Tweets, Google Trends and Sovereign Spreads in the GIIPS

We examine whether the information contained in social media (Twitter, Facebook & Google Blogs) and web search intensity (Google) influences financial markets. Using a multivariate system and focussing on Eurozone’s peripheral countries, the GIIPS (Greece, Ireland, Italy, Portugal and Spain) as well as two of Eurozone’s core countries (France and the Nethelands), we show that social media discussion and search-related queries for the Greek debt crisis provide significant short-run information primarily for the Greek-German and Irish-German government bond yield differential even when other financial control variables (international risk, Eurozone’s risk, default risk and liquidity risk) are accounted for, and to a much lesser extent for Portuguese, Italian and Spanish sovereign yield differentials. Social media discussion and Google search-related queries for the Greek debt crisis do not affect spreads in France and the Netherlands

Why Should Competition Lawyers Care About the Formal Rule of Law?

This paper re-examines stock returns predictability over the business cycle using price-dividend and price-earnings valuation ratios as predictors. Unlike prior studies that habitually implement long-horizon/predictive regressions, we conduct a testing framework in the frequency domain. Predictive regressions support no predictability; in contrast, our results in the frequency domain verify significant predictability at medium and longhorizons. To robustify predictability patterns, the analysis is executed repetitively for fixed-length rolling samples of various sizes. Overall, stock returns are predictable for wavelengths higher than five years. This finding is robust and independent of time, window size and predictor

Identifying networks in social media: The case of #Grexit

We examine the intensity of ‘#Grexit’ usage in Twitter during a period of economic and financial turbulence. Using a frequency-analysis technique, we illustrate that we can extract detailed information from social media data. This allows us to map the networks of interest as it is reflected in Twitter. Our findings identify high-interest in Grexit from Twitter users in key peripheral countries, core Eurozone members as well as core EU member states outside the Eurozone. Overall, our study presents a useful tool for identifying clusters. This is part of a new research agenda utilising the information extracted from big data available via social media channels

Residential electricity pricing in China

The paper aims to evaluate the implications of the new residential pricing system in China by examining price and income elasticity of demand by different household types. We use pre-reform annual panel data for 29 provinces over a fourteen year period, from 1998 to 2011, applying feasible generalize least squares models. The price and income elasticities for household sector are -0.412, and 1.476 at nation level, -0.300 and 1.550 in urban areas and -0.522 and 1.093 in rural areas respectively. With regional effects, the price and income elasticities are -0.146 and 1.286 for urban households in coastal provinces and -0.772 and 1.259 for urban households in inland provinces respectively. The empirical results reveal that there is important heterogeneity in the responsiveness to electricity price changes according to household income level and location

Tracing tourism geographies with Google Trends: a Dutch case study

Search engines make information about places available to billions of users, who explore geographic information for a variety of purposes. The aggregated, large-scale search behavioural statistics provided by Google Trends can provide new knowledge about the spatial and temporal variation in interest in places. Such search data can provide useful knowledge for tourism management, especially in relation to the current crisis of tourist (over)crowding, capturing intense spatial concentrations of interest. Taking the Amsterdam metropolitan area as a case study and Google Trends as a data source, this article studies the spatial and temporal variation in interest in places at multiple scales, from 2007 to 2017. First, we analyze the global interest in the Netherlands and Amsterdam, comparing it with hotel visit data. Second, we compare interest in municipalities, and observe changes within the same municipalities. This interdisciplinary study shows how search data can trace new geographies between the interest origin (what place users search from) and the interest destination (what place users search for), with potential applications to tourism management and cognate disciplines

Revisiting residential demand for electricity in Greece: new evidence from the ARDL approach to cointegration analysis

Residential electricity demand, ARDL, Cointegration, C22, Q41, Q43,