KaydedilenlerDerginizi ekleyin

Dergiler / Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi / 2026 / Cilt: 22 Sayı: 1

Borsa Endeksi Hareketlerinin Tahmini: Trend Belirleyici Veri

Predicting Stock Market Movement: Trend Deterministic Data

Hakan Pabuçcu(BAYBURT ÜNİVERSİTESİ)0000-0003-2267-5175
Sayfa
246–256
Yayın Tarihi
2019-04-30
🏛️Bu makalenin bibliyografik verileri ve tam metin bağlantısı DergiPark üzerinden sağlanmıştır.
DergiPark Kaydı ↗
✨

Yapay Zeka Hızlı Bakış & Çıkarımlar

AI Insight
⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Ayrıca, daha önceki benzer çalışmalarla karşılaştırmalar yapılarak gerek kullanılan veri seti gerekse tahmin modellerinin etkileri tartışılmaktadır.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Bu çalışma BIST 100 borsa endeksinin negatif ve pozitif yönlü hareketlerinin tahmin edilmesini konu edinmektedir.
📊
Temel Bulgular & Veri:BIST 100 veri seti 2009-2018 Aralığını kapsayan günlük kapanış fiyatlarını içermektedir.
💡
Sonuç & Katkı:Ayrıca, daha önceki benzer çalışmalarla karşılaştırmalar yapılarak gerek kullanılan veri seti gerekse tahmin modellerinin etkileri tartışılmaktadır.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:2009-2018 Verileri
⚙️Değişkenler:BIST100 · Yapay Sinir Ağları · Destek Vektör Makineleri

Özet

Bu çalışma BIST 100 borsa endeksinin negatif ve pozitif yönlü hareketlerinin tahmin edilmesini konu edinmektedir. Yapay sinir ağı, destek vektör makinesi ve naive Bayes algoritmasının tahmin performansları karşılaştırılmaktadır. Analizler iki aşamalı olarak yapılmaktadır. Birinci aşamada tahmin modellerinde girdi olarak kullanılacak dokuz adet teknik gösterge, borsa endeksi açılış, kapanış, en yüksek ve en düşük fiyatlar, kullanılarak hesaplanmakta ve sürekli olan bu teknik göstergeler barındırdıkları trende göre kategorize edilerek yeni bir veri seti oluşturulmaktadır. İkinci aşamada ise, trend belirleyici veri seti girdi olarak kullanılmakta ve seçilen üç makine öğrenme algoritması kullanılarak tahminler yapılmaktadır. BIST 100 veri seti 2009-2018 Aralığını kapsayan günlük kapanış fiyatlarını içermektedir. Analizlerle, destek vektör makineleri algoritmasının en iyi sınıflandırıcı olduğu sonucuna ulaşılmıştır. Ayrıca, daha önceki benzer çalışmalarla karşılaştırmalar yapılarak gerek kullanılan veri seti gerekse tahmin modellerinin etkileri tartışılmaktadır.

Abstract

This study focuses on the estimation of negative and positive movements of BIST 100 stock index. The predictive performances of artificial neural network, support vector machine and naive Bayes algorithm are compared. The analyzes are carried out in two stages. In the first stage, nine technical indicators to be used as input in the estimation models are calculated by using the stock index, opening, closing, highest and lowest prices. In the second stage, the trend-setting dataset is used as input and the predictions are made by using three selected machine-learning algorithms. The BIST 100 data set includes the daily closing prices covering the range of 2009-2018. With the analysis, it is concluded that the support vector machines algorithm is the best classifier. In addition, comparisons with previous similar studies and the effects of both the data set used and the prediction models are discussed.

🔖

Bu Makaleye Atıf Yap

Pabuçcu, H. (2019). Borsa Endeksi Hareketlerinin Tahmini: Trend Belirleyici Veri. Selçuk Üniversitesi Sosyal Bilimler Meslek Yüksekokulu Dergisi, Cilt: 22 Sayı: 1, 246–256. https://doi.org/10.29249/selcuksbmyd.487862

📄 Tam Metin PDF Okuyucu

📄

PDF görüntüleyici hazırlanıyor...

Kaynakça (29)

  1. Abu-Mostafa, Y. S., & Atiya, A. F. (1996). "Introduction to Financial Forecasting", Applied Intelligence, 6(3), s. 205–213. http://doi.org/10.1007/BF00126626
  2. Atsalakis, G. S., & Valavanis, K. P. (2009a). "Forecasting Stock Market Short-Term Trends Using a Neuro-Fuzzy Based Methodology", Expert Systems with Applications, 36(7), s. 10696–10707. http://doi.org/https://doi.org/10.1016/j.eswa.2009.02.043
  3. Atsalakis, G. S., & Valavanis, K. P. (2009b). "Surveying Stock Market Forecasting Techniques – Part II: Soft Computing Methods", Expert Systems with Applications, 36(3, Part 2), s. 5932–5941. http://doi.org/https://doi.org/10.1016/j.eswa.2008.07.006
  4. Cao, L., & Tay, F. E. H. (2001). "Application of support Vector Machines in Financial Time Series Forecasting", Omega, 29(4), s. 309–317. http://doi.org/10.1016/S0305-0483(01)00026-3
  5. Chen, A.-S., Leung, M. T., & Daouk, H. (2003). "Application of Neural Networks to an Emerging Financial Market: Forecasting and Trading the Taiwan Stock Index", Computers & Operations Research, 30(6), s. 901–923. http://doi.org/https://doi.org/10.1016/S0305-0548(02)00037-0
  6. Chen, Z., Matousek, R., & Wanke, P. (2018). "Chinese Bank Efficiency During the Global Financial Crisis: A Combined Approach Using Satisficing Dea and Support Vector Machines", North American Journal of Economics and Finance, 43(September 2017), s. 71–86. http://doi.org/10.1016/j.najef.2017.10.003
  7. Chun, S.-H., & Kim, S. H. (2004). "Data Mining for Financial Prediction and Trading: Application to Single and Multiple Markets", Expert Systems with Applications, 26(2), s., 131–139. http://doi.org/https://doi.org/10.1016/S0957-4174(03)00113-1
  8. Enke, D., & Thawornwong, S. (2005). "The Use of Data Mining and Neural Networks for Forecasting Stock Market Returns", Expert Systems with Applications, 29(4), s. 927–940. http://doi.org/https://doi.org/10.1016/j.eswa.2005.06.024
  9. Hassan, M. R., Nath, B., & Kirley, M. (2007). "A Fusion Model of HMM, ANN and GA for Stock Market Forecasting", Expert Systems with Applications, 33(1), s. 171–180. http://doi.org/https://doi.org/10.1016/j.eswa.2006.04.007
  10. Hsu, S. H., Hsieh, J. P. A., Chih, T. C., & Hsu, K. C. (2009). "A Two-Stage Architecture for Stock Price Forecasting by İntegrating Self-Organizing Map and Support Vector Regression", Expert Systems with Applications, 36(4), s. 7947–7951. http://doi.org/10.1016/j.eswa.2008.10.065
  11. Hua, S., & Sun, Z. (2001). "Support vector machine approach for protein subcellular localization prediction", Bioinformatics, 17(8), s. 721–728.
  12. Huang, W., Nakamori, Y., & Wang, S.-Y. (2005). "Forecasting Stock Market Movement Direction with Support Vector Machine", Computers & Operations Research, 32(10), s. 2513–2522. http://doi.org/https://doi.org/10.1016/j.cor.2004.03.016
  13. Kara, Y., Acar Boyacioglu, M., & Baykan, Ö. K. (2011). "Predicting Direction of Stock Price Index Movement Using Artificial Neural Networks and Support Vector Machines: The Sample of the Istanbul Stock Exchange", Expert Systems with Applications, 38(5), s. 5311–5319. http://doi.org/10.1016/j.eswa.2010.10.027
  14. Khemchandani, R., Jayadeva, & Chandra, S. (2009). "Knowledge Based Proximal Support Vector Machines", European Journal of Operational Research, 195(3), s. 914–923. http://doi.org/https://doi.org/10.1016/j.ejor.2007.11.023
  15. Kim, K. J. (2003). "Financial Time Series Forecasting Using Support Vector Machines", Neurocomputing, 55(1–2), s. 307–319. http://doi.org/10.1016/S0925-2312(03)00372-2
  16. Kimoto, T., Asakawa, K., Yoda, M., & Takeoka, M. (1990). "Stock Market Prediction System with Modular Neural Networks", In 1990 IJCNN International Joint Conference on Neural Networks (s. 1–6 vol.1). http://doi.org/10.1109/IJCNN.1990.137535
  17. Kumar, M., & Thenmozhi, M. (2005). "Forecasting Stock Index Movement: A Comparison of Support Vector Machines and Random Fores". In Forest, Indian Institute of Capital Markets 9th Capital Markets Conference Paper. (s. 1–16). http://doi.org/10.2139/ssrn.876544
  18. Leung, M. T., Daouk, H., & Chen, A.-S. (2000). "Forecasting Stock Indices: A Comparison of Classification and Level Estimation Models", International Journal of Forecasting, 16(2), s. 173–190. http://doi.org/https://doi.org/10.1016/S0169-2070(99)00048-5
  19. Malkiel, B. G., & Fama, E. F. (1970)."Effıcient Capital Markets: A Review of Theory and Empirical Work", The Journal of Finance, 25(2), s. 383–417. http://doi.org/10.1111/j.1540-6261.1970.tb00518.x
  20. Olson, D., & Mossman, C. (2003). "Neural Network Forecasts of Canadian Stock Returns Using Accounting Ratios", International Journal of Forecasting, 19(3), s. 453–465. http://doi.org/https://doi.org/10.1016/S0169-2070(02)00058-4
  21. Patel, J., Shah, S., Thakkar, P., & 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), s. 259–268. http://doi.org/10.1016/j.eswa.2014.07.040
  22. Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015b). "Predicting Stock Market Index Using Fusion of Machine Learning Techniques. Expert Systems with Applications, 42(4), s. 2162–2172. http://doi.org/10.1016/j.eswa.2014.10.031
  23. Tan, T. Z., Quek, C., & Ng, G. S. (2007). "Biological Brain‐Inspired Genetic Complementary Learning for Stock Market and Bank Failure Prediction", Computational Intelligence, 23(2), s.236–261. http://doi.org/10.1111/j.1467-8640.2007.00303.x
  24. Tay, F. E. H., & Cao, L. J. (2001). "Improved Financial Time Series Forecasting by Combining Support Vector Machines with Self-Organizing Feature Map", Intelligent-Data-Analysis, 5, s. 339–354.
  25. Thawornwong, S., & Enke, D. (2004). "The Adaptive Selection of Financial And Economic Variables for Use with Artificial Neural Networks", Neurocomputing, 56, s. 205–232. http://doi.org/https://doi.org/10.1016/j.neucom.2003.05.001
  26. Vanstone, B., & Finnie, G. (2009). "An Empirical Methodology For Developing Stockmarket Trading Systems Using Artificial Neural Networks", Expert Systems with Applications, 36(3, Part 2), s. 6668–6680. http://doi.org/https://doi.org/10.1016/j.eswa.2008.08.019
  27. Vapnik, V. (1995). The Nature of Statistical Learning Theory. New York, NY: Springer.
  28. Xu, X., Zhou, C., & Wang, Z. (2009). "Credit scoring Algorithm Based on Link Analysis Ranking with Support Vector Machine", Expert Systems with Applications, 36(2, Part 2), s. 2625–2632. http://doi.org/https://doi.org/10.1016/j.eswa.2008.01.024
  29. Zhong, X., & Enke, D. (2017). "Forecasting Daily Stock Market Return Using Dimensionality Reduction", Expert Systems with Applications, 67, s. 126–139. http://doi.org/10.1016/j.eswa.2016.09.027