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Transformation of the largest Russian companies’ business vocabulary in annual reports: Data Mining

Author

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  • Pavel A. Mikhnenko

    (Bauman University, Moscow, Russia)

Abstract

One of the promising areas of business analysis is the development of new methods and tools for accounting of nonfinancial and non-numeric information. There is a significant number of theoretical and practical solutions in this field; however, the issues of the transformation dynamics of companies’ business vocabulary need to be studied more extensively. The article aims to identify and interpret latent information reflecting strategic guidelines and conditions for the economic development of Russian enterprises. The methodology of the study is based on the concepts of narrative economics and multimodal business analytics, which is a system of scientific-practical methods for analyzing the activities of economic entities through the use of data from heterogeneous sources. The Data Mining methods and tools for analyzing and systematizing large volumes of textual information were used. The data for research were retrieved from the annual reports of the largest Russian companies for 2018–2020. Among the main indicators of the business vocabulary transformation considered in the paper are the occurrence of unique key tokens (UKTs) and the dynamics of its change, as well as the main contexts of UKTs relevant to the problem of development. The findings indicate noticeable changes in the vocabulary of Russian companies’ annual reports, such as a decline in covering formal aspects of economic activity and a growing debate on the development in the presence of risk. It is shown that these trends were most clearly manifested in the reports of metallurgical and energy enterprises. The research results can serve as a basis for enhancing the analytical and predictive effectiveness of modern business analysis.

Suggested Citation

  • Pavel A. Mikhnenko, 2022. "Transformation of the largest Russian companies’ business vocabulary in annual reports: Data Mining," Upravlenets, Ural State University of Economics, vol. 13(5), pages 17-33, November.
    • Handle: RePEc:url:upravl:v:13:y:2022:i:5:p:17-33
    DOI: 10.29141/2218-5003-2022-13-5-2
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    References listed on IDEAS

    as
    1. Evgeniya V. Nekhoda & Nurali U. Arabov & Aleksandr L. Bogdanov & Maria V. German & Tatyana V. Kuklina, 2022. "Decent work in non-financial reporting of Russian companies: Assessing the disclosure quality," Upravlenets, Ural State University of Economics, vol. 13(2), pages 34-56, May.
    2. Enrique Bonson & David Perea & Graca Azevedo, 2021. "Tone and content analysis in the president’s letters to shareholders: Spanish evidence," Upravlenets, Ural State University of Economics, vol. 12(1), pages 78-90, March.
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    More about this item

    Keywords

    Data Mining; text analysis; Russian companies; annual report; token; business vocabulary.;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • O2 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy

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