The Impact of Disclosure Sentiment on the Share Prices of Russian Companies
Information about companies published in a news feed is invariably tinted by emotional tonality. As such, resulting
perceptions may influence the opinion of market players, and consequently affect the dynamics of a company’s share
price. This study aims to evaluate various hypotheses about the impact of the tone of news items regarding dividends,
capital expenditures, and development on the stock prices of Russian companies. Information disclosure is extensively
studied, and there have been limited studies on the effect of disclosures on Russian companies. However, until now, there
have been no research studies which verify hypotheses on the influence of news sentiment on corporate share prices in
the Russian market.
This analysis was conducted using data from 49 Russian public companies included in the Moscow exchange index
over the period from the end of 2017 to the beginning of 2019. To account for the proximate impact of news items on
consequential market phenomena, an event study methodology was applied in order to estimate and construct the
models of dependency of cumulative abnormal return (CAR) on news tone level, and control for financial and nonfinancial
Our results provide evidence for the positive impact of the tone of news texts on the share prices of Russian companies.
The increase in news tone by one standard deviation leads to a cumulative abnormal stock return increase of 0.26
percentage points. This result is consistent with previous research conducted on data from developed stock markets.
Moreover, the relationship between the tone or sentiment level of a news item and the stock price reaction is linear,
without the diminishing marginal effect.
Our conclusions should prompt companies to invest effort in delivering information in a tonally positive way,
highlighting the most positive news. Investors, in turn, should rationally approach the interpretation of published
Current article is dedicated to the relationship between effectiveness of usage of intellectual capital and capital structure of firms in Russia in 2005-2007. Current research showed that effectiveness of usage of intellectual capital of firms has a positive influence over the level of financial leverage. The result of the research has showed that the more effective usage of intellectual capital makes a company more attractive for the credit organizations and opens more sources to obtain financing. There were also revealed some specific features of relationship between the effectiveness of utilization of intellectual capital and corporate financial decisions in Russia. The result is consistent with the results from the similar researches from the developed markets.
An article represents a comprehensive overview of approaches to capital structure modeling on the example of the public corporation Silvinit. At first, there are provided a short review of the company and of the corresponding industry followed by the description of how the analogues for the company were chosen. The next part of the article gives a step-by-step description of the practical implementation of such models as WACC model, EBIT-EPS, method of operational profit. Monte-Carlo approach is used for demonstrating an influence of the leverage increase on tax and interest payments as well as company's default risk. In conclusion the authors compare the results of different approaches with the current capital structure of Silvinit.
EBES is a scholarly association for scholars involved in the practice and study of economics, finance, and business worldwide. EBES was founded in 2008 with the purpose of not only promoting academic research in the field of business and economics, but also encouraging the intellectual development of scholars. Conference were with the support of Istanbul Economic Research Association. We are honored to have received top-tier papers from distinguished scholars from all over the world. In the conference, 323 papers will be presented and 569 colleagues from 56 countries will attend the conference.
The Semantic Evaluation (SemEval) series of workshops focuses on the evaluation and comparison of systems that can analyse diverse semantic phenomena in text with the aim of extending the current state of the art in semantic analysis and creating high quality annotated datasets in a range of increasingly challenging problems in natural language semantics. SemEval provides an exciting forum for researchers to propose challenging research problems in semantics and to build systems/techniques to address such research problems. SemEval-2016 is the tenth workshop in the series of International Workshops on Semantic Evaluation Exercises. The first three workshops, SensEval-1 (1998), SensEval-2 (2001), and SensEval-3 (2004), focused on word sense disambiguation, each time growing in the number of languages offered, in the number of tasks, and also in the number of participating teams. In 2007, the workshop was renamed to SemEval, and the subsequent SemEval workshops evolved to include semantic analysis tasks beyond word sense disambiguation. In 2012, SemEval turned into a yearly event. It currently runs every year, but on a two-year cycle, i.e., the tasks for SemEval-2016 were proposed in 2015. SemEval-2016 was co-located with the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT’2016) in San Diego, California. It included the following 14 shared tasks organized in five tracks: • Text Similarity and Question Answering Track – Task 1: Semantic Textual Similarity: A Unified Framework for Semantic Processing and Evaluation – Task 2: Interpretable Semantic Textual Similarity – Task 3: Community Question Answering • Sentiment Analysis Track – Task 4: Sentiment Analysis in Twitter – Task 5: Aspect-Based Sentiment Analysis – Task 6: Detecting Stance in Tweets – Task 7: Determining Sentiment Intensity of English and Arabic Phrases • Semantic Parsing Track – Task 8: Meaning Representation Parsing – Task 9: Chinese Semantic Dependency Parsing • Semantic Analysis Track – Task 10: Detecting Minimal Semantic Units and their Meanings – Task 11: Complex Word Identification – Task 12: Clinical TempEval iii • Semantic Taxonomy Track – Task 13: TExEval-2 – Taxonomy Extraction – Task 14: Semantic Taxonomy Enrichment This volume contains both Task Description papers that describe each of the above tasks and System Description papers that describe the systems that participated in the above tasks. A total of 14 task description papers and 198 system description papers are included in this volume. We are grateful to all task organisers as well as the large number of participants whose enthusiastic participation has made SemEval once again a successful event. We are thankful to the task organisers who also served as area chairs, and to task organisers and participants who reviewed paper submissions. These proceedings have greatly benefited from their detailed and thoughtful feedback. We also thank the NAACL 2016 conference organizers for their support. Finally, we most gratefully acknowledge the support of our sponsor, the ACL Special Interest Group on the Lexicon (SIGLEX). The SemEval-2016 organizers, Steven Bethard, Daniel Cer, Marine Carpuat, David Jurgens, Preslav Nakov and Torsten Zesch
In this paper, we consider opinion word extraction, one of the key problems in sentiment analysis. Sentiment analysis (or opinion mining) is an important research area within computational linguistics. Opinion words, which form an opinion lexicon, describe the attitude of the author towards certain opinion targets, i.e., entities and their attributes on which opinions have been expressed. Hence, the availability of a representative opinion lexicon can facilitate the extraction of opinions from texts. For this reason, opinion word mining is one of the key issues in sentiment analysis. We designed and implemented several methods for extracting opinion words. We evaluated these approaches by testing how well the resulting opinion lexicons help improve the accuracy of methods for determining the polarity of the reviews if the extracted opinion words are used as features. We used several machine learning methods: SVM, Logistic Regression, Naive Bayes, and KNN. By using the extracted opinion words as features we were able to improve over the baselines in some cases. Our experiments showed that, although opinion words are useful for polarity detection, they are not su fficient on their own and should be used only in combination with other features.
The paper examines the structure, governance, and balance sheets of state-controlled banks in Russia, which accounted for over 55 percent of the total assets in the country's banking system in early 2012. The author offers a credible estimate of the size of the country's state banking sector by including banks that are indirectly owned by public organizations. Contrary to some predictions based on the theoretical literature on economic transition, he explains the relatively high profitability and efficiency of Russian state-controlled banks by pointing to their competitive position in such functions as acquisition and disposal of assets on behalf of the government. Also suggested in the paper is a different way of looking at market concentration in Russia (by consolidating the market shares of core state-controlled banks), which produces a picture of a more concentrated market than officially reported. Lastly, one of the author's interesting conclusions is that China provides a better benchmark than the formerly centrally planned economies of Central and Eastern Europe by which to assess the viability of state ownership of banks in Russia and to evaluate the country's banking sector.
The paper examines the principles for the supervision of financial conglomerates proposed by BCBS in the consultative document published in December 2011. Moreover, the article proposes a number of suggestions worked out by the authors within the HSE research team.