Usage of Media Sentiment Analysis for Forecasting Inflation

Student: Vaniev Konstantin

Supervisor: Ekaterina Artemova

Educational Programme: Economics (Bachelor)

Year of Graduation: 2019

The aim of this paper is to examine the possibility to improve accuracy of classic models designed for inflation forecasting with methods of natural language processing. Of particular interest is using semantic and topic indicators based on news articles. While there has been notable research on the relation between media publications and dynamics of macroeconomic variables in Western countries, this paper focuses on analyzing whether this relation holds for inflation in Russia. Including selected text-based variables in the time-series forecasting model improves nowcast 3% and one-month forecast 1.6% against the baseline model.

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