Browsing by Subject "Twitter"

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Emotions and entrepreneurial finance: analysis of venture capitalists’ and business angels’ digital footprints on Twitter
(2024) Kaiser, Manuel; Kuckertz, Andreas
Emotions are a central concept in previous entrepreneurship research, but this is mainly related to entrepreneurs and their entrepreneurial journey. However, venture capitalists (VCs) and business angels (BAs), two critical investors in the entrepreneurial finance literature, are essential actors in the entrepreneurial process. Still, little is known about investor emotions in this context. Therefore, in this study, we ask how venture capitalists differ from business angels in terms of their expressed emotions. To this end, we use an increasingly familiar research approach by examining the digital footprints of these investors on Twitter. For this purpose, we identify 822 investors from Crunchbase and analyze their 994,969 Tweets with Linguistic Inquiry and Word Count (LIWC) as a text analysis tool. Our results show that venture capitalists display more positive emotions on Twitter than angel investors, meaning that we find an association between VCs and emotional tone. Furthermore, in our post-hoc analysis, we explore further explanations for the differences between VC and BA. In doing so, we show differences in their expressed cognitive processes as well as in their communicated drivers. In both concepts, we find positive associations with the investor type of VC. To conclude this paper, we develop implications for practice and further research based on the results.
Gambling advertising on social media
(2025) Singer, Johannes; Sousa-Poza, Alfonso
Digitalisation has made social media available worldwide and driven the global growth of the gambling industry. In response to these dynamic developments in a rapidly changing media landscape, the State Treaty on Gambling legalised online gambling in Germany in 2021. Providers are now also permitted to use advertising to direct interested parties to the legal gambling market. Since the legal gambling market, unless illegal gambling offers, is supervised by the German gambling authority, and providers must comply with applicable regulations, the aim is to protect the population, especially vulnerable groups, from gambling-related harm. However, advertising gambling can pose risks, especially for children and young people. A particularly high volume of gambling advertising appears on social media because these networks have established themselves as attractive advertising platforms. As children and young people are the most active social media users in Germany, they are likely to be exposed to a high level of gambling advertising on these platforms. Against this background, this dissertation analyses gambling advertising on social media in Germany for the first time. First, a systematic review provides an overview of the research literature on the advertising strategies of gambling providers and their effects on social media. To this end, three scientific databases were searched, and international journal articles in English published in or after 2021 were included. The review addresses the following four questions: (i) What are the characteristics of gambling advertising on social media? (ii) How do gambling advertisements and strategies affect users' attitudes and behaviour on social media? (iii) What safeguards are in place to protect individuals, particularly vulnerable groups, from gambling-related harm? (iv) What research gaps exist in the study of gambling advertising? Clearly, social media play a central role in gambling providers' advertising strategies. They can adapt their strategies flexibly to prevailing circumstances. However, the increasing intensity and complexity of gambling advertising, coupled with the blurring of boundaries between advertising and neutral content, appear problematic. Vulnerable groups, especially children and young people, are exposed to an increased risk as these forms of advertising are especially appealing to them. Furthermore, rapid developments make it difficult for regulatory authorities to keep up. The following chapter makes a first attempt to analyse the advertising strategies and content of German gambling providers on social media. For this purpose, various providers from different sectors are compared with each other. These include providers of sports betting, state lotteries, social lotteries, commercial lotteries, and casinos. The data analysis is based on tweets from selected accounts on the microblogging platform X (formerly Twitter) that were collected via the associated API. A mixed-methods approach is adopted, combining a qualitative summative content analysis with semi-supervised guided topic modelling to determine the frequency, number of followers, interactions, and content of the tweets. Overall, the accounts of German gambling providers demonstrate significantly lower activity levels than those of providers in other countries. Apart from the fact that all sports betting providers mainly used content from the 'News' category, no clear patterns emerged in the advertising strategies of the different providers or sectors. However, the ‘News’ content category was often used in combination with less neutral content categories. This is problematic given that most tweets had hardly any age restrictions or responsible gambling messages. Consequently, this leads to blurring of the distinction between neutral content and gambling advertisements, particularly when the latter is not explicitly identifiable as advertising. This advertising practice specifically endangers children and young people, despite the State Treaty on Gambling defining this target group as needing protection. Finally, a deep learning approach was employed to examine the stigmatisation of gambling disorder on the video platform YouTube in Germany and how it is expressed in the everyday language of users. As stigmatisation and self-stigmatisation are substantial barriers to therapy, and as young people are the age group most affected by gambling-related harm and the most active social media users, it is reasonable to assume that they encounter stigmatising statements on social media. This analysis combines guided topic modelling with qualitative summative content analysis to demonstrate the occurrence of statements associated with the stigmatisation of gambling disorder. Gambling disorder is associated with negative attributes and moral judgments. However, social media can also offer a suitable channel through which to protect young people from the negative consequences of stigmatisation and disseminate information to the general public, as well as targeted groups that are at risk. For example, attention could be drawn to treatment options or the fact that recovery is possible. It can also emphasise that seeking help is a sign of strength, not weakness. This highlights the potential of social media to help reduce the stigma surrounding gambling disorder. To summarise, the role of social media in the context of the State Treaty on Gambling can be described as ambivalent. On the one hand, they are suitable as an attractive advertising platform for gambling providers to address interested individuals and direct them to the state-monitored, legal gambling market. In this sense, they help to protect the population from gambling-related harm since legal gambling providers are subject to the control of the state gambling supervisory authority and must comply with applicable gambler protection regulations, such as advertising regulations. In addition, social media offers the opportunity to address a young age group and inform them about prevention options and offers of help. On the other hand, young people, as the most active user group on social media in Germany, are highly exposed to gambling advertising. Since young people in Germany are most frequently affected by a gambling-related disorder, this certainly seems problematic. High exposure to gambling advertisements harbours health risks, especially as such content is often not accompanied by age restrictions and responsible gambling messages. This blurs the lines between harmless content and gambling advertising. Furthermore, gambling providers can flexibly adapt their advertising strategies on social media to evolving conditions. The fast-moving changes in the media landscape and the advertising practices of gambling providers ultimately present the regulatory authorities with the challenge of keeping pace with these dynamic developments.
How do gambling providers use the social network twitter in Germany? An explorative mixed-methods topic modeling approach
(2023) Singer, Johannes; Kufenko, Vadim; Wöhr, Andrea; Wuketich, Marius; Otterbach, Steffen
This study examines the social media activities of gambling providers in Germany, focusing on the platform Twitter. A collection of 34.151 tweets from 13 Twitter accounts was made, representing casinos, sports betting, state lotteries, social lotteries and lottery brokers. We apply an explorative mixed-methods approach, integrating a summative content analysis together with a semi-supervised guided topic modeling approach, to analyse frequency, number of followers, interaction and content of Twitter messages, and work out differences among the individual providers. The results show that Twitter does not seem to be particularly important for gambling providers who are active in Germany. Regarding outreach, frequency of tweets and interaction, Twitter plays a much smaller role than, for example, in the UK. The potential for Twitter to be an advertising, marketing and interaction channel has not yet been fully exploited, which would make it a perfect moment for developing an appropriate regulatory framework. Overall, the results of semi-supervised topic modeling show that providers most often use informative content ( news ) and combine it with other, usually less neutral content. It is alarming that many providers make little or no use of Responsible Gambling messages. Even though the activities are presently on a low level, they contribute to the normalization of gambling, setting incentives for increasing gambling participation. Children and youth form a large segment of Twitter users. Potential harm can arise especially for this group because of the lack of enforcement of any age limit.
Intermedia agenda-setting from the far right? Three case studies on spillover effects by alternative media inGermany
(2023) Klawier, Tilman
Right-wing alternative media can increase their public impact if they succeed to set their issues on the mainstream media’s agenda. In three qualitative case studies, the present article explores whether and how such intermedia agenda-setting occurs in Germany. Special attention is given to spillover effects between different actors, both at the level of attention and tone towards the issues. Furthermore, the analysis of news articles is supplemented with Twitter data to account for the role of social media. Two of the case studies indicate that right-wing alternative media contributed to push pseudo-scandals into the mainstream. The analyses also reveal alternative news outlets with particular agenda-setting power and point to the crucial role of tabloid media as a bridge to the mainstream. The third study, however, which centered on the Global Compact for Migration, presents a case where intermedia agenda-setting failed. Against this background, the article discusses the conditions under which intermedia agenda-setting by right-wing alternative media is likely to occur and how journalists should deal with such attempts.
Investor beliefs and their impact on financial markets
(2021) Hartmann, Carolin; Burghof, Hans-Peter
The idea of this thesis is to use new data sources to approximate investor beliefs. It investigates whether the approximation improves the measurement of return and volatility in existing model frameworks. The findings are that differences in implied volatility, Google Search volume and Twitter Volume can be proxy variables for investor beliefs. They have an impact on financial market indicators and on the prediction of future market movements. Comparison of the trading behaviour of individual and institutional investors to predict market movements The first approach is to create a new sentiment index which compares the difference between retail investor behaviour at the Stuttgart Stock Exchange (SSE) and professional investors at the Frankfurt Stock Exchange (FSE). The measure is a comparison between the implied volatility measures for the DAX at the FSE (VDAX and VDAX-NEW) and a newly created implied volatility index (VSSE) for the SSE. The sentiment index is significant in predicting the daily returns on a size-based long-short portfolio over a four-year period. The analysis shows the persistent inconsistence between prices of structured products for retail investors on the SSE and option prices of professional investors on the FSE. The results provide empirical evidence that there are significant persistent behavioural differences between the two investor types which is reflected in persistent mispricing. Measurability of investor beliefs and their impact on financial markets The second approach is to measure individual investor beliefs with Google search volume (GSV) and Twitter volume (TV) to analyse their impact on financial markets. The basis is a daily panel of 29 Dow Jones Industrial average index (DJIA) stocks over a time period of 3.5 years in a panel data set-up. The impact on trading activity measured by turnover, is positive for GSV and TV on the same day and the next day which indicates their predictive power. The impact on realized volatility (RV), indicating the share of noise traders on the market, is only positive and significant for TV. It is significant on the same day and the next day. The impact of GSV is not significant. The results support the idea that GSV and TV capture the beliefs of individual investors. Although they suggest that the impact of TV on financial markets is more important than the impact of GSV. Predictive power of Google and Twitter The third approach is to use GSV and TV as a proxy for investor attention and investor sentiment, to assess their predictive power on the RV of the DJIA. The basis is a time-series set-up with a vector autoregression (VAR) model over a period of 2.5 years. The findings show that GSV and TV granger cause RV, controlling for macroeconomic and financial factors. Again, the effect of TV on RV is more important than the effect of GSV. In-sample, the linear prediction model with GSV and TV outperforms a standard AR (1) process. Out-of-sample the AR (1) process outperforms the standard model with GSV and TV. Clustering for high and low volatility groups, the analysis shows that the effect of GSV and TV on RV changes. Especially in times of high and low RV, GSV and TV seem to contain new information, as they improve the model fit compared to a standard AR (1) process. However, the results are not persistent in- and out-of-sample. This underlines that the results of GSV and TV are not generally persistent but depend on the selected criteria. Overall, the results of this thesis show that investor beliefs have an impact on financial markets. The measures, such as a sentiment index based on implied volatility, GSV and TV are proxy variables for investor beliefs. Future research should further improve the comprehension of investor beliefs to improve causality and economic significance in the long term.
On the implications of recent advancements in information technologies and high-dimensional modeling for financial markets and econometric frameworks
(2019) Schmidt, Alexander; Jung, Robert
Around the turn of the millennium, the Organization for Economic Co-operation and Development (OECD) published an article, which summarizes the organizations expectations towards technological developments of the 21st century. Of particular interest to the authors are innovations in the area of information technology, highlighting their far-reaching impact on, amongst others, the financial sector. According to the article, the expected increasing interconnectedness of individuals, markets, and economies holds the potential to fundamentally change not only the flow of information in financial markets but also the way in which people interact with each other and with financial institutions. Looking back at the first two decades of the 21st century, these predictions appear to have been quite accurate: The rise of the internet to a platform of utmost relevance to industries and the economy as a whole profoundly impacts how people nowadays receive and process information and subsequently form, share, and discuss their opinions amongst each other. At the financial markets around the globe trading has become more and more accessible to individuals. Less financial and technical knowledge is required of retail investors to engage in trading, resulting in increased market participation and more heterogeneous trader profiles. This, in turn, influences the dynamics in the financial markets and challenges some of the conventional wisdom concerning market structures. In this context, the interdependencies between the media, retail investors, and the stock market are of particular interest for practitioners. However, the changed dynamics in the flow and exchange of data and information are also highly interesting from a researchers perspective, resulting in entire branches of the academic literature devoted to the topic. While these branches have grown in many different directions, this doctoral thesis explores two specific aspects of this field of research: First, it investigates the consequences of the increased interconnectedness of individuals and markets for the dynamics between the new information technologies and the financial markets. This entails gaining new insights about these dynamics and assessing how investors process certain company-related information for their investment decisions by means of sentiment analysis of large, publicly available data sets. Secondly, it illustrates how an advanced understanding of high-dimensional models, resulting from such analyses of large data sets, can be beneficial in re-thinking and improving existing econometric frameworks. Three independent but related research projects are presented in this thesis that address both of the aforementioned aspects to give a more holistic picture of the implications that the profoundly changed flow and exchange of data and information of the last decades hold for finance and econometrics. As such, the projects (i) highlight the importance of carefully assessing the dynamics between investor sentiment and stock market volatility in an intraday context, (ii) analyze how investors process newly available, rich sources of information on a firms environmental, social, and governance (ESG) practices for their investment decisions, and (iii) propose a new approach to detecting multiple structural breaks in a cointegrated framework enabled by new insights about high-dimensional models. The first original work of this doctoral thesis aims at closing an existing gap in the behavioral finance literature by taking an intraday perspective in assessing the relationship between investor sentiment and stock market volatility. More precisely, the paper titled "The Twitter myth revisited: Intraday investor sentiment, Twitter activity and individual-level stock return volatility", which is joint work with Simon Behrendt, takes a closer look at the dynamics of individual-level stock return volatility, measured by absolute 5-minute returns, and Twitter sentiment and activity in an intraday context. After accounting for the intraday periodicity in absolute returns, we discover some statistically significant co-movements of intraday volatility and information from stock-related Tweets for all constituents of the Dow Jones Industrial Average (DJIA). However, economically, the effects are of negligible magnitude, and out-of-sample forecast performance is not improved when including Twitter sentiment and activity as exogenous variables. From a practical point of view, this chapter finds that high-frequency Twitter information is not particularly useful for highly active investors with access to such data for intraday volatility assessment and forecasting when considering individual-level stocks. Inspired by this first research project, the second original work presented in this thesis keeps its focus on sentiment analysis in the context of the financial markets. Titled "Sustainable news - A sentiment analysis of the effect of ESG information on stock prices", it investigates the effect of ESG-related news sentiment on the stock market performance of the DJIA constituents. Relying on a large data set of news articles that were published online or in print media between the years of 2010 and 2018, each articles sentiment with respect to ESG-related topics is extracted using a dictionary approach from which a polarity-based sentiment index is calculated. Estimating autoregressive distributed lag models reveals significant effects of both temporary and permanent changes in ESG-related news sentiment on idiosyncratic returns for the vast majority of the DJIA constituents. According to the models results, one can assign the stocks to different groups depending on their investors apparent predisposition towards ESG news, which in turn seems to be linked with a stocks financial performance. The last original work presented is then concerned with the second aspect of this doctoral thesis - the question of how our enhanced understanding of the increasingly high dimensional datasets that occur in practice can produce new solutions to familiar problems in econometrics. The paper "Multiple structural breaks in cointegrating regressions: A model selection approach", which is joint work with Karsten Schweikert, introduces the least absolute shrinkage and selection operator (lasso) as a tool for consistent breakpoint estimation. In this paper, we propose a new approach to model structural change in cointegrating regressions using penalized regression techniques. First, we consider a setting with fixed breakpoint candidates and show that a modified adaptive lasso estimator can consistently estimate structural breaks in the intercept and slope coefficient of a cointegrating regression. In such a scenario, one could also perceive our method as performing an efficient subsample selection. Second, we extend our approach to a diverging number of breakpoint candidates and provide simulation evidence that timing and magnitude of structural breaks are consistently estimated. Third, we use the adaptive lasso estimation to design new tests for cointegration in the presence of multiple structural breaks, derive the asymptotic distribution of our test statistics and show that the proposed tests have power against the null of no cointegration. Finally, we use our new methodology to study the effects of structural breaks on the long-run PPP relationship.
Sovereign and bank risk
contagion, policy uncertainty and interest rates
(2024) Bales, Stephan; Burghof, Hans-Peter
This dissertation addresses the dependence between sovereign and bank default risk and the importance of policy uncertainty and interest rates for this nexus. To this end, the thesis includes four self-contained but interrelated studies with different methodological approaches. The first paper sheds light on the cross-country contagion of sovereign and bank default risk between 2009 and 2021 to assess the introduction of the European Banking Union in 2014. Based on Credit Default Swap premia of systemically important banks in the 10 largest eurozone countries, the estimated network structures provide evidence that the introduction of the Single Supervisory Mechanism, as part of the European Banking Union, has been effective in reducing overall financial contagion in the short run (up to 1 month). In the long run, the risk dependence is still very pronounced. Nevertheless, a shock in sovereign or bank risk is less severely transmitted to other eurozone countries after 2014, indicated by lower volatility spillovers. Thus, the Banking Union supports financial stability by weakening the strength of dependence rather than eliminating the dependence itself. The second study takes a closer look at the domestic dependence between sovereign and bank risk in 14 countries. The estimation of dynamic conditional correlations indicates that the dependence is significantly higher in euro member states. This reveals a systematic eurozone risk factor mainly rooted in the home bias of domestic sovereign bond holdings of eurozone banks. Moreover, fixed-effect panel regressions indicate that the sovereign-bank correlation increases in times of great policy uncertainty, high interbank market rates, low bank lending margins, and a low ratio of core bank capital. Economically, banks with a low level of core equity capital are less capable of withstanding shocks to their balance sheets, which spills over to the state and results in higher risk dependence. In addition, banks charge each other higher rates for short-term lending during times of financial distress. In this way, bank liquidity issues and lending aversion in the interbank market are passed on to other banks and ultimately to the sovereign. Overall, the second study emphasizes the importance of bank capital adequacy regulations and joint European policies to mitigate domestic sovereign-bank dependencies. The third study extends prior results and examines the impact of economic policy uncertainty (EPU) on the sovereign-bank nexus by introducing a continuous wavelet time domain. This setting allows to derive causal lead-lag relationships for each point in time. The assessment of the lead-lag relationships in 10 countries shows that a higher level of sovereign default risk leads to an increase in bank risk in the short horizon. In the medium run (6-32 months), the relationship reverses and the default risk of banks determines sovereign risk. Once the influence of policy uncertainty on sovereign and bank risk is eliminated, the partial coherency shows that the sovereign-bank dependence significantly weakens. This reveals the great relevance of political risk factors for the sovereign-bank nexus. The final study addresses the impact of different sources of uncertainty. Besides newspaper-based economic policy uncertainty, the study employs the implied volatility of options written on the S&P500 and a Twitter-based uncertainty index. Based on stock returns of the 22 largest U.S. banks, the computation of principal components, Granger causality, and volatility spillover provides evidence that EPU and Twitter-based uncertainty capture different sources of investor perception in the very short horizon (up to 1 week). Twitter captures consumer uncertainty more appropriately in the short run than newspapers, which usually have a delay in responding to news due to editorial processes. In addition, the study reveals that the impact of uncertainty is considerably stronger for banks with a high ratio of loans to total assets and a large ratio of derivatives to total bank assets. Moreover, banks with a greater loan ratio face a higher level of credit risk. Assuming that bank risk can be transmitted to the state through the sovereign-bank nexus, the results emphasize the importance of differentiating between the sources of uncertainty to evaluate its implications for financial stability. The findings also highlight the increasing importance of social media for the financial markets.