There is a general consensus of the good sensing and novelty characteristics of Twitter as an information media for the complex financial market. This paper investigates the permeability of Twittersphere, the total universe of Twitter users and their habits, towards relevant events in the financial market. Analysis shows that a general purpose social media is permeable to financial-specific events and establishes Twitter as a relevant feeder for taking decisions regarding the financial market and event fraudulent activities in that market. However, the provenance of contributions, their different levels of credibility and quality and even the purpose or intention behind them should to be considered and carefully contemplated if Twitter is used as a single source for decision taking. With the overall aim of this research, to deploy an architecture for real-time monitoring of irregularities in the financial market, this paper conducts a series of experiments on the level of permeability and the permeable features of Twitter in the event of one of these irregularities. To be precise, Twitter data is collected concerning an event comprising of a specific financial action on the 27th January 2017: the announcement about the merge of two companies Tesco PLC and Booker Group PLC, listed in the main market of the London Stock Exchange
Twitter, as the heart of publicly accessible Social Media, is one of the currently used platforms to share financial information and is a valuable source of information for different roles in the financial market. For all these roles, the quality analysis of Twitter as a source of financial information is essential to take decisions. The work in this paper is aligned with the ongoing work of the authors to a solution for irregularity monitoring in the financial market by harnessing data in online social media. To do so, the permeability of a variety of social media data feeders to financial irregularities should be analysed. That is the case of the experiment in this paper by putting the focus on Twitter microblogging platform and checking if this general purpose social media is permeable to a specific financial event. For this, we detail the analysis of Twitter permeability to a specific event in the past few months: the announcement about the merge of Tesco and Booker to create a UK's Leading Food Business on the 27 th January 2017. Both companies Tesco PLC and Booking Group PLC are listed in the main market of LSE (London Stock Exchange). Our findings provide promising evidences to address the problem of real-time detection of irregularities in the financial market via Twitter according to the volume (as a sign of the importance of the irregularity) and to other features (as signs of the potential origin causing the irregularity).
The dawn of big data has seen the volume, variety, and velocity of data sources increase dramatically. Enormous amounts of structured, semi-structured and unstructured heterogeneous data can be garnered at a rapid rate, making analysis of such big data a herculean task. This has never been truer for data relating to financial stock markets, the biggest challenge being the 7 Vs of big data which relate to the collection, pre-processing, storage and real-time processing of such huge quantities of disparate data sources. Data fusion techniques have been adopted in a wide number of fields to cope with such vast amounts of heterogeneous data from multiple sources and fuse them together in order to produce a more comprehensive view of the data and its underlying relationships. Research into the fusing of heterogeneous financial data is scant within the literature, with existing work only taking into consideration the fusing of text-based financial documents. The lack of integration between financial stock market data, social media comments, financial discussion board posts and broker agencies means that the benefits of data fusion are not being realised to their full potential. This paper proposes a novel data fusion model, inspired by the data fusion model introduced by the Joint Directors of Laboratories, for the fusing of disparate data sources relating to financial stocks. Data with a diverse set of features from different data sources will supplement each other in order to obtain a Smart Data Layer, which will assist in scenarios such as irregularity detection and prediction of stock prices.
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