Does investor attention influence stock market activity? The case of spin-off deals

This paper investigates empirically the nature of the interactions between mass media, investor attention and the stock market using data from a sample of 16 spin-off deals traded on NYSE and published between 2004 and 2010 in “Wall Street Journal”, the US’s second-largest newspaper by circulation. The results show that: i) the impact of media sentiment on the stock market reactions is enhanced / moderated by the level of attention of investors; ii) individual investors’ attention is grabbed by stocks experiencing high trading volumes on the previous day; iii) high attention could result in downward pressure on stock market returns.Media Sentiment, Investor Attention, Internet Search, Spin-off

Dynamics of Information Diffusion and Social Sensing

Statistical inference using social sensors is an area that has witnessed remarkable progress and is relevant in applications including localizing events for targeted advertising, marketing, localization of natural disasters and predicting sentiment of investors in financial markets. This chapter presents a tutorial description of four important aspects of sensing-based information diffusion in social networks from a communications/signal processing perspective. First, diffusion models for information exchange in large scale social networks together with social sensing via social media networks such as Twitter is considered. Second, Bayesian social learning models and risk averse social learning is considered with applications in finance and online reputation systems. Third, the principle of revealed preferences arising in micro-economics theory is used to parse datasets to determine if social sensors are utility maximizers and then determine their utility functions. Finally, the interaction of social sensors with YouTube channel owners is studied using time series analysis methods. All four topics are explained in the context of actual experimental datasets from health networks, social media and psychological experiments. Also, algorithms are given that exploit the above models to infer underlying events based on social sensing. The overview, insights, models and algorithms presented in this chapter stem from recent developments in network science, economics and signal processing. At a deeper level, this chapter considers mean field dynamics of networks, risk averse Bayesian social learning filtering and quickest change detection, data incest in decision making over a directed acyclic graph of social sensors, inverse optimization problems for utility function estimation (revealed preferences) and statistical modeling of interacting social sensors in YouTube social networks.Comment: arXiv admin note: text overlap with arXiv:1405.112

False News On Social Media: A Data-Driven Survey

In the past few years, the research community has dedicated growing interest to the issue of false news circulating on social networks. The widespread attention on detecting and characterizing false news has been motivated by considerable backlashes of this threat against the real world. As a matter of fact, social media platforms exhibit peculiar characteristics, with respect to traditional news outlets, which have been particularly favorable to the proliferation of deceptive information. They also present unique challenges for all kind of potential interventions on the subject. As this issue becomes of global concern, it is also gaining more attention in academia. The aim of this survey is to offer a comprehensive study on the recent advances in terms of detection, characterization and mitigation of false news that propagate on social media, as well as the challenges and the open questions that await future research on the field. We use a data-driven approach, focusing on a classification of the features that are used in each study to characterize false information and on the datasets used for instructing classification methods. At the end of the survey, we highlight emerging approaches that look most promising for addressing false news

Risk Spillovers and Interconnectedness between Systemically Important Institutions

In this paper, we gauge the degree of interconnectedness and quantify the linkages between global and other systemically important institutions, and the global financial system. We document that the two groups and the financial system become more interconnected during the global financial crisis when linkages across groups grow. In contrast, during tranquil times linkages within groups prevail. Global systemically important banks (G-SIBs) contribute most to system-wide distress but are also most exposed. There are more links coming from G-SIBs to other systemically important institutions (O-SIIs) than the other way around, confirming the role of G-SIBs as major risk transmitters in the financial system. The two groups and the global financial system tend to co-vary for periods up to 60 days Prior to their official designation as G-SIBs or O-SIIs, the prevalent news sentiment about these institutions (we measure with a textual analysis) was negative. Importantly, the systemic importance and exposure of G-SIBs and O-SIIs is perceived differently by the Financial Stability Board (FSB) and the European Banking Authority (EBA)

A network model of mass media opinion dynamics

The coexistence of diverse opinions is necessary for a pluralistic society in which people can confront ideas and make informed choices. The media functions as a primary source of information, and diversity across news sources in the media forms the basis for wider discourse in the public. However, due to numerous economic and social pressures, news sources frequently co-orient their content through what is known as intermedia agenda-setting. Past research on the subject has examined relationships between individual news sources. However, to understand emergent behaviour such as opinion diversity, we cannot simply analyse individual relationships in isolation, but instead need to view the media as a complex system of many interacting entities. The aim of this thesis is to develop and empirically test a method for understanding the network effects that intermedia agenda-setting has on the diversity of expressed opinions within the media. Utilising latent signals extracted from news articles, we put forward a methodology for inferring networks that capture how agendas propagate between news sources via the opinions they express on various topics. By applying this approach to a large dataset of news articles published by globally and locally prominent news organisations, we identify how the structure of intermedia networks is indicative of the level of opinion diversity across various topics. We then develop a theoretical model of opinion dynamics in noisy domains that is motivated by the empirical observations of intermedia agenda formation. From this, we derive a general analytical expression for opinion diversity that holds for any network and depends on the network's topology through its spectral properties alone. Finally, we validate the analytical expression in a linear model against empirical data. This thesis aids our understanding of how to model emergent behaviour of the media and promote diversity

The agenda-setting relationship between the news media and public opinion: the case of global warming 1988-1992

This thesis is an investigation of the relationship between the mass media and public opinion. For just over twenty years, mass communication researchers have been studying this relationship under the rubric of agenda-setting. This thesis will embrace that approach. Agenda-setting is a term applied somewhat loosely to a class of research which seeks to unveil relationships between what the mass media portray as important and what the public considers to be important. This line of inquiry grew out of social scientists\u27 interest in the effects of the mass media on society. The central question in all agenda-setting studies is: Do the mass media influence what we think about, and what we consider to be important