Browsing by Subject "Twitter"

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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.