This paper performs topic modeling using all publicly available CSR (Corporate Social Responsibility) reports for all constituent firms of the major stock market indices of 15 industrialized countries included in MSCI Europe for the sample period from 1999 to 2016. Our text mining results and LDA analyses indicate that "employees safety", "employees training support", "carbon emission", "human right", "efficient power", and "healthcare medicines" are the common topics reported by publicly listed companies in Europe and the UK. There is a clear sector bias with industrial firms emphasizing "employee safety", Utilities concentrating on "efficient power" while consumer discretionary and consumer staples highlighting "food waste" and "food packaging." To produce these results, we used a battery of python code to organize the hundreds of reports downloaded from Bloomberg and the internet, the latest R-algorithm to estimate LDA (Latent Dirichlet Allocation) model and the LDAvis interactive tool to visualize and refine the LDA model.

The paper proposes a new approach to measure inflation expectations of the Russian population based on text mining of information on the Internet with the help of machine learning techniques. Two indicators were constructed on the base of readers’ comments to inflation news in major Russian economic media available in the web at the period from 2014 through 2016: with the help of words frequency and sentiment analysis of comments content. During the whole considered period of time both indicators were characterized by dynamics adequate to the development of macroeconomic situation and were also able to forecast dynamics of official Bank of Russia indicators of population inflation expectations for approximately one month in advance.

In this paper, based on a cross-country analysis, the authors distil different models of the financial sector, which are characterized by peculiar interrelations among size, structure, efficiency, stability, inclusion and the institutional quality of financial development. Against this backdrop, the model of the Russian financial sector is described. To identify the financial sector models, cluster analysis involving the EM algorithm with a Bayesian extension is performed on a vast sample of countries. The analysis allows setting key long-term indicators of the Russian financial sector development, taking into consideration its potential of transition to the cluster of more financially advanced economies.

The need to absorb windfalls gains and manage them appropriately has been discussed extensively by academics and policy makers alike. We explore the role of the financial sector in intermediating these windfalls. Controlling for the level of financial development, inflation, GDP growth and country fixed-effects, we find a relative decline in financial sector deposits in countries that experience an unexpected natural resource windfall as measured by shocks to exogenous world prices. Moreover, we find a similar relative decline in lending, which is mostly due to the decrease in deposits. The smaller role for the financial sector in intermediating resource booms is accompanied by a stronger role of governments in channeling resources into the economy, mostly through higher government consumption.