Rumor Clarification, Digital Platform, and Stock Movement

Stock return is influenced by information release, dissemination, and acceptance. Rumor clarification is supposed to reduce asymmetric information and abnormal stock return. In this research, we extracted 4134 rumor-clarification pairs from 687,429 postings in social media, and quantified the language used in these messages, along with online firm behaviors, to study the effect of clarifications on stock returns. Our findings include (1) the digitalized rumor clarification messages affect the abnormal returns of the relevant stocks; (2) Such influence can be quantified and measured by the emotion polarity of rumor clarification; (3) Firm’s online clarification behaviors may have no influence on abnormal returns except for the total response number of rumor clarification for a listed company. In particular, investors prefer to trust the clarifications from the companies with frequent online interactive engagements

Research on Automatic Identification of Rumors in Stock Forum Based on Machine Learning

When rumors prevail in securities market, it is very difficult for investors to identify valid information. In the meantime, investors have much more ways to access information with the evolution of internet. But there is an overwhelming quantity of information on the Internet, the coexistence of facts and rumors, namely, “widely circulated” and “specious”, yet “unconfirmed officially” vague information, makes it more difficult for investors who with limited rationality to distinguish facts from rumors. Existing studies are mainly devoted in the method of event study, namely screening rumors from “official channels” that clarified, which is neither timely efficient in terms of accessing to rumors nor providing the basis for decision-making. Traditional news has evolved into various forms of social media, including forums, blogs, micro-blogs etc., and users can not only gain quick access to more valuable and timely information, but also amplify information that embed the news effectively by participating in commenting on various social media. Dynamic information creation, sharing and coordination among Web users are exerting increasingly prominent impact on the securities market in now days. Thus, it is very necessary to study the effects of social media as online forums on the securities market. In this paper, the method of machine learning is adopted for the first time to identifying the Internet rumors automatically, and successfully in crawling massive forum data by smart computer technology. Unlike the case study and statistical sampling of rumors, this paper conduct automatic identification of Internet rumors by utilize the smart technology, thus paving the way for more in-depth analysis about the effects of Internet media on the securities market in future

Event Study and Principal Component Analysis Based on Sentiment Analysis – A Combined Methodology to Study the Stock Market with an Empirical Study

This paper provides an improved method by introducing Sentiment Analysis into the Event Study and Principal Component Analysis. The model is constructed by using the heuristic mean-end analysis. This method enables us to take into investors’ feelings towards related stocks when we study the stock market’s reaction to a given event. This paper investigates the Chinese A-shared market over 2013–2019 to study the influence of rumors and the offsetting impact of rumor clarifications on the stock price. The results indicate that no matter investor sentiment is bullish or bearish, stock price reacts significantly to rumors before as well as when the rumor goes public. Furthermore, clarification offsets the positive abnormal returns caused by rumors with bullish sentiment substantially at a limited level. Still, after five days, it creates a positive effect like the positive rumor does on the stock price. Under the bearish sentiment, clarification brings an insignificant impact on the stock price. The results indicate that the source of rumor may not come from the media and investment decisions established on rumors would be beneficial to investors before as well as after they are published. Moreover, official clarification causes an offset effect, but it is very limited

Market reactions for targets of M&A rumours—evidence from China

This paper investigates investors’ reactions to takeover rumours in China’s stock markets from 2004 to 2014. While we find prerumour price run-ups (abnormal returns) for merger and acquisition (M&A) targets, the pre-rumour market overreaction is significantly positive only for target firms that are state-owned enterprises (SOEs). There are no significant abnormal returns for M&A rumour targets over a 41-day event window (20, þ20). Nonetheless, capital market reactions to true rumours are higher than reactions to false rumours, indicating that investors can typically distinguish between them. Finally, we document that while firms with higher institutional ownership have a higher probability of being the subject of false M&A rumours, rumoured targets with higher institutional ownership experience lower market reactions

Complexity in Economic and Social Systems

There is no term that better describes the essential features of human society than complexity. On various levels, from the decision-making processes of individuals, through to the interactions between individuals leading to the spontaneous formation of groups and social hierarchies, up to the collective, herding processes that reshape whole societies, all these features share the property of irreducibility, i.e., they require a holistic, multi-level approach formed by researchers from different disciplines. This Special Issue aims to collect research studies that, by exploiting the latest advances in physics, economics, complex networks, and data science, make a step towards understanding these economic and social systems. The majority of submissions are devoted to financial market analysis and modeling, including the stock and cryptocurrency markets in the COVID-19 pandemic, systemic risk quantification and control, wealth condensation, the innovation-related performance of companies, and more. Looking more at societies, there are papers that deal with regional development, land speculation, and the-fake news-fighting strategies, the issues which are of central interest in contemporary society. On top of this, one of the contributions proposes a new, improved complexity measure

\u3ci\u3eThe New York Times\u3c/i\u3e and Media Framing During the Coronavirus Pandemic

This study analyzes how The New York Times framed the coronavirus pandemic via Twitter. Using framing theory, this study examines a collection of tweets from The New York Times Twitter account during the week of March 13-20, 2020, which was the week following President Donald Trump’s declaration of a national emergency. Each tweet was manually cataloged according to a specific set of qualifying keywords. From the collected tweets, this study identified four dominant frames that characterized The New York Times’ coverage of the pandemic during the week of March 13, 2020. This study also discusses the structure of the “disease narrative” and the rise of Twitter as a legitimate source for news

Investor relations: A bibliometric study in behavioral finance, behavioral economics and behavioral accounting

This study aims to verify the main characteristics of the scientific production on behavioral finance applied to the area of investor relations. Through a bibliometric research carried out in the Scopus database, 121 publications were found, which were analyzed by statistical and graphic techniques. The results indicate a numerically reduced and volatile production of articles on the subject, without robust growth so far. Few documents were found by author, institution, and journal, as well as a small number of connections, concerning citations, among them. This research area is thus largely unexplored within behavioral finance, although academic production in behavioral economics on the use of prospects for selling complex products is proportionally very broad. Bibliographic coupling indicated four main themes, the most relevant being the influence of the different disclosure means of financial information on investor behavior. These results suggest that the subject has not aroused the interest of researchers and that, although relevant, it is still little explored. On the other hand, the results indicate a broad field for further studies. These findings are relevant, since they allow a clear and comprehensive view on a relevant and still little explored theme

Reputational Sanctions in China\u27s Securities Market

Literature suggests two distinct paths to stock market development: an approach based on legal protections for investors, and an approach based on self-regulation of listed companies by stock exchanges. This Essay traces China\u27s attempts to pursue both approaches, while focusing primarily on the role of the stock exchanges as regulators. Specifically, the Essay examines a fascinating but unstudied aspect of Chinese securities regulation – public criticism of listed companies by the Shanghai and Shenzhen exchanges. Based on both event study methodology and extensive interviews of market actors, we find that the public criticisms have significant effects on listed companies and their executives. On both exchanges, significant abnormal stock price returns occur in response to corporate disclosure of the underlying misconduct giving rise to the criticisms, as well as in response to publications of the criticisms themselves. Interviews suggest that the impact of the stock exchange criticisms extends beyond the stock market, as banks and bank regulators make use of the sanction data for their own purposes. We evaluate the role of public criticisms in China\u27s evolving scheme of securities regulation, contributing to several strands of research on the role of the media in corporate governance, the use of shaming sanctions in corporate governance, and the importance of informal mechanisms in supporting China\u27s economic growth