Discovering Organizational Correlations from Twitter

Organizational relationships are usually very complex in real life. It is difficult or impossible to directly measure such correlations among different organizations, because important information is usually not publicly available (e.g., the correlations of terrorist organizations). Nowadays, an increasing amount of organizational information can be posted online by individuals and spread instantly through Twitter. Such information can be crucial for detecting organizational correlations. In this paper, we study the problem of discovering correlations among organizations from Twitter. Mining organizational correlations is a very challenging task due to the following reasons: a) Data in Twitter occurs as large volumes of mixed information. The most relevant information about organizations is often buried. Thus, the organizational correlations can be scattered in multiple places, represented by different forms; b) Making use of information from Twitter collectively and judiciously is difficult because of the multiple representations of organizational correlations that are extracted. In order to address these issues, we propose multi-CG (multiple Correlation Graphs based model), an unsupervised framework that can learn a consensus of correlations among organizations based on multiple representations extracted from Twitter, which is more accurate and robust than correlations based on a single representation. Empirical study shows that the consensus graph extracted from Twitter can capture the organizational correlations effectively.Comment: 11 pages, 4 figure

INTERNATIONAL INVESTMENTS WITH EXCHANGE RATE RISK: THE CASE OF CENTRAL AND EASTERN EUROPE CURRENCIES

The paper investigates the impact that exchange rate risk has on the risk-return profile of investments in emerging countries. The emerging countries under scrutiny are Czech Republic, Hungary, Poland, Romania, Russia and Turkey, all from Central and Eastern Europe. We examine the importance of currency risk from the perspective of a US dollar based investor, by looking at the contribution that changes in exchange rates of these countries’ currencies against the US dollar has for the total risk of investments in these markets, on one hand, and on the correlation between these markets’ returns and the US market return. Our analysis spans over an interval between December 2005 and August, 2009, thereby taking into account the exchange rate risk contribution in normal versus turbulent times. We find that exchange rate volatility is not an additional factor for the volatility of CEE markets when returns are denominated in US dollars. In general, exchange rate risk is a positive contributor to the risk of an investment in CEE markets, and that in more turbulent times, as the ones after September 2008, the impact of exchange rate risk is higher than in normal times. Moreover, in financial crisis times we observe that currency risk lowers the correlation between the US market and CEE markets, and does not indirectly increase the risk of a US investment made in any CEE market through the correlation between markets. Therefore, even in turbulent times, portfolio diversification in CEE financial assets may prove beneficial for US investors.Exchange rate, currency risk, international investments, Central and Eastern Europe

How Prediction Markets can Save Event Studies

Event studies have been used in political science to study the cost of regulation (Schwert, 1981), the value of political connections (Roberts, 1990a; Fisman, 2001), the effect of political parties on defense spending (Roberts, 1990b), the importance of rules in congressional committees (Gilligan and Krehbiel, 1988), the reaction of different interests to trade legislation (Schnietz, 2000), how party control in parliamentary systems affects broad-based stock indices (Herron, 2000), the value of defense contracts (Rogerson, 1989), the effect of the political party of the US President and congressional majorities on particular industry segments (Mattozzi, 2008; Knight, 2006; Herron et al., 1999; Den Hartog and Monroe, 2008; Monroe, 2008; Jayachandran, 2006), and other questions