基于网络的内幕交易异常检测
Network-based Anomaly Detection for Insider Trading
摘要：
内幕交易是导致经济不稳定的众多白领犯罪之一。传统上，对非法内幕交易的侦查一直是一个人为驱动的过程。在本文中，我们收集了美国证券交易委员会（SEC）通过EDGAR系统提供的内幕交易，目的是启动一个自动化的大规模和数据驱动的方法来识别非法内幕交易的问题。这项研究的目标是确定有趣的模式，这可能是潜在异常的指标。我们使用收集的数据来构建网络，捕捉内部人员交易行为之间的关系。我们探讨了不同的方式建立网络从内幕交易数据，并认为需要一个结构，能够捕捉交易者之间的高阶关系。我们的研究结果表明发现了有趣的模式。
英文摘要：
Insider trading is one of the numerous white collar crimes that can contribute to the instability of the economy. Traditionally, the detection of illegal insider trades has been a human-driven process. In this paper, we collect the insider tradings made available by the US Securities and Exchange Commissions (SEC) through the EDGAR system, with the aim of initiating an automated large-scale and data-driven approach to the problem of identifying illegal insider tradings. The goal of the study is the identification of interesting patterns, which can be indicators of potential anomalies. We use the collected data to construct networks that capture the relationship between trading behaviors of insiders. We explore different ways of building networks from insider trading data, and argue for a need of a structure that is capable of capturing higher order relationships among traders. Our results suggest the discovery of interesting patterns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
