Constructing Media-based Enterprise Networks for Stock Market Risk Analysis

Stock comovement analysis is essential to understand the mechanism of stock markets. Previous studies focus on the comovement from the perspectives of fundamentals or preferences of investors. In this article, we propose a framework to explore the comovements of stocks in terms of their relationships in Web media. This is achieved by constructing media-based enterprise networks in terms of the co-exposure in news reports of stocks and mutual attentions among them. Our experiments based on CSI 300 listed firms show the significant comovements of stocks brought out by their behaviors in Web media. Furthermore, utilizing media based enterprise networks can help us identify the most influential firms which can stir up the stock markets

You are What You Say: The Influence of Company Tweets on Its Stock Performance

This paper investigates the relationship between Twitter metrics and stock price performance of a company. The objective of this research is to contribute to the area of research that seeks to uncover the business value of social media platforms. Building on prior research, this paper identifies two categories of metrics that have been used to examine the relationship between Twitter metrics and stock performance of a company, namely traffic and motivation. While traffic is measured as volume of tweets, motivation is measured from two perspectives; polarity (positive, neutral, and negative) and emotion (positive emotion and negative emotion). Unstructured data from Twitter and Yahoo finance Website about Amazon was gathered to test the study hypothesis. A combination of machine learning techniques for text analytics and hierarchical regression analysis was used to analyze the data. Results indicate that emotional motivation expressed in tweets sent out by a company positively influences the company’s stock performance

Stock Exchange Frequent Topics @NYSE

The utilization of Twitter content to predict the market is necessary since the fundamental perception related to the activities of the market influenced by the opinion in the market space. Therefore this research is utilizing the tweet of @NYSE and compare the research finding @IDX_BEI the stock exchange office in Indonesia. There is 2069 tweet extracted from the period January until June 2018. Further analysis using the Exploratory Factor Analysis conducted, and show that @NYSE delivering more varieties of information related to the stock market. It is not only information related to the IPO, but also an opinion from the recognized economist and analyst. The findings show that Twitter could be improved for further utilization and reduce asymmetric information related to the market Keywords: @NYSE, Frequent Topics, Unstructured Dataset DOI: 10.7176/RJFA/10-16-10 Publication date: August 31st 201

An ANN-based approach of interpreting user-generated comments from social media

The IT advancement facilitates growth of social media networks, which allow consumers to exchange information online. As a result, a vast amount of user-generated data is freely available via Internet. These data, in the raw format, are qualitative, unstructured and highly subjective thus they do not generate any direct value for the business. Given this potentially useful database it is beneficial to unlock knowledge it contains. This however is a challenge, which this study aims to address. This paper proposes an ANN-based approach to analyse user-generated comments from social media. The first mechanism of the approach is to map comments against predefined product attributes. The second mechanism is to generate input-output models which are used to statistically address the significant relationship between attributes and comment length. The last mechanism employs Artificial Neural Networks to formulate such a relationship, and determine the constitution of rich comments. The application of proposed approach is demonstrated with a case study, which reveals the effectiveness of the proposed approach for assessing product performance. Recommendations are provided and direction for future studies in social media data mining is marked

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

There is a general consensus of the good sensing and novelty character- istics of Twitter as an information media for the complex fi nancial market. This paper investigates the permeability of Twitter sphere, 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 fi nancial-specifi c events and establishes Twitter as a relevant feeder for taking decisions regarding the fi nancial market and event fraudulent activities in that market. However, the provenance of contributions, their diferent 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 specifi c 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 (LSE), to create the UK's Leading Food Business. The experiment attempts to answer two research questions which aim to characterize the features of Twitter permeability to the fi nancial market. The experimental results con rm that a far-impacting financial event, such as the merger considered, caused apparent disturbances in all the features considered, that is, information volume, content and sentiment as well as geographical provenance. Analysis shows that despite, Twitter not being a specifi c fi nancial forum, it is permeable to financial events