Financial Discussion Boards Irregularities Detection System (FDBs-IDS) using information extraction

Advances in Intelligent Systems and Computing

et al. 2018

Self Cite

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.

Section: A Financial Discussion Boards (Fdbs)mentioning

confidence: 99%

Big Data Fusion Model for Heterogeneous Financial Market Data (FinDf)

Evans

Owda

Advances in Intelligent Systems and Computing

et al. 2018

Self Cite

“…The aim of this paper is to analyse that permeability for the case of the microblogging platform Twitter. The Intelligent System Group at the Manchester Metropolitan University has worked in the detection of irregularities form Financial Discussion Forums [39]. Meanwhile, the Information & Computing Lab of the University of Vigo has a relevant know-how in applying Twitter analytics to a variety of real life problems [45,48].…”

Section: Contributionmentioning

confidence: 99%

Twitter permeability to financial events: an experiment towards a model for sensing irregularities

Vilas

Redondo

et al. 2018

Multimed Tools Appl

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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

“…IE has been used in other areas such as accounting [13] and search engine [14]. However, other than the initial work described in [11] and [15], there is very little usage of IE techniques in FDBs' financial crimes related research.…”

Section: Introductionmentioning

confidence: 99%

“…During the detection of potentially illegal comments in [15], share prices were not considered. Hence, the novel methodology introduced in this paper will be used to flag all the potentially illegal comments while integrating the share prices into the detection process.…”

Section: Introductionmentioning

confidence: 99%

The Detection of Fraud Activities on the Stock Market Through Forward Analysis Methodology of Financial Discussion Boards

Lee

Owda

Advances in Intelligent Systems and Computing

2018

Self Cite

Financial discussion boards (FDBs) have been widely used for a variety of financial knowledge exchange activities through the posting of comments on the FDBs. Popular public FDBs are prone to be used as a medium to spread false financial information due to having a larger group of audiences. Although online forums, in general, are usually integrated with anti-spam tools such as Akismet, moderation of posted contents heavily relies on human moderators. Unfortunately, popular FDBs attract many comments per day which realistically prevents human moderators from continuously monitoring and moderating possibly fraudulent contents. Such manual moderation can be extremely time-consuming. Moreover, due to the absence of useful tools, no relevant authorities are actively monitoring and handling potential financial crimes on FDBs. This paper presents a novel forward analysis methodology implemented in an Information Extraction (IE) prototype system named FDBs Miner (FDBM). This methodology aims to detect potentially illegal comments on FDBs while integrating share prices in the detection process as this helps to categorise the potentially illegal comments into different risk levels for investigation priority. The IE prototype system will first extract the public comments and per minute share prices from FDBs for the selected listed companies on London Stock Exchange (LSE). In the forward analysis process, the comments are flagged using a predefined Pump and Dump financial crime related keyword template. By only flagging the comments against the keyword template yields an average of 9.82% potentially illegal comments. It is unrealistic and unaffordable for human moderators to read these comments on a daily basis in long run. Hence, by integrating the share prices' hikes and falls to categorise the flagged comments based on risk levels, it saves time and allows relevant authorities to prioritise and investigate into the higher risk flagged comments as it can potentially indicate real Pump and Dump crimes on FDBs.