Soccer Sponsor: Fan Or Businessman?
Can self-assessments of health reveal the true health differentials between ‘rich’ and ‘poor’? The potential sources of bias include psychological adaptation to ill-health, socioeconomic covariates of health reporting errors and income measurement errors. We propose an estimation method to reduce the bias by isolating the component of self-assessed health that is explicable in terms of objective health indicators and allowing for broader dimensions of economic welfare than captured by current incomes. On applying our method to survey data for Russia we find a pronounced (nonlinear) economic gradient in health status that is not evident in the raw data. This is largely attributable to the health effects of age, education and location.
The question about possibilities to use Twitter users’ moods to increase accuracy of stock price movement prediction draws attention of many researchers. In this paper we examine the possibility of analyzing Twitter users’ mood to improve accuracy of predictions for Gold and Silver stock market prices. We used a lexicon-based approach to categorize the mood of users expressed in Twitter posts and to analyze 755 million tweets downloaded from February 13, 2013 to September 29, 2013. As forecasting technique, we select Support Vector Machines (SVM), which have shown the best performance. Results of SVM application to prediction the stock market prices for Gold and Silver are discussed.
Development of linguistic technologies and penetration of social media provide powerful possibilities to investigate users’ moods and psychological states of people. In this paper we discussed possibility to improve accuracy of stock market indicators predictions by using data about psychological states of Twitter users. For analysis of psychological states we used lexicon-based approach, which allow us to evaluate presence of eight basic emotions in more than 755 million tweets. The application of Support Vectors Machine and Neural Networks algorithms to predict DJIA and S&P500 indicators are discussed.
Research question: This paper investigates how football sponsorship influences the financial performance of sponsors. We suggest a new instrumental variable to avoid endogeneity.
Research methods: We use an instrumental variable regression framework combined with a fixed effects model. The number of tweets containing both team and sponsor names are collected to use as the instrumental variable.
Results and findings: We analyze top European leagues. Our results show that football sponsorship is more charity than commercial investment. The analysis of determinants of becoming a sponsor and sponsorship amount shows that companies owned by individuals are more likely to become a sponsor.
Implications: Shareholders should be aware of sponsorship deals, and senior management should analyze the financial assumptions of such projects carefully.
According to a number of scholars, Twitter possesses big potential to become a “crossroads of discourses” due to its openness, de-hierarchization, and spontaneity (Miller, 2010; Shirky, 2008). At the same time, substantial criticism has risen towards political and deliberative efficacy of Twitter (Fuchs, 2014). The authors aim at analyzing the features of the Twitter-based agenda setting within the hybrid media system in Russia (Chadwick, 2013; Bodrunova and Litvinenko, 2013a). The research question is whether the use of Twitter in the Russian socio-political context potentially leads to the formation of the “crossroads of opinions” or, in contrast, to closing-up of political discussion and to further fragmentation of public discourse. The research focuses on structural and content aspects of discussion on anti-migrant bashings in Biryulyovo (Moscow) that happened in October 2013. Our research methods include automated vocabulary-based web crawling, word frequency analysis, manual coding of tweets, and interpretation of statistical data. Preliminary results suggest an unexpectedly high level of mediatization of the discussion; the hypothesis about the “crossroads” nature of the discussion on the Russian Twitter seems to be proven, which makes this platform differ from the Russian Facebook where, according to another recent study (Bodrunova and Litvinenko, 2013b), political discussions are held mostly in closed-up communicative milieus, or “echo chambers” (Sunstein, 2007).