[1] 王锴. 主成分Logistic回归模型在国债期货跨品种套利中的应用[N]. 期货日报, 2020-06-15. [2] ALDRIDGE I. High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems[M]. Hoboken, NJ: Wiley, 2010. [3] BROGAARD J, HENDERSHOTT T, RIORDANR R. High frequency trading and price discovery[J]. The Review of Financial Studies, 2014, 27(8): 2267-2306. [4] ANGEL J, MCCABE D. Fairness in financial markets: The case of high frequency trading[J]. Journal of Business Ethis, 2013, 112: 585-595. [5] HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18: 1527-1554. [6] GOODFELLOW I, BENGIO Y, COURVILLE A. Deep Learning[M]. Cambridge, MA: The MIT Press, 2017. [7] WANG M, DENG W. Deep face recognition[C]// [2020-02-10]. https://arxiv.org/abs/1804.06655. [8] KARPATHY A, TODERICI G, SHETTY S, et al. Large-scale video classification with convolutional neural networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2014: 1725-1732. [9] DING Y, LIU Yang, LUAN H, et al. Visualizing and understanding neural machine translation[C]// Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics. Vancouver, Canada: ACL, 2017: 1150-1159. [10] QIAN Y, BI M, TAN T,et al. Very deep convolutional neural networks for noise robust speech recognition[C]// IEEE/ACM Transactions on Audio, Speech, and Language Processing. IEEE, 2016, 42(12): 2263-2276. [11] GOODFELLOW I, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]// Advances in Neural Information Processing Systems 27. ACM, 2014. [12] SALIMANS T, GOODFELLOW I, ZAREMBA W, et al. Improved techniques for training GANs[C]// Advances in Neural Information Processing Systems 29. ACM, 2016: 2234-2242. [13] YANG J, KANNAN A, BATRA D, et al. LR-GAN: Layered recursive generative adversarial networks for image generation[DB/OL]. [2020-02-10]. https://arxiv.org/abs/1703.01560. [14] HOANG Q, NGUYEN T D, LE T, et al. MGAN: Training generative adversarial nets with multiple generators[C]// 6th International Conference on Learning Representations. La Jolla, CA: International Conference on Representation Learning, 2018. [15] PERSIO L D, HONCHAR O. Artificial neural networks architectures for stock price prediction: Comparisons and applications[J]. International Journal of Circuits, Systems and Signal Processing, 2016, 10: 403-413. [16] 龙奥明, 毕秀春, 张曙光. 基于LSTM 神经网络的黑色金属期货套利策略模型[J].中国科学技术大学学报, 2018, 48(2): 125-132. [17] 杨青, 王晨蔚. 基于深度学习LSTM神经网络的全球股票指数预测研究[J]. 统计研究, 2019, 36(3): 67-79. [18] 孙达昌, 毕秀春. 基于深度学习算法的高频交易策略及其盈利能力[J]. 中国科学技术大学学报, 2018, 48(11): 58-67. [19] LAKSHMINARAYANAN S K, MCCRAE J. A comparative study of SVM and LSTM deep learning algorithms for stock prediction[DB/OL]. [2020-02-10]. http://CEURWS.org/Vol-2563/aics 41.pdf. [20] DEV S, WESLEY C, FARHANA H Z. A comparative study of LSTM and DNN for stock market forecasting[C]// 2018 IEEE International Conference on Big Data. IEEE, 2018. [21] ARVAND F, SHERIDAN H. Deep learning for the prediction of stock market trends[C]// 2019 IEEE International Conference on Big Data. IEEE, 2019. [22] 陆广泉, 谢扬才, 刘星,等.一种基于KNN的半监督分类改进算法[J].广西师范大学学报(自然科学版), 2012, 30 (1): 48-52. [23] 刘蓉. 半监督学习的Co-training算法研究[J]. 电脑编程技巧与维护, 2010(14): 6-7.
[24] KINGMA D P, BA J. Adam: A method for stochastic optimization[DB/OL]. [2020-02-10]. https://arxiv.org/abs/1412.6980.
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