Do Internet Stock Message Boards Influence Trading? Evidence from Heavily Discussed Stocks with No Fundamental News

Sabherwal, Sanjiv; Sarkar, Salil K.; Zhang, Ying

doi:10.1111/j.1468-5957.2011.02258.x

Cited by 116 publications

(84 citation statements)

References 52 publications

(133 reference statements)

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“…The methodologies implemented in FDBM can significantly reduce the time and efforts needed by the relevant authorities to investigate P&D crime on FDBs in real time. As suggested by [29], regulators need to monitor share price based FDBs closely as share price based FDBs are becoming increasingly popular and the authors also find strong positive relationship between the stock prices of smaller companies and the investors' sentiments on FDBs.…”

Section: Discussionmentioning

confidence: 94%

“…It can be easily modified whenever needed. The IE keyword template consists of a series of keywords and phrases that were thoroughly researched [2], [27]- [29] and has been validated by experts in the relevant field. The IE keyword template will be used by the forward and backward analysers for the comments flagging process.…”

Section: ) Ie Templatesmentioning

confidence: 99%

“…2 depicts the FDB-DS structure. [6], [27]- [29]. The following is a sample list of the keywords and phrases that were used in this work: …”

Section: B Dataset Acquisitionmentioning

confidence: 99%

See 2 more Smart Citations

Novel Methods for Resolving False Positives during the Detection of Fraudulent Activities on Stock Market Financial Discussion Boards

Lee¹,

Owda²,

Crockett³

2018

ijacsa

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Abstract-Financial discussion boards (FDBs) have been widely used for a variety of financial knowledge exchange activities through the posting of comments. Popular public FDBs are prone to being used as a medium to spread false financial information due to larger audience groups. Although online forums are usually integrated with anti-spam tools, such as Akismet, moderation of posted content heavily relies on manual tasks. Unfortunately, the daily comments volume received on popular FDBs realistically prevents human moderators to watch closely and moderate possibly fraudulent content, not to mention moderators are not usually assigned with such task. Due to the absence of useful tools, it is extremely time consuming and expensive to manually read and determine whether each comment is potentially fraudulent. This paper presents novel forward and backward analysis methodologies implemented in an Information Extraction (IE) prototype system named FDBs Miner (FDBM). The methodologies aim to detect potentially illegal Pump and Dump comments on FDBs with the integration of per-minute share prices in the detection process. This can possibly reduce false positives during the detection as it categorises the potentially illegal comments into different risk levels for investigation purposes. The proposed system extracts company's ticker symbols (i.e. unique symbol that represents and identifies each listed company on stock market), comments and share prices from FDBs based in the UK. The forward analysis methodology flags the potentially Pump and Dump comments using a predefined keywords template and labels the flagged comments with price hike thresholds. Subsequently, the backward analysis methodology employs a moving average technique to determine price abnormalities and backward analyse the flagged comments. The first detection stage in forward analysis found 9.82% of potentially illegal comments. It is unrealistic and unaffordable for human moderators or financial surveillance authorities to read these comments on a daily basis. Hence, by integrating share prices to perform backward analysis can categorise the flagged comments into different risk levels. It helps relevant authorities to prioritise and investigate into the higher risk flagged comments, which could potentially indicate a real Pump and Dump crime happening on FDBs when the system is being used in real time.

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Section: Discussionmentioning

confidence: 94%

Section: ) Ie Templatesmentioning

confidence: 99%

See 1 more Smart Citation

Novel Methods for Resolving False Positives during the Detection of Fraudulent Activities on Stock Market Financial Discussion Boards

Lee¹,

Owda²,

Crockett³

2018

ijacsa

View full text Add to dashboard Cite

show abstract

“…At the beginning of the emergence of social media, stock discussion forums pioneered an alternative source of information for investors, specially retailers, supplementing the traditional news media. A plethora of academic works study the capability of these highly-specialized social channels to predict returns and to detect abnormal behaviours in the stock market [3,9,15,47]. From the investor's perspective, success highly depends on the quality and the rapidness of the information to support the decision-making.…”

Section: Introductionmentioning

confidence: 99%

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

Vilas

Redondo

Crockett

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

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“…Zhang et al (2011) construct a "collective hope and fear" index with the postings in Twitter and find a negative correlation between this index and S&P 500, NASDAQ and DJIA. Sabherwal et al (2011) also construct the "online trader's credit-weighted sentiment index" from the Lion.com and find this index can predict the subsequent two trading days' returns. Zhang et al (2012) download message board postings from Thelio!WallStreetPit and show the sentiment generated from text classifiers is an negative indicator of next-day's stock return.…”

Section: Machine Learning Text Mining and Linguistic Classificationmentioning

confidence: 99%

The role of internet in stock market

Shen¹

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Do Internet Stock Message Boards Influence Trading? Evidence from Heavily Discussed Stocks with No Fundamental News

Cited by 116 publications

References 52 publications

Novel Methods for Resolving False Positives during the Detection of Fraudulent Activities on Stock Market Financial Discussion Boards

Novel Methods for Resolving False Positives during the Detection of Fraudulent Activities on Stock Market Financial Discussion Boards

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

The role of internet in stock market

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