Measuring inflation expectations ofthe Russian population with the help of machine learning

Author:

Goloshchapova I.1,Andreev M.2

Affiliation:

1. X5 Retail Group; Lomonosov Moscow State University

2. Lomonosov Moscow State University

Abstract

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.

Publisher

NP Voprosy Ekonomiki

Subject

Economics and Econometrics,Finance

Reference42 articles.

1. Bank of Russia (2015). Bank of Russia annual report for 2015. (In Russian).

2. Vyugin V. V. (2013). Mathematical foundations of the theory of machine learning and forecasting. Мoscow: MCCME. (In ¬Russian).

3. Kreptsev D., Porshakov A., Seleznev S., Sinyakov A. (2016). Equilibrium interest rate: Estimates for Russia. Bank of Russia Working Paper Series, No. 13. (In Russian).

4. Rubtsova Yu. V. (2015). Constructing a corpus for sentiment classification training. Programmnie Produkty i Sistemy, No. 1, pp. 72—78. (In Russian).

5. Stolbov M. (2011). Statistics of search queries in Google as an indicator of financial conditions. Voprosy Ekonomiki, No. 11, pp. 79—93. (In Russian).

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