近年来，围绕金融网络进行的构建与分析研究受到学界的广泛关注。现有研究视角主要集中在利用金融市场内的交易数据，构建网络并开展分析；然而，在大数据时代，如何利用包括新闻文本在内的非金融市场另类数据开展研究，仍有较大探索空间。采用文本挖掘中的共现分析思路，提出一套针对中文语境基于事件关联挖掘的金融网络构建方法，并在此网络基础上分别进行拓扑结构分析、金融事件路径传递分析和事件-实体关联映射分析。实验结果表明，基于金融网络构建方法，不仅能帮助理解金融市场演变路径，而且可以在一些情境下更好地识别金融市场参与实体间的关联关系。
In recent years, researches on the construction and analysis of financial network have been widely concerned. The existing researches mainly focus on the use of transaction data in the financial market. However, in the era of big data, how to use non-financial market data, such as news texts, to carry out researches still be a large space for exploration. Based on the idea of co-occurrence analysis, this paper proposes a method of constructing financial network based on event association mining in Chinese context. Based on the constructed network, we carry out the analysis of topological structure, financial event path transitivity, and event entity association. The experimental results show that our proposed approach based on financial networks can not only help to understand the evolution of financial markets, but also better realize the relationship between the entities involved in financial markets in some situations.
[1] Golosovsky M, Solomon S. Growing complex network of citations of scientific papers:Modeling and measurements[J]. Physical Review E, 2017, 95(1):012324.
[2] Rocha L E. Dynamics of air transport networks:a review from a complex systems perspective[J]. Chinese Journal of Aeronautics, 2017, 30(2):469-478.
[3] Saleh M, Esa Y, Mohamed A. Applications of complex network analysis in electric power systems[J]. Energies, 2018, 11(6):1381.
[4] 欧阳红兵, 刘晓东. 中国金融机构的系统重要性及系统性风险传染机制分析:基于复杂网络的视角[J]. 中国管理科学, 2015, 23(10):30-37.
[5] Zhu Y, Yang F, Ye W. Financial contagion behavior analysis based on complex network approach[J]. Annals of Operations Research, 2018, 268(1-2):93-111.
[6] 刘超, 郝丹辉, 唐孝文, 等. 基于复杂网络的金融风险跨市场传导机制研究:以金融危机时期(2007-2009年)数据为例[J]. 运筹与管理, 2018, 27(8):155-161.
[7] 边巧妹,翟永会,佘小博.系统重要性银行与系统重要性行业分析:基于复杂网络的研究[J]. 金融,2019,9(3):197-204.
[8] 隋聪, 谭照林, 王宗尧. 基于网络视角的银行业系统性风险度量方法[J]. 中国管理科学, 2016, 24(5):54-64.
[9] Fu P, Zhu A, Ni H, et al. Threshold behaviors of social dynamics and financial outcomes of Ponzi scheme diffusion in complex networks[J]. Physica A:Statistical Mechanics and its Applications, 2018, 490:632-642.
[10] Zhu A, Fu P, Zhang Q, et al. Ponzi scheme diffusion in complex networks[J]. Physica A:Statistical Mechanics and its Applications, 2017, 479:128-136.
[11] 崔瑜, 谢艳, 安立波, 等. 金融市场风险交叉传染机制:基于医学SIRS传染病模型[J]. 南方金融, 2017(2):3-13.
[12] Derbali A, Hallara S. Measuring systemic risk of Greek banks:New approach by using the epidemic model "SEIR"[J]. Cogent Business Management, 2016, 3(1):1153864.
[13] Alamsyah A, Ramadhani D P. The dynamic of banking network topology case study:Indonesian presidential election event[C]//2018 International Workshop on Big Data and Information Security(IWBIS). IEEE, 2018:37-42.
[14] Lismont J, Cardinaels E, Bruynseels L, et al. Predicting tax avoidance by means of social network analytics[J]. Decision Support Systems, 2018, 108:13-24.
[15] Zhou Y, Wang X, Zhang J, et al. Analyzing and detecting money-laundering accounts in online social networks[J]. IEEE Network, 2018, 32(3):115-121.
[16] Al-Nabki M W, Fidalgo E, Alegre E, et al. ToRank:Identifying the most influential suspicious domains in the Tor network[J]. Expert Systems with Applications, 2019, 123:212-226.
[17] Colladon A F, Remondi E. Using social network analysis to prevent money laundering[J]. Expert Systems with Applications, 2017, 67:49-58.
[18] Zhao S, Wang Q, Massung S, et al. Constructing and embedding abstract event causality networks from text snippets[C]//Proceedings of the 10th ACM International Conference on Web Search and Data Mining. ACM, 2017:335-344.
[19] Souza T T, Aste T. Predicting future stock market structure by combining social and financial network information[J]. Physica A:Statistical Mechanics its Applications, 2019, 535:122343.
[20] 王子傲. 金融新闻中的关联实体挖掘研究[D]. 哈尔滨:哈尔滨工业大学, 2018.
[21] 周威. 融合因果事件的知识图谱构建及其应用研究[D]. 上海:华东师范大学, 2019.
[22] Mihalcea R, Tarau P. Textrank:Bringing order into texts[C]//Proceedings of the 2004 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2004:404-411.
[23] Murtagh F, Contreras P. Algorithms for hierarchical clustering:an overview[J]. Wiley Interdisciplinary Reviews:Data Mining Knowledge Discovery, 2012, 2(1):86-97.
[24] Corsi F, Lillo F, Pirino D, et al. Measuring the propagation of financial distress with Granger-causality tail risk networks[J]. Journal of Financial Stability, 2018, 38:18-36.
[25] Výrost T, Lyócsa Š, Baumöhl E. Granger causality stock market networks:Temporal proximity and preferential attachment[J]. Physica A:Statistical Mechanics its Applications, 2015, 427:262-276.
[26] Zhong T, Peng Q, Wang X, et al. Novel indexes based on network structure to indicate financial market[J]. Physica A:Statistical Mechanics its Applications, 2016, 443:583-594.
[27] Xu R, Wong W K, Chen G, et al. Topological characteristics of the hong kong stock market:A test-based p-threshold approach to understanding network complexity[J]. Scientific Reports, 2017, 7:41379.
[28] Zhao L, Wang G J, Wang M, et al. Stock market as temporal network[J]. Physica A:Statistical Mechanics its Applications, 2018, 506:1104-1112.
