面向限价指令簿趋势分析的网络集成模型

doi:10.16451/j.cnki.issn1003-6059.202108008

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Abstract To analyze the trend of limit order books(LOBs) better, a network ensemble model for trend analysis of LOBs(NEM-LOB) is proposed. Two long short-term memory(LSTM) sub-models and one convolutional neural network sub-model are integrated in NEM-LOB. One LSTM sub-model captures the global temporal dependence through the distribution information of LOBs. The other LSTM sub-model captures the global dynamics through the dynamic information of LOBs and order streams. The local features are extracted through the factual information of LOBs. Finally, three sub-models are combined to extract features to obtain prediction results. Experiments on FI-2010 dataset show that NEM-LOB makes a better trend analysis for LOBs by combining order streams.

Key words： Order Streams Limit Order Books(LOBs) Ensemble Model Convolutional Neural Network(CNN) Long Short-Term Memory(LSTM)

Received: 28 April 2021

ZTFLH:

TP 183

Fund:Natural Science Foundation of Jiangsu Higher Education Institutions(No.19KJA550002), Six Talent Peak Project of Jiangsu Province(No.XYDXX-054), Project Funded by the Priority Academic Program Development of Jiangsu Higher Education Institutions

Corresponding Authors: ZHANG Li, Ph.D., professor. Her research interests include machine learning, pattern recognition and image processing.

About author:: LÜ Xuerui, master student. Her research interests include machine learning, deep learning and financial time series analysis.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202108008 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2021/V34/I8/751

[1] HU G S, HU Y X, YANG K, et al. Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions // Proc of the International Conference on Acoustics, Speech, and Signal Processing. Washington, USA: IEEE, 2018: 2706-2710.
[2] CONT R. Statistical Modeling of High-Frequency Financial Data. IEEE Signal Processing Magazine, 2011, 28(5): 16-25.
[3] WEI L J, SHI L. Investor Sentiment in an Artificial Limit Order Market[C/OL]. [2021-03-16]. https://downloads.hindawi.com/journals/complexity/2020/8581793.pdf.
[4] 杨宝臣,郭灿,王硕.基于限价指令簿信息的流动性冲击影响.系统工程, 2017, 35(3): 21-28.
(YANG B C, GUO C, WANG S. Order Flows′ Liquidity Shock Based on the Order-Driven Market. Systems Engineering, 2017, 35(3): 21-28.)
[5] 张传海,汪璐欹,彭哲.限价指令簿信息对价格动态的非对称效应——来自我国A股市场的经验研究.武汉金融, 2020(3): 18-27.
(ZHANG C H, WANG L Q, PENG Z. Asymmetric Effect of Limit Order Book Information on Price Dynamics: An Empirical Study from My Country′s A-Shares Market. Wuhan Finance, 2020(3): 18-27.)
[6] TRAN D T, MAGRIS M, KANNIAINEN J, et al. Tensor Representation in High-Frequency Financial Data for Price Change Prediction // Proc of the IEEE Symposium Series on Computational Intelligence. Washington, USA: IEEE, 2017. DOI: 10.1109/SSCI.2017.8280812.
[7] NTAKARIS A, MAGRISV M, KANNIAINEN J, et al. Benchmark Dataset for Mid-price Forecasting of Limit Order Book Data with Machine Learning Methods[C/OL]. [2021-03-16].https://arxiv.org/pdf/1705.03233v4.pdf.
[8] KERCHEVAL A N, ZHANG Y. Modelling High-Frequency Limit Order Book Dynamics with Support Vector Machines. Quantitative Finance, 2015, 15(8): 1315-1329.
[9] NTAKARIS A, KANNIAINEN J, GABBOUJ M, et al. Mid-price Prediction Based on Machine Learning Methods with Technical and Quantitative Indicators[C/OL]. [2021-03-16].https://arxiv.org/pdf/1907.09452v1.pdf.
[10] TSANTEKIDIS A, PASSALIS N, TEFAS A, et al. Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks // Proc of the 19th IEEE Conference on Business Informatics. Washington, USA: IEEE, 2017: 7-12.
[11] NIÑO J, ARÉVALO A, LEÓN D, et al. Price Prediction with CNN and Limit Order Book Data // Proc of the Workshop on Engineering Applications. Berlin, Germany: Springer, 2018: 124-135.
[12] PASSALIS N, TEFAS A, KANNIAINEN J, et al. Adaptive Normalization for Forecasting Limit Order Book Data Using Convolutional Neural Networks // Proc of the IEEE International Confe-rence on Acoustics, Speech, and Signal Processing. Washington, USA: IEEE, 2020: 1713-1717.
[13] MÄKINEN Y, KANNIAINEN J, GABBOUJ M, et al. Forecasting Jump Arrivals in Stock Prices: New Attention-Based Network Architecture Using Limit Order Book Data. Quantitative Finance, 2019, 19(12): 2033-2050.
[14] ZHANG Z H, ZOHREN S, ROBERTS S. DeepLOB: Deep Convolutional Neural Networks for Limit Order Books. IEEE Transactions on Signal Processing, 2019, 67(11): 3001-3012.
[15] TRAN D T, IOSIFIDIS A, KANNIAINEN J, et al. Temporal Attention-Augmented Bilinear Network for Financial Time-Series Data Analysis. IEEE Transactions on Neural Networks and Learning Systems, 2018, 30(5): 1407-1418.
[16] NAIK N, MOHAN B R. Intraday Stock Prediction Based on Deep Neural Network. National Academy Science Letters, 2020, 43(2): 241-246.
[17] CHOI S P, CHAN Y, LAM S, et al. Analyzing the Importance of Broker Identities in the Limit Order Book through Deep Learning. Big Data, 2021,9(2): 89-99.
[18] GOULD M D, PORTER M A, WILLIAMS S, et al. Limit Order Books. Quantitative Finance, 2013, 13(11): 1709-1742.
[19] LI J Y, WANG X T, LIN Y Y, et al. Generating Realistic Stock Market Order Streams. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(1): 727-734.
[20] HOCHREITER S, SCHMIDHUBER J. Long Short-Term Memory. Neural Computation, 1997, 9(8): 1735-1780.
[21] ZHAO R, WANG K, SU H, et al. Bayesian Graph Convolution LSTM for Skeleton Based Action Recognition // Proc of the IEEE/CVF International Conference on Computer Vision. Washington, USA: IEEE, 2019: 6881-6891.
[22] TRAN D T, GABBOUJ M, IOSIFIDIS A. Multilinear Class-Speci-fic Discriminant Analysis. Pattern Recognition Letters, 2017, 100: 131-136.
[23] PASSALIS N, TSANTEKIDIS A, TEFAS A, et al. Time-Series Classification Using Neural Bag-of-Features // Proc of the 25th European Signal Processing Conference. Washington, USA: IEEE, 2017: 301-305.
[24] PASSALIS N, TEFAS A, KANNIAINEN J, et al. Deep Temporal Logistic Bag-of-Features for Forecasting High Frequency Limit Order Book Time Series // Proc of the IEEE International Conference on Acoustics, Speech and Signal Processing. Washington, USA: IEEE, 2019: 7545-7549.
[25] PASSALIS N, TEFAS A, KANNIAINEN J, et al. Temporal Logistic Neural Bag-of-Features for Financial Time Series Forecasting Leveraging Limit Order Book Data. Pattern Recognition Letters, 2020, 136: 183-189.
[26] TSANTEKIDIS A, PASSALIS N, TEFAS A, et al. Using Deep Learning to Detect Piece Change Indications in Financial Markets // Proc of the 25th European Signal Processing Conference. Washington, USA: IEEE, 2017: 2511-2515.
[27] TSANTEKIDIS A, PASSALIS N, TEFAS A, et al. Using Deep Learning for Price Prediction by Exploiting Stationary Limit Order Book Features[C/OL]. [2021-03-16]. https://arxiv.org/pdf/1810.09965.pdf.