The Russian Journal of Money and Finance has released its first issue of 2025, focusing on modeling trust in the central bank using social media data, forecasting inflation with news texts, and analyzing factors impacting inflation risks.
Trust in the central bank plays a critical role in monetary policy efficiency. Anastasia Matevosova of Lomonosov Moscow State University and the Institute of Economics of the Russian Academy of Sciences highlights that “the higher the level of trust in the central bank, the stronger the effect of its monetary policy on inflation expectations.” Matevosova presents an innovative approach using big data by building an indicator based on the sentiment analysis of comments from the VK social network. This method, unlike traditional survey-based approaches, is less complex and more cost-effective. It can utilize data with various frequencies, such as weekly or annual. Findings suggest that a positive credibility shock, with a two-week delay, reduces inflation expectations in the short term.
Inflation prediction can also benefit from the use of textual analysis and neural networks. Elizaveta Volgina, also from Lomonosov Moscow State University, incorporates news from mass media along with standard macroeconomic variables like wage dynamics, production index, and crude prices. Volgina concludes that “forecasts taking into account news indices are more accurate.”
In implementing monetary policy, central banks must consider factors that might lead to higher-than-expected inflation. Alexandra Chudaeva from RANEPA asserts, “They include wage growth and a decline in production over a one-year horizon and an increase in retail turnover and a weaker ruble over a one-month horizon.”
The latest edition of the Russian Journal of Money and Finance is now available online.
