ABSTRACT Market regulators and stock exchanges around the globe need to ensure that investors trade in a fair and efficient manner. The main motive for market surveillance is to make the market more efficient and free from rouge elements. Identification of financial rumors is vital for orderly functioning of the stock market. Social media platforms allow spread of unverified information to a large mass quickly due to their interconnected nature and large number of participating members. Due to the deluge of data over various media channels including social media, manual scanning of financial rumors is inefficient. This necessitates the use of a big data infrastructure for collection, storage, and analysis of financial news related data. In this paper, we introduce a framework for automated detection of financial rumors using big data. Our framework is based on extant research on knowledge-based discovery in databases and detection of fraudulent financial activities. We describe an in-depth descriptive case study of the world’s fastest stock exchange, the Bombay Stock Exchange. Through the case, we highlight the importance of analytics for detection of financial rumors and the importance of the big data infrastructure to carry out such a task. We identify several critical factors that lead to successful identification of financial rumors. We believe the framework can be used by market regulators, stock exchanges, and security research agencies to identify information-based market manipulation using a systematic data-driven approach over a big data infrastructure.

中文翻译：

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使用大数据分析检测金融谣言：以孟买证券交易所为例

摘要 全球市场监管机构和证券交易所需要确保投资者以公平有效的方式进行交易。市场监督的主要动机是使市场更有效率，并消除胭脂成分。识别金融谣言对于股市的有序运行至关重要。由于社交媒体平台的相互关联性和大量参与成员，社交媒体平台允许将未经验证的信息迅速传播到大量人群。由于包括社交媒体在内的各种媒体渠道的数据泛滥，手动扫描金融谣言效率低下。这需要使用大数据基础设施来收集、存储和分析财经新闻相关数据。在本文中，我们介绍了一个使用大数据自动检测金融谣言的框架。我们的框架基于对数据库中基于知识的发现和欺诈性金融活动检测的现有研究。我们对世界上最快的证券交易所孟买证券交易所进行了深入的描述性案例研究。通过这个案例，我们强调了分析检测金融谣言的重要性，以及大数据基础设施对执行此类任务的重要性。我们确定了导致成功识别财务谣言的几个关键因素。我们相信，市场监管机构、证券交易所和证券研究机构可以使用该框架，通过大数据基础设施上的系统数据驱动方法来识别基于信息的市场操纵。