DỰ BÁO THỜI ĐIỂM MUA BÁN CHỨNG KHOÁN KẾT HỢP PHƯƠNG PHÁP PHÂN ĐOẠN DỮ LIỆU VÀ VECTƠ HỖ TRỢ HỒI QUY
Abstract
Trong bài báo này, chúng tôi sử dụng phương pháp hồi quy vectơ hỗ trợ (SVR) và phân đoạn dữ liệu (TBSM) để xây dựng mô hình dự báo thời điểm mua bán chứng khoán tại thị trường Mỹ trên sàn NASDAQ. TBSM được dùng để phân đoạn chuỗi dữ liệu thời gian thành các xu hướng phản ánh đồ thị giá tạo nên những tín hiệu giao dịch, những tín hiệu này kết hợp với các chỉ số kỹ thuật được đưa vào SVR huấn luyện để tìm ra các điểm mua bán. Hướng tiếp cận tập trung nghiên cứu vào việc thay đổi các giá trị ngưỡng và tham số cũng như các tín hiệu tạo điểm giao dịch đem lại lợi nhuận cao hơn so với các nghiên cứu trước.References
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[3] Kumar A, Anshul K, Tej P, Surya P, A Stock Market Prediction Model using Artificial Neural Network, IEEE-20180, 2012.
[4] Chi-Yuan Yeh, Chi-Wei Huang, Shie-Jue Lee, A multiple-kernel support vector regression approach for stock market price forecasting, Expert Systems with Applications 38 (2011) 2177–2186.
[5] Kwon Y. K., Moon B. R., A Hybrid Neurogenetic Approach for Stock Forecasting, IEEE, Vol. 18, No. 3 (2007).
[6] Yanru Xu, Zhengui L. L, A Study on Feature Selection for the Trend Prediction of Stock Trading Price, 2013.
[7] Huang S-C., Wu T-K., Integrating GA-based time-scale feature extractions with SVMs for stock index forecasting, Expert Systems with Applications 35, 2008, pp. 2080–2088.
[8] Huang C-L., Tsai C-Y., A hybrid SOFM-SVR with a filter-based feature selection for stock market forecasting, Expert Systems with Applications 36, 2009, pp. 1529–1539.
[9] Afolabi M. O., Olude O., Predicting Stock Prices Using a Hybrid Kohonen Self Organizing Map, IEEE, 2007.
[10] Jheng-Long Wu, Pei-Chann Chang, and Yi-Fang Pan, A Trend-Based Segmentation Method and the Support Vector Regression for Financial Time Series Forecasting, Mathematical Problems in Engineering Volume 2012.
[11] Pei-Chann Chang, Chin-Yuan Fan, Chen-Hao Liu, Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction, 2009.
[12] Pei-Chann Chang, Chen-Hao Liu, Jun-Lin Lin, Chin-Yuan Fan, Celeste S.P. Ng, A neural network with a case based dynamic window for stock trading prediction, Expert Systems with Applications 36, 2009, 6889–6898.
[13] H. Wu, B. Salzberg, D. Zhang, Online event-driven subsequence matching over financial data streams, in Proc. SIGMOD, Stream Manag, Jun. 2004, pp. 23–34.
[14] J. Murphy, Technical Analysis of Financial Markets, New York Institute of Finance, USA, 1999
[15] T. Turner, A Beginner’s Guide to Day Trading Online, Adams Media, 2nd Ed., 2007
[16] Pittipol Kantavat, Boonserm Kijsirikul, Combining technical analysis and support vector machine for stock trading, IEEE, 2008
Published
2015-12-18
How to Cite
TÂM, Đặng Công; TRANG, Trần Hồng; ĐỨC, Dương Minh.
DỰ BÁO THỜI ĐIỂM MUA BÁN CHỨNG KHOÁN KẾT HỢP PHƯƠNG PHÁP PHÂN ĐOẠN DỮ LIỆU VÀ VECTƠ HỖ TRỢ HỒI QUY.
JBIS, [S.l.], dec. 2015.
Available at: <https://jbis.ueh.edu.vn/index.php/TSTHQL/article/view/76>. Date accessed: 19 may 2024.
Issue
Section
Bài viết
Keywords
TBS Method; Support Vector Regression; Stepwise Regression Analysis
