org/10.1111/1467-9965.00058
  • Hoang, D. ve Wiegratz, K. (2023). Machine learning methods in finance: Recent applications and prospects. European Financial Management, 29(5), 1657-1701. https://doi.org/10.1111/eufm.12408
  • Hoseinzade, E. ve Haratizadeh, S. (2019). CNNpred: CNN-based stock market prediction using a diverse set of variables. Expert Systems with Applications, 129, 273-285. https://doi.org/10.1016/j.eswa.2019.03.029
  • Huang, J., Chai, J., ve Cho, S. (2020). Deep learning in finance and banking: A literature review and classification. Frontiers of Business Research in China, 14(1), 13. https://doi.org/10.1186/s11782-020-00082-6
  • Kim, H. Y. ve Won, C. H. (2018). Forecasting the volatility of stock price index: A hybrid model integrating LSTM with multiple GARCH-type models. Expert Systems with Applications, 103, 25-37. https://doi.org/10.1016/j.eswa.2018.03.002
  • Krauss, C., Do, X. A. ve Huck, N. (2017). Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500. European Journal of Operational Research, 259(2), 689-702. https://doi.org/10.1016/j.ejor.2016.10.031
  • Leippold, M., Wang, Q., ve Zhou, W. (2022). Machine learning in the Chinese stock market. Journal of Financial Economics, 145(2), 64-82. https://doi.org/10.1016/j.jfineco.2021.08.017
  • Li, X., Shang, W. ve Wang, S. (2019). Text-based crude oil price forecasting: A deep learning approach. International Journal of Forecasting, 35(4), 1548-1560. https://doi.org/10.1016/j.ijforecast.2018.07.006
  • Lin, Y., Yan, Y., Xu, J., Liao, Y., ve Ma, F. (2021). Forecasting stock index price using the CEEMDAN-LSTM model. The North American Journal of Economics and Finance, 57, 101421. https://doi.org/10.1016/j.najef.2021.101421 Manela, A. ve Moreira, A. (2017). News implied volatility and disaster concerns. Journal of Financial Economics, 123(1), 137-162. https://doi.org/10.1016/j.jfineco.2016.01.032
  • Manogna, R. L. ve Anand, A. (2024). A bibliometric analysis on the application of deep learning in finance: Status, development and future directions. Kybernetes, 53(12), 5951-5971. https://doi.org/10.1108/K-04-2023-0637
  • Meng, C., Chen, C., Xu, H. ve Li, T. (2024). Asset pricing and portfolio investment management using machine learning: Research trend analysis using scientometrics. Economics, 18(1), 20220108. https://doi.org/10.1515/econ-2022-0108
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Details

Primary Language Turkish
Subjects Econometrics (Other)
Journal Section Articles
Authors

Erdi Bayram 0000-0003-4478-7231

Gökhan Berk Özbek 0000-0003-0288-069X

Publication Date July 20, 2025
Submission Date March 21, 2025
Acceptance Date July 10, 2025
Published in Issue Year 2025 Volume: 34 Issue: Uygarlığın Dönüşümü - Sosyal Bilimlerin Bakışıyla Yapay Zekâ

Cite

APA Bayram, E., & Özbek, G. B. (2025). YAPAY ZEKA TEMELLİ ÖĞRENME YÖNTEMLERİNİN FİNANS ALANINDA KULLANIMI: BİBLİYOMETRİK BİR ANALİZ. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 34(Uygarlığın Dönüşümü - Sosyal Bilimlerin Bakışıyla Yapay Zekâ), 303-321. https://doi.org/10.35379/cusosbil.1662365
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