SavedAdd your journal

Journals / Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi / 2026 / Cilt: 34 Sayı: Uygarlığın Dönüşümü: Yapay Zekâ Özel Sayı

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

YAPAY ZEKA TEMELLİ ÖĞRENME YÖNTEMLERİNİN FİNANS ALANINDA KULLANIMI: BİBLİYOMETRİK BİR ANALİZ

Erdi Bayram(MANISA CELAL BAYAR UNIVERSITY)0000-0003-4478-7231
Gökhan Berk Özbek(BURSA ULUDAG UNIVERSITY)0000-0003-0288-069X
Pages
303–321
Publication Date
2025-07-20
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The findings reveal a significant increase in the number of publications in this field, particularly after 2018.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The study presents a bibliometric analysis of research in finance focusing on machine learning, deep learning, and reinforcement learning.
📊
Key Findings & Data: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.
💡
Conclusion & Impact:The findings reveal a significant increase in the number of publications in this field, particularly after 2018.
🔬Design:Modeling & Simulation
📍Scope:2001 Verileri
⚙️Variables:Makine Öğrenimi · Derin Öğrenme · Pekiştirmeli Öğrenme

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.

Özet

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.

🔖

Cite This Article

Bayram, E., Özbek, G. B. (2025). USING OF ARTIFICIAL INTELLIGENCE-BASED LEARNING METHODS IN FINANCE: A BIBLIOMETRIC ANALYSIS. Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, Cilt: 34 Sayı: Uygarlığın Dönüşümü: Yapay Zekâ Özel Sayı, 303–321. https://doi.org/10.35379/cusosbil.1662365

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (50)

  1. 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
  2. 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
  3. 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
  4. 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
  5. Aydın, N. (2024). Bibliyometrik analiz nasıl yapılır: Genel bir bakış. Balkan ve Yakın Doğu Sosyal Bilimler Dergisi. 19(Özel Sayı), 153-160.
  6. 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
  7. 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
  8. 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
  9. 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
  10. Bucci, A. (2020). Realized volatility forecasting with neural networks. Journal of Financial Econometrics, 18(3), 502-531. https://doi.org/10.1093/jjfinec/nbaa008
  11. 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
  12. 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
  13. 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
  14. 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
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. Moral-Munoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A. ve Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. El Profesional de La Información, 29(1). https://doi.org/10.3145/epi.2020.ene.03
  38. Nikou, M., Mansourfar, G. ve Bagherzadeh, J. (2019). Stock price prediction using DEEP learning algorithm and its comparison with machine learning algorithms. Intelligent Systems in Accounting, Finance and Management, 26(4), 164-174. https://doi.org/10.1002/isaf.1459
  39. Oliveira, N., Cortez, P. ve Areal, N. (2017). The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices. Expert Systems with Applications, 73, 125-144. https://doi.org/10.1016/j.eswa.2016.12.036
  40. Patel, J., Shah, S., Thakkar, P. ve Kotecha, K. (2015a). Predicting stock and stock price index movement using trend deterministic data preparation and machine learning techniques. Expert Systems with Applications, 42(1), 259-268. https://doi.org/10.1016/j.eswa.2014.07.040
  41. Patel, J., Shah, S., Thakkar, P. ve Kotecha, K. (2015b). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4), 2162-2172. https://doi.org/10.1016/j.eswa.2014.10.031
  42. Rasekhschaffe, K. C. ve Jones, R. C. (2019). Machine learning for stock selection. Financial Analysts Journal, 75(3), 70-88. https://doi.org/10.1080/0015198X.2019.1596678
  43. Renault, T. (2017). Intraday online investor sentiment and return patterns in the US stock market. Journal of Banking & Finance, 84, 25-40. https://doi.org/10.1016/j.jbankfin.2017.07.002
  44. Samitas, A., Kampouris, E. ve Kenourgios, D. (2020). Machine learning as an early warning system to predict financial crisis. International Review of Financial Analysis, 71, 101507. https://doi.org/10.1016/j.irfa.2020.101507
  45. Sapankevych, N. ve Sankar, R. (2009). Time series prediction using support vector machines: A survey. IEEE Computational Intelligence Magazine, 4(2), 24-38. https://doi.org/10.1109/MCI.2009.932254
  46. Schumaker, R. P. ve Chen, H. (2009). Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems, 27(2), 1-19. https://doi.org/10.1145/1462198.1462204
  47. Sirignano, J. ve Cont, R. (2021). Universal features of price formation in financial markets: perspectives from deep learning. G. Creamer, G. Kazantsev ve T. Aste (Eds.), Machine Learning and AI in Finance (s. 5-15). Routledge.
  48. White, H. (1988, Temmuz). Economic prediction using neural networks: The case of IBM daily stock returns. [Konferans sunumu özeti]. IEEE 1988 International Conference on Neural Networks, San Diego, CA, Amerika Birleşik Devletleri
  49. Yürük, M. F. ve Ekşi, İ. H. (2019). Yapay zeka yöntemleri ile işletmelerin finansal başarısızlığının tahmin edilmesi: BİST imalat sektörü uygulaması. Mukaddime, 10(1), 393-422. https://doi.org/10.19059/mukaddime.533151
  50. Zhong, X. ve Enke, D. (2019). Predicting the daily return direction of the stock market using hybrid machine learning algorithms. Financial Innovation, 5(1), 1-20. https://doi.org/10.1186/s40854-019-0138-0