NASDAQ 100 INDEX ESTİMATE WİTH MACHİNE LEARNİNG ALGORİTHMS

Year 2022, Volume: 6 Issue: 16, 124 - 143, 28.07.2022

Zeynep Şengül

Abstract

Investors want to be informed about the future to ensure maximum profit and minimal damage before making an investment decision. Today, changes in the stock market are influenced not only by the economy but also by external factors, resulting in sudden ups and downs. Individual and institutional investors who make investment decisions follow stock indexes and take advantage of technical and basic analysis. In this study, the daily closing data of the NASDAQ index was brought back from 2016-2021 to the next day by using Linear, Polynomial, Sigmoid, Radial based Support Vector regressions, Random Forest Regression, K-nearest neighbors Regression algorithms. For model performance evaluation, it is determined that the RBF-DVR is the best prediction by comparing MSE, RMSE, MPE and R2 values

Keywords

NASDAQ 100 Index, Support Vector Regression, Random Forest Regression, K - Nearest Neighbors Regression

References

• Alhnaity, B., & Abbod, M. (2020). A new hybrid financial time series prediction model. Engineering Applications of Artificial Intelligence, 95, 103873.
• Breiman, L. (2001). Random forests. Machine learning, 45(1), 5-32.
• Cutler, A., Cutler, D. R., & Stevens, J. R. (2012). Random forests. In Ensemble machine learning (pp. 157-175). Springer, Boston, MA.
• Doad, P. K., & Bartere, M. M. (2013). A Review: Study of Various Clustering Techniques. International Journal of Engineering Research & Technology, 2(11), 3141-3145.
• Filiz, E., Karaboğa, H. A., & Akoğul, S. (2017). BIST-50 ENDEKSİ DEĞİŞİM DEĞERLERİNİN SINIFLANDIRILMASINDA MAKİNE ÖĞRENMESİ YÖNTEMLERİ VE YAPAY SİNİR AĞLARI KULLANIMI.Çukurova Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 26(1), 231-241.
• Friedman, J., Hastie, T., & Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics, 28(2), 337-407.
• Meesad, P., & Rasel, R. I. (2013, May). Predicting stock market price using support vector regression. In 2013 International Conference on Informatics, Electronics and Vision (ICIEV) (pp. 1-6). IEEE.
• Mitchell, T.M., (1997). Machine learning.
• Polamuri, S. R., Srinivas, K., & Mohan, A. K. (2019). Stock market prices prediction using random forest and extra tree regression. International Journal of Recent Technology and Engineering., 8(3), 1224-1228.
• Taşcı, E., & Onan, A. (2016). K-en yakın komşu algoritması parametrelerinin sınıflandırma performansı üzerine etkisinin incelenmesi. Akademik Bilişim, 1(1), 4-18.
• Tay, F. E., & Cao, L. (2001). Application of support vector machines in financial time series forecasting. omega, 29(4), 309-317.
• Vapnik Vladimir, N. (1995). The nature of statistical learning theory.
• Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia computer science, 167, 599-606.
• Zheng, C., Kasprowicz, C. G., & Saunders, C. (2017). Customized Routing Optimization Based on Gradient Boost Regressor Model. arXiv preprint arXiv:1710.11118.