This work is licensed under the Creative Commons Attribution 4.0 International License.
Yu Hua. Empirecal Analysis of Quantitative Investment Trend Strategy [J]. Financial Management, 2019, (04):189.Search in Google Scholar
Hiemstra Y. Linear Regression versus Back Propagation Networks to Predict Quarterly Stock Market Excess Returns [J]. Computational Economics, 1996, 9(1):67–76.Search in Google Scholar
Huang C F, Hsieh T N, Chang B R, et al. A Comparative Study of Stock Scoring Using Regression and Genetic-Based Linear Models [C]. IEEE International Conference on Granular Computing. IEEE, 2012.Search in Google Scholar
Huang HY, Wang M, Zhu J M. Multi factor stock selection model based on multiple regression analysis [J]. Journal of TongHua Normal University, 2016, (8):3.Search in Google Scholar
Chaudhary S, Arora V, Singh V. Regression based on Stock Selection Market Prediction [J]. IJARIIE, 2018, 4(3).Search in Google Scholar
Chen M X, Wu M, Wu H, et al. Logistic Forecasting Stock Selection Model based on Financial Driving Factors [J]. Economic and Trading Practice, 2018, (12X):2.Search in Google Scholar
Steve Craighead, Bruce Klemesrud. Stock Selection Based on Cluster and Outlier Analysis [C]. IMA Conference. 2002.Search in Google Scholar
Newton Da Costa, Jefferson Cunha, Sergio Da Silva. Stock Selection Based on Cluster Analysis [J]. Finance, 2005, 13(1):1–9.Search in Google Scholar
Ruizhong Wang. Stock Selection Based on Data Clustering Method [C]. 2011 Seventh International Conference on Computational Intelligence and Security, IEEE, 2011.Search in Google Scholar
Ratchata Peachavanish. Stock Selection and Trading Based on Cluster Analysis of Trend and Momentum Indicators [J]. Proceedings of the International MultiConference of Engineers and Computer Scientists, 2016.Search in Google Scholar
Li Wenxing, Li Junqi. Improvement of Semi-supervised Kernel Clustering Algorithm Based on Multi-Factor Stock Selection [J]. Statistics & Information Forum, 2018, 33(3):30–36.Search in Google Scholar
Lee M C. Using Support Vector Machine with a Hybrid Feature Selection Method to the Stock Trend Prediction [J]. Expert Systems with Applications, 2009, 36(8):10896–10904.Search in Google Scholar
Zikowski K. Using Volume Weighted Support Vector Machines with Walk Forward Testing and Feature Selection for the Purpose of Creating Stock Trading Strategy [J]. Expert Systems with Applications, 2015, 42(4):1797–1805.Search in Google Scholar
Ru Z, Zi-Ang L, Shaozhen C, et al. Adaboost-SVM Multi-Factor Stock Selection Model Based on Adaboost Enhancement [J]. International Journal of Statistics & Probability, 2018, 7(5):9–18.Search in Google Scholar
Zhang R, Lin Z A, Chen S, et al. Multi-factor Stock Selection Model Based on Kernel Support Vector Machine [J]. Journal of Mathematics Research, 2018, 10(5):119–129.Search in Google Scholar
Gao Anjing. Research on Stock Selection Based on The Method of Dynamic Fuzzy Integration Support Vector Machine [D]. Harbin Institute of Technology, 2017.Search in Google Scholar
Meng Qingyan. Optimizing Multi-Factor Stock Selection System Using GBDT-SVM Multi-Level Model [J]. Statistics and Application, 2019, 8(1):184–192.Search in Google Scholar
Tan Z, Yan Z, Zhu G. Stock Selection with Random Forest: An Exploitation of Excess Return in the Chinese Stock Market [J]. Heliyon, 2019, 5(8):e02310.Search in Google Scholar
Li Qi, Yang Junqi. Application on Random Forest Algorithm in Multi-Factor Stock Selection [J]. Manager Journal, 2017, (2):243.Search in Google Scholar
Xiangkun Zheng. Multi-Factor Model Based on the Minimal Spanning Tree [D]. Hebei University of Technology, 2016.Search in Google Scholar
Jia Xiujuan. Quantitative Stock Selection Based on Support Vector Machine of Random Forest [J]. Journal of Regional Financial Research, 2019, (1):27–30.Search in Google Scholar
Asriel E. Levin. Stock Selection via Nonlinear Multi-Factor Models [C]. Advances in Neural Information Processing Systems 8, Nips, Denver, Co, November. DBLP, 1996.Search in Google Scholar
Ghosn J, Bengio Y. Multi-Task Learning for Stock Selection [C]. Advances in Neural Information Processing Systems 9, Nips, Denver, Co, Usa, December. DBLP, 1997.Search in Google Scholar
Quah T S, Srinivasan B. Improving Returns on Stock Investment through Neural Network Selection [J]. Expert Systems with Applications, 2006, 17(4):295–301.Search in Google Scholar
Stanley, G, Eakins, et al. Can Value-Based Stock Selection Criteria Yield Superior Risk-Adjusted Returns: An Application of Neural Networks [J]. International Review of Financial Analysis, 2003.Search in Google Scholar
Yeh, I-Cheng, Liu, et al. Using Mixture Design and Neural Networks to Build Stock Selection Decision Support Systems [J]. Neural Computing & Applications, 2017.Search in Google Scholar
Kim G H, Kim S H. Variable Selection for Artificial Neural Networks with Applications for Stock Price Prediction [J]. Applied Artificial Intelligence, 2018:1–14.Search in Google Scholar
Yuxuan Huang, Luiz Femando Capretz, Danny Ho. Neural Network Models for Stock Selection Based on Fundamental Analysis [J]. 2019 IEEE Canadian Conference of Electrical and Computer Engineering, IEEE, 2019.Search in Google Scholar
Tsai C F, Hsiao Y C. Combining Multiple Feature Selection Methods for Stock Prediction: Union, Intersection, and Multi-Intersection Approaches [J]. Decision Support Systems, 2011, 50(1):258–269.Search in Google Scholar
Lan Taiqiang. An Empirical Study on Comprehensive Stock Selection Based on Principal Component Analysis and BP Neural Network [D]. Ji’nan University, 2017.Search in Google Scholar
Tong-Seng Quah. Using Neural Network for DJIA Stock Selection [J]. Engineering Letters, 2007.Search in Google Scholar
Hui Yang, Yingying Zhu, Qiang Huang. A Multi-Indicator Feature Selection for CNN-Driven Stock Index Prediction [J]. Springer Nature Switzerland AG 2018, 2018:35–46.Search in Google Scholar
Zhang X, Tan Y. Deep Stock Ranker: A LSTM Neural Network Model for Stock Selection [M]. Data Mining and Big Data. 2018.Search in Google Scholar
Zhang R, Huang C, Zhang W, et al. Multi Factor Stock Selection Model Based on LSTM [J]. International Journal of Economics & Finance, 2018, 10(8):36–42.Search in Google Scholar
Sun J. A Stock Selection Method Based on Earning Yield Forecast Using Sequence Prediction Models [J]. Papers, 2019.Search in Google Scholar
Zhou Zhiyuan. Research and Application of Multi-Factor Stock Selection Model Based on RNN-ACT Algorithm [D]. Kunming University of Technology, 2018.Search in Google Scholar