Are investors guided by the news disclosed by companies or by journalists?

Most previous studies demonstrating the influential role of the textual information released by the media on stock market performance have concentrated on earnings-related disclosures. By contrast, this paper focuses on disposal announcements, so that the impacts of listed companies’ announcements and journalists’ stories can be compared concerning the same events. Consistent with previous findings, negative words, rather than those expressing other types of sentiment, statistically significantly affect adjusted returns and detrended trading volumes. However, extending previous studies, the results of this paper indicate that shareholders’ decisions are mainly guided by the negative sentiment in listed companies’ announcements rather than that in journalists’ stories. Furthermore, this effect is restricted to the announcement day. The average market reaction–measured by adjusted returns–is inversely related only when the announcements are ignored by the media, but the dispersion of market reaction–measured by detrended trading volume–is positively affected only when announcements are followed up by journalists.

A survey of data mining techniques for social media analysis

Social network has gained remarkable attention in the last decade. Accessing social network sites such as Twitter, Facebook LinkedIn and Google+ through the internet and the web 2.0 technologies has become more affordable. People are becoming more interested in and relying on social network for information, news and opinion of other users on diverse subject matters. The heavy reliance on social network sites causes them to generate massive data characterised by three computational issues namely; size, noise and dynamism. These issues often make social network data very complex to analyse manually, resulting in the pertinent use of computational means of analysing them. Data mining provides a wide range of techniques for detecting useful knowledge from massive datasets like trends, patterns and rules [44]. Data mining techniques are used for information retrieval, statistical modelling and machine learning. These techniques employ data pre-processing, data analysis, and data interpretation processes in the course of data analysis. This survey discusses different data mining techniques used in mining diverse aspects of the social network over decades going from the historical techniques to the up-to-date models, including our novel technique named TRCM. All the techniques covered in this survey are listed in the Table.1 including the tools employed as well as names of their authors.