Template-type: ReDIF-Article 1.0
Author-Name: Valeria Zvereva
Author-Email: zverevava01@cbr.ru
Author-Workplace-Name: Bank of Russia; HSE University
Author-Name: Anna Krupkina
Author-Email: krupkinaas@cbr.ru
Author-Workplace-Name: Bank of Russia
Author-Name: Andrey Andreev
Author-Email: andreevav@cbr.ru
Author-Workplace-Name: Bank of Russia
Author-Name: Oleg Semiturkin
Author-Email: semiturkinon@cbr.ru
Author-Workplace-Name: Bank of Russia
Author-Name: Maria Kudaeva
Author-Email: kudaevams@cbr.ru
Author-Workplace-Name: Bank of Russia
Title: Identifying Turning Points in Bank of Russia Business Activity Indicators Using Machine Learning Methods
Abstract: This study develops a methodology for identifying threshold values of Bank of Russia business activity indicators to determine business cycle phases using machine learning classification models. The study relies on monthly monitoring of businesses data from the Bank of Russia for the period from January 2009 to September 2025. The greatest contribution to the model predictions comes from the following monitoring indicators: enterprises' assessments of actual demand for products, business climate indicators, and enterprise expectations regarding changes in production volumes over the next three months. Comparative accuracy analysis shows the systematic superiority of ensemble methods (e.g. bagging and various types of boosting) over parametric models. The obtained threshold values make it possible to formalise and strengthen the analytical basis for expert judgements on the current phase of the business cycle using Bank of Russia survey data.
Classification-JEL: C24, C45, C83, E32, E37 
Keywords: enterprise surveys, business cycle phases, machine learning, threshold identification, leading indicators
Journal: Russian Journal of Money and Finance
Pages: 3-36
Volume: 85
Issue: 2
Year: 2026
Month: June
DOI:
File-URL: https://rjmf.econs.online/upload/documents/RJMF-85-2-Identifying-Turning-Points-in-Business-Activity-Indicators.pdf
Handle: RePEc:bkr:journl:v:85:y:2026:i:2:p:3-36