TY  - JOUR
T1  - Derin Öğrenme Tabanlı Fiyat Tahmini ve Algoritmik Ticaret: BİST100 Endeksinde Bir Uygulama
TT  - Deep Learning Based Price Prediction and Algorithmic Trading on BIST100
AU  - Salur, Mehmet Nuri
AU  - Akusta, Ahmet
PY  - 2024
DA  - September
Y2  - 2024
DO  - 10.25295/fsecon.1447129
JF  - Fiscaoeconomia
JO  - FSECON
PB  - Ahmet Arif EREN
WT  - DergiPark
SN  - 2564-7504
SP  - 1194
EP  - 1215
VL  - 8
IS  - 3
LA  - tr
AB  - Bu araştırma, BİST100 endeksinde yer alan hisse senetlerinin alım satımı için derin öğrenme tabanlı metodolojilerin kullanılmasını ele almaktadır. Özellikle, son dönemdeki piyasa dalgalanmaları üzerine yoğunlaşılmıştır. Tahmine Dayalı İşlem Algoritması (TDİA) adı verilen, derin öğrenme esaslı bir işlem algoritması geliştirilmiş ve bu algoritmanın BİST100'de temsil edilen çeşitli sektörlerdeki hisse senedi hareketlerini tahmin etme ve işlem gerçekleştirme başarısı değerlendirilmiştir. Çalışma, Ağustos 2022'den Aralık 2023'e kadar olan ve toplam 270 işlem gününü kapsayan veriler üzerine kuruludur. Algoritmik ticaret, ticaretin yürütülmesinde sağladığı verimlilik, hız ve hassasiyet sayesinde modern finans dünyasında önemli bir yere sahiptir. Özellikle BİST100 gibi dinamik piyasalarda, algoritmik alım satımın önemi, geleneksel stratejilerin hızlı değişimlere ve karmaşıklıklara uyum sağlama konusundaki zorlukları nedeniyle daha da belirginleşmektedir. Bu çalışmada benimsenen metodoloji, geçmiş fiyat, hacim, hisse senedi endeksi ve döviz kuru verilerini kullanarak gelecekteki hisse senedi hareketlerini tahmin etmeye yönelik derin öğrenme modelinin geliştirilmesini ve uygulanmasını kapsamaktadır. Bu model, alım veya satım emirlerini gerçekleştirmek üzere tanımlanmış kurallar seti üzerinde çalışan bir Tahmine Dayalı İşlem Algoritması'nın temelini oluşturmaktadır. Araştırmanın temel bulguları, TDİA'nın seçilen hisse senetlerinde ortalama %15,87 kar ile kayda değer bir başarı elde ettiğini göstermektedir. Bu sonuçlar, algoritmik ticaretin potansiyelini ve derin öğrenme metodolojilerinin finansal piyasalarda kullanımının etkinliğini vurgulamaktadır.
KW  - Algoritmik Ticaret
KW  - Fiyat Tahmini
KW  - Derin Öğrenme
KW  - Tahmine Dayalı İşlem Algoritması
N2  - This research addresses using deep learning-based methodologies for trading stocks in the BIST100 index. In particular, the focus is on recent market fluctuations. A deep learning-based trading algorithm called Predictive Trading Algorithm (PTA) is developed, and its success in predicting stock movements in various sectors represented in the BIST100 is evaluated. The study is based on data from August 2022 to December 2023, covering 270 trading days. Algorithmic trading is essential in the modern financial world thanks to its efficiency, speed, and precision in trade execution. Especially in dynamic markets such as the BIST100, the importance of algorithmic trading becomes even more evident due to the difficulties of traditional strategies in adapting to rapid changes and complexities. The methodology adopted in this study involves developing and applying a deep learning model to predict future stock movements using historical price, volume, stock index, and exchange rate data. This model forms the basis of a Predictive Trading Algorithm based on a defined set of rules to execute buy or sell orders. The main findings of the research show that the PTRA achieves remarkable success with an average profit of 15.87% on the selected stocks. These results emphasize the potential of algorithmic trading and the effectiveness of using deep learning methodologies in financial markets.
CR  - Adegboye, A., Kampouridis, M. & Otero, F. (2023). Algorithmic Trading with Directional Changes. Artificial Intelligence Review, 56(6), 5619-5644. https://doi.org/10.1007/S10462-022-10307-0/TABLES/14
CR  - Aloud, M. E. & Alkhamees, N. (2021). Intelligent Algorithmic Trading Strategy Using Reinforcement Learning and Directional Change. IEEE Access, 9, 114659-114671. https://doi.org/10.1109/ACCESS.2021.3105259
CR  - Boehmer, E., Fong, K. & Wu, J. J. (2015). Algorithmic Trading and Market Quality: International Evidence. Journal of Financial and Quantitative Analysis, 56(8), 2659-2688. https://doi.org/10.1017/S0022109020000782
CR  - Cartea, Á., Jaimungal, S. & Kinzebulatov, D. (2016). Algorithmic Trading with Learning. International Journal of Theoretical and Applied Finance, 19(4). https://doi.org/10.1142/S021902491650028X
CR  - Cartea, Á., Jaimungal, S. & Ricci, J. (2018). Algorithmic Trading, Stochastic Control, and Mutually Exciting Processes. SIAM Review, 60(3), 673-703. https://doi.org/10.1137/18M1176968
CR  - Chaboud, A. P., Chiquoine, B., Hjalmarsson, E. & Vega, C. (2014). Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market. The Journal of Finance, 69(5), 2045-2084. https://doi.org/10.1111/JOFI.12186
CR  - Çankal, A. & Yakut, E. (2016). Portfolio Optimization Using of Methods Multi Objective Genetic Algorithm and Goal Programming: An Application in BIST-30. Business and Economics Research Journal, 7(2), 43-43. https://doi.org/10.20409/BERJ.2016217495
CR  - Garcia, D. & Schweitzer, F. (2015). Social Signals and Algorithmic Trading of Bitcoin. Royal Society Open Science. https://doi.org/10.1098/rsos.150288
CR  - Hansen, K. B. (2020). The Virtue of Simplicity: On Machine Learning Models in Algorithmic Trading. Big Data & Society. https://doi.org/10.1177/2053951720926558
CR  - Hatch, B. C., Johnson, S. A., Wang, Q. E. & Zhang, J. (2021). Algorithmic Trading and Firm Value. Journal of Banking & Finance, 125, 106090. https://doi.org/10.1016/J.JBANKFIN.2021.106090
CR  - Kalaycı, C. B., Ertenlıce, O., Akyer, H. & Aygören, H. (2017). Ortalama-Varyans Portföy Optimizasyonunda Genetik Algoritma Uygulamaları Üzerine Bir Literatür Araştırması. Pamukkale Üniversitesi Mühendislik Bilimleri Dergisi, 23(4), 470-476. https://gcris.pau.edu.tr/handle/11499/43710
CR  - Kelejian, H. H. & Mukerji, P. (2016). Does High Frequency Algorithmic Trading Matter for Non-AT Investors?. Research in International Business and Finance, 37, 78-92. https://doi.org/10.1016/J.RIBAF.2015.10.014
CR  - Koegelenberg, D. J. C. & van Vuuren, J. H. (2024). A Dynamic Price Jump Exit and Re-Entry Strategy for Intraday Trading Algorithms Based on Market Volatility. Expert Systems with Applications, 243, 122892. https://doi.org/10.1016/J.ESWA.2023.122892
CR  - Li, Y., Zheng, W. & Zheng, Z. (2019). Deep Robust Reinforcement Learning for Practical Algorithmic Trading. IEEE Access, 7, 108014-108021. https://doi.org/10.1109/ACCESS.2019.2932789
CR  - Liu, P., Zhang, Y., Bao, F., Yao, X. & Zhang, C. (2023). Multi-Type Data Fusion Framework Based on Deep Reinforcement Learning for Algorithmic Trading. Applied Intelligence, 53(2), 1683-1706. https://doi.org/10.1007/S10489-022-03321-W/TABLES/15
CR  - Lu, B., Hao, S., Pinedo, M. & Xu, Y. (2021). Frontiers in Service Science: Fintech Operations-An Overview of Recent Developments and Future Research Directions. Service Science. https://doi.org/10.1287/serv.2021.0270
CR  - MacKenzie, D. (2018). ‘Making’, ‘Taking’ and the Material Political Economy of Algorithmic Trading. Economy and Society, 47(4), 501-523. https://doi.org/10.1080/03085147.2018.1528076
CR  - Massei, G. (2023). Algorithmic Trading: An Overview and Evaluation of Its Impact on Financial Markets. http://dspace.unive.it/handle/10579/23509
CR  - Ponomarev, E. S., Oseledets, I. V., & Cichocki, A. S. (2019). Using Reinforcement Learning in the Algorithmic Trading Problem. Journal of Communications Technology and Electronics, 64(12), 1450-1457. https://doi.org/10.1134/S1064226919120131/FIGURES/7
CR  - Pricope, T.-V. (2021). Deep Reinforcement Learning in Quantitative Algorithmic Trading: A Review. https://arxiv.org/abs/2106.00123v1
CR  - Sazu, M. H. (2022). How Machine Learning Can Drive High Frequency Algorithmic Trading for Technology Stocks. International Journal of Data Science and Advanced Analytics, 4(4), 84-93. https://ijdsaa.com/index.php/welcome/article/view/97
CR  - Shavandi, A., & Khedmati, M. (2022). A multi-agent deep reinforcement learning framework for algorithmic trading in financial markets. Expert Systems with Applications, 208, 118124. https://doi.org/10.1016/J.ESWA.2022.118124
CR  - Stefano, V. D., & Taes, S. (2022). Algorithmic Management and Collective Bargaining. Transfer European Review of Labour and Research. https://doi.org/10.1177/10242589221141055
CR  - Tao, R., Su, C. W., Xiao, Y., Dai, K. & Khalid, F. (2021). Robo Advisors, Algorithmic Trading and Investment Management: Wonders of Fourth Industrial Revolution in Financial Markets. Technological Forecasting and Social Change, 163, 120421. https://doi.org/10.1016/J.TECHFORE.2020.120421
CR  - Théate, T. & Ernst, D. (2021). An Application of Deep Reinforcement Learning to Algorithmic Trading. Expert Systems with Applications, 173, 114632. https://doi.org/10.1016/J.ESWA.2021.114632
CR  - Uğur, Ö., Aladağlı, E. E. & Tekin, Ö. (2018). Algoritmik Ticaret ve Finansal Araçlar için Gerçek Zamanlı Çalışan Bir Prototip. https://open.metu.edu.tr/handle/11511/61719
CR  - Upson, J. & Van Ness, R. A. (2017). Multiple Markets, Algorithmic Trading, and Market Liquidity. Journal of Financial Markets, 32, 49-68. https://doi.org/10.1016/J.FINMAR.2016.05.004
CR  - Vo, A. & Yost-Bremm, C. (2020). A High-Frequency Algorithmic Trading Strategy for Cryptocurrency. Journal of Computer Information Systems, 60(6), 555-568. https://doi.org/10.1080/08874417.2018.1552090
CR  - Weller, B. M. (2018). Does Algorithmic Trading Reduce Information Acquisition?. The Review of Financial Studies, 31(6), 2184-2226. https://doi.org/10.1093/RFS/HHX137
CR  - Yadav, Y. (2015). How Algorithmic Trading Undermines Efficiency in Capital Markets. Vanderbilt Law Review, 68(6), 1607-1671.
CR  - Zhang, X., Zhang, Y., Wang, S., Yao, Y., Fang, B. & Yu, P. S. (2018). Improving Stock Market Prediction via Heterogeneous Information Fusion. Knowledge-Based Systems. https://doi.org/10.1016/j.knosys.2017.12.025
CR  - Zulkifli, Z. S., Surip, M., Mohammad, H., Zamri, N., Mamat, M. & Idris, N. S. U. (2023). Algorithmic Trading System Based on Technical Indicators in Artificial Intelligence: A Review. AIP Conference Proceedings, 2484(1). https://doi.org/10.1063/5.0110055/2879570
UR  - https://doi.org/10.25295/fsecon.1447129
L1  - https://dergipark.org.tr/en/download/article-file/3772592
ER  -