The impact of stock spams on volatility

This paper is dedicated to study the impact of stock spams through the analysis of the variations of volatility. We use the methodology of event studies on a sample of hundred ten firms. The results show positive and significant changes in volatility during 12 days of the event window; a widening of the variation [lowest price - highest price] was noticed following the consignment of messages by the spammers. The sending of stock spams affected the behaviour of investors, indicating thus that the spamming activity is a lucrative business.Stock spam, event studies, volatility, penny stock

Stock market prediction using machine learning classifiers and social media, news

Accurate stock market prediction is of great interest to investors; however, stock markets are driven by volatile factors such as microblogs and news that make it hard to predict stock market index based on merely the historical data. The enormous stock market volatility emphasizes the need to effectively assess the role of external factors in stock prediction. Stock markets can be predicted using machine learning algorithms on information contained in social media and financial news, as this data can change investors’ behavior. In this paper, we use algorithms on social media and financial news data to discover the impact of this data on stock market prediction accuracy for ten subsequent days. For improving performance and quality of predictions, feature selection and spam tweets reduction are performed on the data sets. Moreover, we perform experiments to find such stock markets that are difficult to predict and those that are more influenced by social media and financial news. We compare results of different algorithms to find a consistent classifier. Finally, for achieving maximum prediction accuracy, deep learning is used and some classifiers are ensembled. Our experimental results show that highest prediction accuracies of 80.53% and 75.16% are achieved using social media and financial news, respectively. We also show that New York and Red Hat stock markets are hard to predict, New York and IBM stocks are more influenced by social media, while London and Microsoft stocks by financial news. Random forest classifier is found to be consistent and highest accuracy of 83.22% is achieved by its ensemble

Using Text Mining to Analyze Quality Aspects of Unstructured Data: A Case Study for “stock-touting” Spam Emails

The growth in the utilization of text mining tools and techniques in the last decade has been primarily driven by the increase in the sheer volume of unstructured texts and the need to extract useful and more importantly, quality information from them. The impetus to analyse unstructured data efficiently and effectively as part of the decision making processes within an organization has further motivated the need to better understand how to use text mining tools and techniques. This paper describes a case study of a stock spam e-mail architecture that demonstrates the process of refining linguistic resources to extract relevant, high quality information including stock profile, financial key words, stock and company news (positive/negative), and compound phrases from stock spam e-mails. The context of such a study is to identify high quality information patterns that can be used to support relevant authorities in detecting and analyzing fraudulent activities

Stock Spams: Another Kind Of Stock Prices Manipulation

This research investigates the market reaction to an information-based manipulation called stock spams. The impact is focused on the liquidity variable which is measured by Amivest ratio. Using the event study methodology on a sample of penny stocks for the period February 2006 through October 2008, our findings suggest positive and significant abnormal liquidities for stocks targeted by manipulators during the event window. Robustness checks were performed using a non-parametric test. These results support the thesis that this kind of manipulation is a very flourishing business that manipulators exploit by simply purchasing stocks at low prices and selling them at higher prices

Mitigating the Tragedy of the Digital Commons: the Case of Unsolicited Commercial Email

The growth of unsolicited commercial email imposes increasing costs on organizations and causes considerable aggravation on the part of email recipients. A thriving anti-spam industry addresses some of the frustration. Regulation and various economic and technical means are in the works – all aimed at bringing down the flood of unwanted commercial email. This paper contributes to our understanding of the UCE phenomenon by drawing on scholarly work in areas of marketing and resource ownership and use. Adapting the tragedy of the commons to the email context, we identify a causal structure that drives the direct e-marketing industry. Computer simulations indicate that although filtering may be an effective method to curb UCE arriving at individual inboxes, it is likely to increase the aggregate volume, thereby boosting overall costs. We also examine other response mechanisms, including self-regulation, government regulation, and market mechanisms. The analysis advances understanding of the digital commons, the economics of UCE, and has practical implications for the direct e-marketing industrySPAM; Unsolicited Commercial Email (UCE); Tragedy of the Digital Commons; Simulation

Supporting Financial Market Surveillance: An IT Artifact Evaluation

In this paper, an IT artifact instantiation (i.e. software prototype) to support decision making in the field of financial market surveillance, is presented and evaluated. This artifact utilizes a qualitative multi-attribute model to identify situations in which prices of single stocks are affected by fraudsters who aggressively advertise the stock. A quantitative evaluation of the instantiated IT artifact, based on voluminous and heterogeneous data including data from social media, is provided. The empirical results indicate that the developed IT artifact instantiation can provide support for identifying such malicious situations. Given this evidence, it can be shown that the developed solution is able to utilize massive and heterogeneous data, including user-generated content from financial blogs and news platforms, to provide practical decision support in the field of market surveillance

Pump and Dumps in the Bitcoin Era: Real Time Detection of Cryptocurrency Market Manipulations

In the last years, cryptocurrencies are increasingly popular. Even people who are not experts have started to invest in these securities and nowadays cryptocurrency exchanges process transactions for over 100 billion US dollars per month. However, many cryptocurrencies have low liquidity and therefore they are highly prone to market manipulation schemes. In this paper, we perform an in-depth analysis of pump and dump schemes organized by communities over the Internet. We observe how these communities are organized and how they carry out the fraud. Then, we report on two case studies related to pump and dump groups. Lastly, we introduce an approach to detect the fraud in real time that outperforms the current state of the art, so to help investors stay out of the market when a pump and dump scheme is in action.Comment: Accepted for publication at The 29th International Conference on Computer Communications and Networks (ICCCN 2020