Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (7): 31-39.doi: 10.11896/jsjkx.210400304

• Database & Big Data & Data Science • Previous Articles     Next Articles

Related Transaction Behavior Detection in Futures Market Based on Bi-LSTM

ZHANG Yuan1, KANG Le2,3, GONG Zhao-hui3, ZHANG Zhi-hong1   

  1. 1 School of Information Engineering,Zhengzhou University,Zhengzhou 450001,China
    2 Department of Computer Science and Technology,Tsinghua University,Beijing 100084,China
    3 Finance Innovation Laboratory of Zhengzhou Esunny Information,Zhengzhou 450018,China
  • Received:2021-04-29 Revised:2021-08-16 Online:2022-07-15 Published:2022-07-12
  • About author:ZHANG Yuan,born in 1996,postgra-duate,is a student member of China Computer Federation.His main research interests include data mining and financial big data.
    ZHANG Zhi-hong,born in 1965,Ph.D,professor,is a senior member of China Computer Federation.His main research interests include financial big data,blockchain,and distributed computing.
  • Supported by:
    Major Public Welfare Project of Henan Province(201300210300).

PDF (PC)

640

Abstract: With the continuous development of the futures market,its transaction volume keeps increasing.Behind the massive transactions,some traders use related transaction behaviors to manipulate the futures market and disrupt the order of transactions,which has brought severe challenges to market supervision and risk control.How to mining potential related transaction behaviors from massive transaction data is an important task for maintaining fair transactions in the futures market.In response to this problem,this paper proposes a bidirectional long short-term memory(Bi-LSTM) network model with multi-feature information fusion,which extracts multiple dimensions of shallow feature information such as trading time,trading volume,position changes,and futures varieties from the original transaction data.The Bi-LSTM network model learns deep features from the contextualrelationship in the forward and backward directions of the time series and realizes the detection of related transaction behavior.For shallow features extraction,a multi-granularity window feature extraction method based on transaction behavior is proposed,to captures the correlation of transactions between accounts from the levels of day,hour,minute,second,etc.It solves the problems of high data dimension,large amount of data,and weak correlation of original transaction data.The model introduces the Dropout strategy to alleviate problems of slow convergence and over-fitting.Experimental results on the real data of Zhengzhou Commodity Exchange show that the proposed method evidently improves the classification precision and recall compared with some traditional classification models and RNN and LSTM network.At the same time,the ablation experiment of each dimension information proves the effectiveness of the multi-feature fusion method and the multi-granularity window strategy.In addition,the transaction data of two futures varieties are extracted for testing,and the results show that the proposed model has good generalization ability.

Key words: Bi-LSTM, Futures market, Multi-feature information fusion, Multi-granularity feature extraction, Related transaction behavior

CLC Number: 

  • TP301
[1]HSU C H,LEE H C.Insider trading and information revelation with the introduction of futures markets[J].Economic Mode-lling,2014,43:173-182.
[2]MENG K.The China Securities Regulatory Commission revise the management method of Futures Exchange [EB/OL].(2019-11-16) [2019-11-16].http://epaper.zqrb.cn/html/2019-11/16/content_530969.htm.
[3]CHEN C,ZHAO L,BIAN J,et al.Investment behaviors can tell what inside:Exploring stock intrinsic properties for stock trend prediction[C]//Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.New York:ACM,2019:2376-2384.
[4]SZEGEDY C,VANHOUCKE V,IOFFE S,et al.Rethinking the inception architecture for computer vision[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ:IEEE,2016:2818-2826.
[5]ZHU Z K,LI J B.Multi-Feature Information Fusion LSTM-RNN Detection for OSA[J].Journal of Computer Research and Development,2020,57(12):2547-2555.
[6]SRIVASTAVA N,HINTON G,KRIZHE A,et al.Dropout:A Simple Way to Prevent Neural Networks from Overfitting[J].Journal of Machine Learning Research,2014,15(1):1929-1958.
[7]FRANKE M,HOSER B.On the analysis of irregular stock market trading behavior[M]//Data Analysis,Machine Learning and Applications.Berlin:Springer,2008:355-362.
[8]PALSHIKAR G K,APTE M.Collusion set detection usinggraph clustering[J].Data Mining & Knowledge Discovery,2008,16(2):135-164.
[9]CAO H B,YU M O,PHILIP S,et al.Detecting abnormal coupled sequences and sequence changes in group-based manipulative trading behaviors[C]//Association for Computing Machi-nery.2010:85-94.
[10]ZHANG X J,ZHANG T L,ZHANG Z H.a clustering detection method of related transaction behavior for futures market[J].Computer Applications and Software,2016(12):275-278.
[11]ZHAI J,CAO Y,YAO Y,et al.Coarse and fine identification of collusive clique in financial market[J].Expert Systems with Applications,2017,69:225-238.
[12]HUANG B,LIU Z,CHEN J,et al.Behavior pattern clustering in blockchain networks[J].Multimedia Tools & Application,2017,76(19):20099-20110.
[13]WANG J,ZHOU S,GUAN J.Detecting potential collusivecliques in futures markets based on trading behaviors from real data[J].Neurocomputing,2012,92:44-53.
[14]YU X P.Related account group identification technology in futures market[D].Shanghai:Shanghai JiaoTong University,2013.
[15]MA X F,FENG Z.identification method of suspected related account based on transaction behavior[J].The Foreland of Trading Technology,2017(12):52-56.
[16]MIAO Z C,SHI G W,LU J D,et al.An Automatic Identification System for Related Behaviors of Financial Derivatives Investors:CN 109636608 A[P].2019-04-16.
[17]HU L.Associated account identification method and electronicequipment:CN 110879816 A[P].2020-03-13.
[18]SUNNY M,MASWOOD M,ALHARBI A G.Deep Learning-Based Stock Price Prediction Using LSTM and Bi-Directional LSTM Model[C]//2020 2nd Novel Intelligent and Leading Emerging Sciences Conference(NILES).2020:87-92.
[19]XIE Y,LIANG R Y,LIANG Z Y,et al.Speech Emotion Classification Using Attention-Based LSTM[J].IEEE/ACM Transactions on Audio,Speech and Language Processing(TASLP),2019,27(11):1675-1685.
[1] CHENG Si-wei, GE Wei-yi, WANG Yu, XU Jian. BGCN:Trigger Detection Based on BERT and Graph Convolution Network [J]. Computer Science, 2021, 48(7): 292-298.
[2] SUN Guo-zi, LYU Jian-wei, LI Hua-kang. MeTCa:Multi-entity Trusted Confirmation Algorithm Based on Edit Distance [J]. Computer Science, 2020, 47(12): 327-331.
[3] DU Lin, CAO Dong, LIN Shu-yuan, QU Yi-qian, YE Hui. Extraction and Automatic Classification of TCM Medical Records Based on Attention Mechanism of BERT and Bi-LSTM [J]. Computer Science, 2020, 47(11A): 416-420.
[4] FENG Luan-luan, LI Jun-hui, LI Pei-feng, ZHU Qiao-ming. Technology and Terminology Detection Oriented National Defense Science [J]. Computer Science, 2019, 46(12): 231-236.
[5] CHEN Jun-hua, HAO Yan-hui, ZHENG Ding-wen, CHEN Si-yu. Modeling and Decision-making of Futures Market Price Prediction with DBN [J]. Computer Science, 2018, 45(6A): 75-78.
[6] LIU Fei-long, HAO Wen-ning, CHEN Gang, JIN Da-wei and SONG Jia-xing. Attention of Bilinear Function Based Bi-LSTM Model for Machine Reading Comprehension [J]. Computer Science, 2017, 44(Z6): 92-96.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
渝ICP备16013121号-4
Add:18# Honghu West Rd., Yubei ,Chongqing,China    Post Code: 401121
Tel: 023-63500828/023-67039612/023-67039623
E-mail: jsjkx12@163.com
Powered by Beijing Magtech Co., Ltd.