Abstract
Abnormal trading behavior in e-markets, also known as market manipulation, is done with the aim of deceiving traders in order to obtain unusual profits. These behaviors harm the proper functioning and integrity of the market and are, therefore, detrimental to trading activities. Although many studies have investigated this issue, their focus is on modeling the detection method, and they have neglected the processes that lead to manipulation. So that, to questions such as: What is the main reason for the success of these manipulations? What is the central idea behind manipulative strategies? Are manipulative schemes based on a specific theory? No answer has scientifically been given yet. Accordingly, there is a knowledge gap in the literature; this study aims to provide this complementary perspective and fill this gap. In this study, as a pioneer in this field, we develop the concept of the trading behavior’s diffusion network. Then, we analyze and examine the diffusion process from the aspects of temporal dynamics of trading behaviors, diffusion network topology, diffusion patterns, and structural characteristics of the diffusion network. The results indicate significant differences in the diffusion process of manipulative behavior compared to regular trading behavior and random behavior. In addition, manipulators showed higher social influence (network centrality) in the diffusion network of manipulative behavior. Our findings bear significant implications for market supervisors and regular traders and provide a new dimension for researchers and supervisors in determining approaches and methods for detecting manipulation.
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References
Akoglu L, Tong H, Koutra D (2014) Graph-based anomaly detection and description: a survey. Data Min Knowl Discov. https://doi.org/10.1007/s10618-014-0365-y
Alexander C, Cumming D (2020) Corruption and fraud in financial markets: malpractice misconduct and manipulation. John Wiley & Sons
Almudi I, Fatas-Villafranca F (2021) Coevolution in economic systems. Elem Evolut Econ. https://doi.org/10.1017/9781108767798
Badham J, Kee F, Hunter RF (2021) Network structure influence on simulated network interventions for behaviour change. Soc Netw 64:55–62. https://doi.org/10.1016/j.socnet.2020.08.003
Barabási A-L (2005) The origin of bursts and heavy tails in human dynamics. Nature 435(7039):207–211. https://doi.org/10.1038/nature03459
Berahmand K, Bouyer A, Samadi N (2019) A new local and multidimensional ranking measure to detect spreaders in social networks. Computing 101(11):1711–1733. https://doi.org/10.1007/s00607-018-0684-8
Berahmand K, Nasiri E, Rostami M, Forouzandeh S (2021) A modified DeepWalk method for link prediction in attributed social network. Computing 103(10):2227–2249. https://doi.org/10.1007/s00607-021-00982-2
Malm A, Schoepfer A, Bichler G, Boyd N (2013) Pushing the Ponzi: The Rise and Fall of a Network Fraud. In Crime and Networks. Routledge, Milton Park.
Cao R, Liu XF, Fang Z, Xu X-K, Wang X (2023) How do scientific papers from different journal tiers gain attention on social media? Inf Process Manage 60(1):103152. https://doi.org/10.1016/j.ipm.2022.103152
Centola D (2010) The spread of behavior in an online social network experiment. Science 329(5996):1194–1197. https://doi.org/10.1126/science.1185231
Centola D (2018) How behavior spreads: the science of complex contagions. Princeton University Press
Cheng X, Zhao N (2020) Modelling the diffusion of investment decisions on modular social networks. Complexity 2020:e2981231. https://doi.org/10.1155/2020/2981231
Crovella ME, Bestavros A (1996) Self-similarity in world wide web traffic: evidence and possible causes. ACM SIGMETRICS Perform Eval Rev 24(1):160–169. https://doi.org/10.1145/233008.233038
Ding H, Xie L (2023) Simulating rumor spreading and rebuttal strategy with rebuttal forgetting: an agent-based modeling approach. Physica A Stat Mech Appl 612:128488. https://doi.org/10.1016/j.physa.2023.128488
Gündüç S, Eryiğit R (2021) Time dependent correlations between the probability of a node being infected and its centrality measures. Physica A Stat Mech Appl 563:125483. https://doi.org/10.1016/j.physa.2020.125483
Harrigan N, Achananuparp P, Lim E-P (2012) Influentials, novelty, and social contagion: the viral power of average friends, close communities, and old news. Soc Netw 34(4):470–480. https://doi.org/10.1016/j.socnet.2012.02.005
Keuchenius A, Törnberg P, Uitermark J (2021) Adoption and adaptation: a computational case study of the spread of Granovetter’s weak ties hypothesis. Soc Netw 66:10–25. https://doi.org/10.1016/j.socnet.2021.01.001
Khodabandehlou S, Golpayegani AH, S. (2022) Market manipulation detection: a systematic literature review. Expert Syst Appl 210:118330. https://doi.org/10.1016/j.eswa.2022.118330
Khodabandehlou S, Golpayegani AH (2024) FiFrauD: unsupervised financial fraud detection in dynamic graph streams. ACM Trans Knowl Discov Data. https://doi.org/10.1145/3641857
Khodabandehlou S, Zivari Rahman M (2017) Comparison of supervised machine learning techniques for customer churn prediction based on analysis of customer behavior. J Syst Inf Technol 19(1/2):65–93. https://doi.org/10.1108/JSIT-10-2016-0061
Leskovec J, McGlohon M, Faloutsos C, Glance N, Hurst M (2007) Cascading behavior in large blog graphs. In: Proceedings of the 2007 SIAM international conference on data mining. Society for Industrial and Applied Mathematics
Li W, Li T, Berahmand K (2022) An effective link prediction method in multiplex social networks using local random walk towards dependable pathways. J Comb Optim 45(1):31. https://doi.org/10.1007/s10878-022-00961-z
Matsubara Y, Sakurai Y, Prakash BA, Li L, Faloutsos C (2012) Rise and fall patterns of information diffusion: model and implications. In: Proceedings of the 18th ACM SIGKDD international conference on knowledge discovery and data mining. pp. 6–14 https://doi.org/10.1145/2339530.2339537
Nash R, Bouchard M, Malm A (2013) Investing in people: The role of social networks in the diffusion of a large-scale fraud. Social Networks 35(4):686–698. https://doi.org/10.1016/j.socnet.2013.06.005
Putniņš TJ (2018) An overview of market manipulation. (SSRN Scholarly Paper ID 3398258). Social Science Research Network. https://papers.ssrn.com/abstract=3398258
Shi C, Zhang Q, Chu T (2022) Source estimation in continuous-time diffusion networks via incomplete observation. Physica A 592:126843. https://doi.org/10.1016/j.physa.2021.126843
Shi F-B, Sun X-Q, Shen H-W, Cheng X-Q (2019) Detect colluded stock manipulation via clique in trading network. Physica A 513:565–571. https://doi.org/10.1016/j.physa.2018.09.011
Siering M, Clapham B, Engel O, Gomber P (2017) A taxonomy of financial market manipulations: establishing trust and market integrity in the financialized economy through automated fraud detection. J Inf Technol 32(3):251–269. https://doi.org/10.1057/s41265-016-0029-z
Vega-Redondo F (2007) Complex social networks. Cambridge University Press
Wang K, Yaqub W, Lakhdari A, Suleiman B (2021) Combating fake news by empowering fact-checked news spread via topology-based interventions. (arXiv:2107.05016) arXiv https://doi.org/10.48550/arXiv.2107.05016
Wang W, Liu Q-H, Liang J, Hu Y, Zhou T (2019) Coevolution spreading in complex networks. Phys Rep 820:1–51. https://doi.org/10.1016/j.physrep.2019.07.001
Wang X, Xing Y, Wei Y, Zheng Q, Xing G (2020) Public opinion information dissemination in mobile social networks—taking Sina Weibo as an example. Inf Discov Deliv 48(4):213–224. https://doi.org/10.1108/IDD-10-2019-0075
Zhai J, Cao Y, Yao Y, Ding X, Li Y (2017) Computational intelligent hybrid model for detecting disruptive trading activity. Decis Support Syst 93:26–41. https://doi.org/10.1016/j.dss.2016.09.003
Zhu A, Fu P, Zhang Q, Chen Z (2017) Ponzi scheme diffusion in complex networks. Physica A 479:128–136. https://doi.org/10.1016/j.physa.2017.03.015
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Samira Khodabandehlou: Conceptualization, Methodology, Formal analysis, Investigation, Visualization, Writing- original draft, Writing- Reviewing and Editing. Alireza Hashemi Golpayegani: Conceptualization, Supervision.
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Khodabandehlou, S., Hashemi Golpayegani, S.A. How do abnormal trading behaviors diffuse in electronic markets?. Soc. Netw. Anal. Min. 14, 98 (2024). https://doi.org/10.1007/s13278-024-01262-5
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DOI: https://doi.org/10.1007/s13278-024-01262-5