Does web anticipate stocks? Analysis for a subset of systemically important banks

Is web buzz able to lead stock behavior for a set of systemically important banks? Are stock movements sensitive to the geo-tagging of the web buzz? Between Dec. 2013 and April 2014, we scrape about 4000 world media websites retrieving all public information related to 10 systemically important banks. We process web news with a sentiment analysis algorithm in order to detect article mood. We show that web buzz does not seem to lead stock behavior as Granger test fails to support an average association that goes one-way from web to stocks. We nevertheless find a statistically sound anticipation capacity for single banks with gains ranging from 4 to 12%. Hierarchical clustering and Principal Component Analysis suggest that Euro area level decisions/facts do in fact drive stock behaviour, while web news about single banks only episodically make a difference in stock movements. Our analysis confirms that the location of the web source matters. The use of sources with international echo eliminates some of the noise introduced by irrelevant texts at the country level and improves the predictive power of the model up to 27.5%.JRC.G.1-Financial and Economic Analysi

National Wheat Yield Prediction of France as Affected by the Prediction Level.

The effect of aggregation of simulation results on the prediction of national total wheat yield of France was examined using five prediction models. Three of these models were combinations of trend functions and averages. The other two, an additive and a multiplicative model applied crop growth simulation results in combination with a trend function. The simulation results were aggregated to sub-regional, regional and national level and subsequently introduced, in combination with the estimated planted area, in the prediction models. The prediction results werecompared with official yield statistics. The study demonstrates that aggregation of the simulation results affects the prediction results. Better results are obtained when the predictions are executed at a regional or sub-regionallevel. The multiplicative model performed best.JRC.(SAI)-Space Application Institut

Proceedings of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis

Research in automatic Subjectivity and Sentiment Analysis, as subtasks in Affective Computing within the Artificial Intelligence field of Natural Language Processing (NLP), has flourished in the past years. The growth in interest in these tasks was motivated by the birth and rapid expansion of the Social Web that made it possible for people all over the world to share, comment or consult content on any given topic. In this context, opinions, sentiments and emotions expressed in Social Media texts have been shown to have a high influence on the social and economic behaviour worldwide. The aim of the 4th Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis (WASSA 2013) was to continue the line of the previous three editions, bringing together researchers in Computational Linguistics working on Subjectivity and Sentiment Analysis and researchers working on interdisciplinary aspects of affect computation from text. Additionally, this year, we extended the focus to Social Media phenomena and the impact of affect-related phenomena in this context. WASSA 2013 was organized in conjunction to the 2013 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, on June 14, 2013, in Atlanta, Georgia, United States of America. At this fourth edition of the workshop, we received a total of 29 submissions, from a wide range of countries, of which 8 were accepted as long and another 8 as short papers. Each paper has been thoroughly reviewed by 2 members of the Program Committee. The accepted papers were all highly assessed by the reviewers, the best paper receiving an average punctuation (computed as an average of all criteria used to assess the papers) of 4.75 out of 5. The main topics of the accepted papers are related to affect in Social Media - the creation and evaluation of resources for subjectivity, sentiment and emotion in social media, cross-lingual and multilingual resource creation and use, the detection of sarcasm and spam and the detection of illegal activities in digital social settings. The invited talks reflected the multimodal and interdisciplinary nature of the research in affect-related phenomena, from topics related to multimodal methods for emotion detection, theories of emotion and applications of emotion detection in Social Media. This year’s edition has shown again that the topics addressed by WASSA are of high interest to the research community and that the contributions presented in this forum bring an important development both to the theoretical, as well as to the application-oriented scenarios.JRC.G.2-Global security and crisis managemen

An Introduction to the Europe Media Monitor Family of Applications

Most large organizations have dedicated departments that monitor the media to keep up-to-date with relevant developments and to keep an eye on how they are represented in the news. Part of this media monitoring work can be automated. In the European Union with its 23 official languages, it is particularly important to cover media reports in many languages, to capture the complementary news content found across the different languages, and to be able to access this multilingual information across languages. We present here the four publicly accessible systems of the Europe Media Monitor (EMM) family of applications (see http://press.jrc.it/overview.html), which cover between 19 and 53 languages. We give an overview of their functionality and discuss some of the implications of the fact that they cover quite so many languages. We discuss design issues necessary to be able to achieve this high multilinguality, and the benefits there are to monitoring so many languages.JRC.G.2-Global security and crisis managemen

Thematic Indicators Derived from World News Report

A method for deriving statistical indicators from the Europe Media Monitor (EMM) is described. EMM monitors world news in real time from the Internet and various News Agencies. The new method measures the intensity of news reporting for any country concerning a particular theme. Two normalised indi-cators are defined for each theme (j) and for each country (c). The first (Icj ) is a measure of the relative importance for a given theme to that country. The sec-ond (Ijc )is a measure of the relative importance placed on that country with re-spect to the given theme by the world’s media. The method has then been ap-plied to news articles processed by EMM for each day during August 2003. This month was characterized by a number of serious terrorist bomb attacks visible both in the EMM data and in the derived indicators. The calculated indi-cators for a selection of 14 countries are presented. Their interpretation and pos-sible biases in the data are discussed. The data are then applied to identify can-didate countries for “forgotten conflicts”. These are countries with high levels of conflict but poorly reported in the world’s media.JRC.G.2-Support to external securit