Các yếu tố ảnh hưởng đến sai số trong dự báo tỷ suất sinh lời của cổ phiếu đơn lẻ: Ứng dụng học máy với Spark MLlib

Abraham, A., Nath, B., & Mahanti, P. K. (2001). Hybrid intelligent systems for stock market analysis. Paper presented at the International Conference on Computational Science, San Francisco, California, USA.

Amihud, Y., Christensen, B. J., & Mendelson, H. (1992). Further evidence on the risk-return relationship (Vol. 11). Stanford, CA: Stanford University.

Atsalakis, G. S., & Valavanis, K. P. (2009). Surveying stock market forecasting techniques - Part II: Soft computing methods. Expert Systems with Applications, 36(3), 5932-5941.

Banz, R. W. (1981). The relationship between return and market value of common stocks. Journal of Financial Economics, 9(1), 3-18.

Basu, S. (1983). The relationship between earnings’ yield, market value and return for NYSE common stocks: Further evidence. Journal of Financial Economics, 12(1), 129-156.

Black, F. (1972). Capital market equilibrium with restricted borrowing. The Journal of Business, 45(3), 444-455.

Breen, W. J., & Korajczyk, R. A. (1993). On selection biases in book-to-market based tests of asset pricing models. Evanston, IL: Northwestern University.

Bui, K. T., & Thai, T. D. (2021). Capital structure and trade-off theory: Evidence from Vietnam. The Journal of Asian Finance, Economics, and Business, 8(1), 45-52. doi:10.13106/jafeb.2021.vol8.no1.045

Bui, K. T., & Tran, H. T. (2021). Support vector regression algorithm under in the CAPM Framework. Paper presented at the 2021 International Conference on Data Analytics for Business and Industry (ICDABI), Sakheer, Bahrain. doi:10.1109/ICDABI53623.2021.9655797

Cao, L.-J., & Tay, F. E. H. (2003). Support vector machine with adaptive parameters in financial time series forecasting. IEEE Transactions on Neural Networks, 14(6), 1506-1518.

Chaudhary, P. (2017). Testing of CAPM in Indian context. Business Analyst, 37(1), 1-18.

Chen, H., Xiao, K., Sun, J., & Wu, S. (2017). A double-layer neural network framework for high-frequency forecasting. ACM Transactions on Management Information Systems (TMIS), 7(4), 1-17.

Cortes, C., & Vapnik, V. (1995). Support-vector networks. Machine Learning, 20(3), 273-297.

da Silva Fonseca, J. G., Jr., Oozeki, T., Takashima, T., Koshimizu, G., Uchida, Y., & Ogimoto, K. (2012). Use of support vector regression and numerically predicted cloudiness to forecast power output of a photovoltaic power plant in Kitakyushu, Japan. Progress in Photovoltaics: Research and Applications, 20(7), 874-882.

Fama, E. F. (1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417.

Fama, E. F. (1991). Efficient capital markets II. The Journal of Finance, 46(5), 1575-1617.

Fama, E. F., & French, K. R. (2021). The cross-section of expected stock returns. Chicago, IL: University of Chicago Press.

Fama, E. F., & James, D. (1973). Equilibrium: Empirical tests. The Journal of Political Economy, 81(3), 607-636.

Gogas, P., Papadimitriou, T., & Karagkiozis, D. (2018). The Fama 3 and Fama 5 factor models under a machine learning framework. Truy cập ngày 10/10/2021 tại https://ideas.repec.org/p/rim/rimwps/18-05.html

Graham, J. R., & Harvey, C. R. (2001). The theory and practice of corporate finance: Evidence from the field. Journal of Financial Economics, 60(2/3), 187-243.

Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2018). Stock price prediction using support vector regression on daily and up to the minute prices. The Journal of Finance and Data Science, 4(3), 183-201.

Jagannathan, R., & McGrattan, E. R. (1995). The CAPM debate. Federal Reserve Bank of Minneapolis Quarterly Review, 19(4), 2-17.

Kumar, M., & Thenmozhi, M. (2014). Forecasting stock index returns using ARIMA-SVM, ARIMA-ANN, and ARIMA-random forest hybrid models. International Journal of Banking, Accounting and Finance, 5(3), 284-308.

Lohano, K., & Kashif, M. (2018). Testing asset pricing models on the Pakistan stock exchange. IBA Business Review, 13(2), 1-19.

Malkiel, B. G. (2003). The efficient market hypothesis and its critics. Journal of Economic Perspectives, 17(1), 59-82.

Markowitz, H. (1952). Portfolio selection. Journal of Finance, 7(1), 77-91.

Patel, J., Shah, S., Thakkar, P., & Kotecha, K. (2015). Predicting stock market index using fusion of machine learning techniques. Expert Systems with Applications, 42(4), 2162-2172.

Qu, H., & Zhang, Y. (2016). A new kernel of support vector regression for forecasting high-frequency stock returns. Mathematical Problems in Engineering, 2016, 1-9. doi:10.1155/2016/4907654

Ramedani, Z., Omid, M., Keyhani, A., Shamshirband, S., & Khoshnevisan, B. (2014). Potential of radial basis function based support vector regression for global solar radiation prediction. Renewable and Sustainable Energy Reviews, 39, 1005-1011.

Tay, F. E., & Cao, L. (2001). Application of support vector machines in financial time series forecasting. Omega, 29(4), 309-317.

Tong, J. (2015). The price forecasting of military aircraft based on SVR. Journal of Computer and Communications, 3(5), 234-238.

Tran, H. T. (2020). Application of Machine Learning in CAPM (Master’s thesis, University of Economics Ho Chi Minh City, Ho Chi Minh City, Vietnam). Truy cập ngày 10/10/2021 tại https://opac.ueh.edu.vn/record=b1032827~S8

Tran, K. T., Banh, T. T., & Nguyen, A. H. T. (2012). Dự đoán giá cổ phiếu trên thị trường chứng khoán Việt Nam bằng phương pháp lai GA-SVR [Predicting stock prices on Vietnam stock market by hybrid method GA-SVR]. Chuyên san Các công trình nghiên cứu, phát triển và ứng dụng Công nghệ thông tin và Truyền thông, V-1(7), 12-22.

Treynor, J. L. (1961). Market value, time, and risk. Truy cập ngày 10/10/2021 tại https://ssrn.com/abstract=2600356

Trinh, N. T. (2013). Dự đoán xu hướng thị trường chứng khoán bằng cách sử dụng Twitter [Predict stock market trends using Twitter] (Master’s thesis). Vietnam National University Hanoi, Hanoi, Vietnam.

Vapnik, V. (2013). The nature of statistical learning theory. New York, NY: Springer Science & Business Media.

Wang, J.-J., Wang, J.-Z., Zhang, Z.-G., & Guo, S.-P. (2012). Stock index forecasting based on a hybrid model. Omega, 40(6), 758-766.

Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153-163.

Yuan, F.-C., Lee, C.-H., & Chiu, C. (2020). Using market sentiment analysis and genetic algorithm-based least squares support vector regression to predict gold prices. International Journal of Computational Intelligence Systems, 13(1), 234-246.

Zhang, N., Lin, A., & Shang, P. (2017). Multidimensional k-nearest neighbor model based on EEMD for financial time series forecasting. Physica A: Statistical Mechanics and Its Applications, 477, 161-173.

Zhong, X., & Enke, D. (2017). Forecasting daily stock market return using dimensionality reduction. Expert Systems with Applications, 67, 126-139.

Xem thêm