Subject. The impact of digitalization on the market capitalization of the largest Russian public companies in 2022–2024 in the context of sanctions pressure. Objectives. Empirical analysis of the impact of digitalization on the market capitalization of the largest Russian public companies in 2022–2024 using machine learning and clustering methods, analysis based on SHAP modeling. Methods. The research methodology includes k-means cluster analysis based on three criteria (the effect of digitalization, market capitalization, and the share of import-substituted software) with the determination of the optimal number of clusters using the elbow method, the construction of an integral digitalization index based on min-max normalization of private indicators, and the evaluation of a machine learning model – a random forest – to identify market dependence. capitalization depends on digital and fundamental financial indicators. For a meaningful interpretation of nonlinear effects, the SHAP method is used, which makes it possible to estimate the relative contribution of each factor to the explanation of market valuation. The empirical base of the study includes data from 29 of the largest Russian public companies for 2022-2024 (87 observations "company – year"). Results. Based on the random forest model, the dependence of market capitalization on digital and fundamental financial indicators has been revealed. Digital indicators – the share of software import substitution, the absolute effect of digitalization, and R&D costs – play an important role in shaping market valuation, especially through non-linear threshold effects. Conclusions. The results obtained are interpreted in terms of the costs of the transition phase of digital transformation and the dual channel of digitalization's influence on market valuation – direct signaling through individual digital indicators (primarily software import substitution).
Keywords: digitalization, capitalization, cluster analysis, random forest, import substitution
References:
Bharadwaj A., El Sawy O.A., Pavlou P.A., Venkatraman N. Digital business strategy: toward a next generation of insights. MIS Quarterly, 2013, vol. 37, no. 2, pp. 471–482. DOI: 10.25300/MISQ/2013/37:2.3
Vial G. Understanding digital transformation: a review and a research agenda. Journal of Strategic Information Systems, 2019, vol. 28, no. 2, pp. 118–144. DOI: 10.1016/j.jsis.2019.01.003
Wu Fei, Hu Huizhi, Lin Huiyan, Ren Xiaoyi. Enterprise digital transformation and capital market performance: empirical evidence from stock liquidity. Management World, 2021, vol. 37, no. 7, pp. 130–144. DOI: 10.19744/j.cnki.11-1235/f.2021.0097
Jardak M.K., Ben Hamad S. The effect of digital transformation on firm performance: evidence from Swedish listed companies. Journal of Risk Finance, 2022, vol. 23, no. 4, pp. 329–348. DOI: 10.1108/JRF-12-2021-0199 EDN: COLWHE
Zhai Huayun, Yang Min, Chan Kam C. Does digital transformation enhance a firm's performance? Evidence from China. Technology in Society, 2022, vol. 68, no. 101841. DOI: 10.1016/j.techsoc.2021.101841 EDN: SEBCUN
Yang Xiuyun, Han Qi. Nonlinear effects of enterprise digital transformation on environmental, social and governance (ESG) performance: evidence from China. Sustainability Accounting, Management and Policy Journal, 2024, vol. 15, no. 2, pp. 355–381. DOI: 10.1108/SAMPJ-08-2023-0553 EDN: GYRBVE
Osipova R.G. [Digitalization as a competitive advantage of Russian organizations]. Vestnik Akademii znanii, 2020, no. 37(2), pp. 258–262. (In Russ.) DOI: 10.24411/2304-6139-2020-10174 EDN: XYBJTM
Sharafutdinov R.B., Saifullin R.I. [The impact of digitalization on increasing the competitiveness of the Russian oil and gas industry]. Molodoi uchenyi, 2019, no. 47, pp. 23–242. (In Russ.) EDN: SIOBNL
Baemova L.Z., Baemova K.S. [Digital transformation of Russian companies: trends and development prospects]. Vestnik UGNTU. Nauka, obrazovanie, ekonomika. Seriya: Ekonomika, 2024, no. 3, pp. 70–78. (In Russ.) DOI: 10.17122/2541-8904-2024-3-49-70-78 EDN: FWOUPA
Buevich A.P., Ivanova A.O., Fomin M.G. [Characteristics of the digital transformation of Russian companies]. Ekonomika, predprinimatel'stvo i pravo, 2025, vol. 15, no. 5, pp. 3081–3094. (In Russ.) DOI: 10.18334/epp.15.5.123006 EDN: GIRPHE
Barney J.B. Firm Resources and Sustained Competitive Advantage. Journal of Management, 1991, vol. 17, no. 1, pp. 99–120. DOI: 10.1177/014920639101700108 EDN: YBVKYV
Teece D.J. Explicating dynamic capabilities: the nature and microfoundations of (sustainable) enterprise performance. Strategic Management Journal, 2007, vol. 28, no. 13, pp. 1319–1350. DOI: 10.1002/smj.640
Connelly B.L., Certo S.T., Ireland R.D., Reutzel C.R. Signaling theory: a review and assessment. Journal of Management, 2011, vol. 37, no. 1, pp. 39–67. DOI: 10.1177/0149206310388419
Mikalef P., Pappas I.O., Krogstie J., Pavlou P.A. Big data and business analytics: a research agenda for realizing business value. Information and Management, 2020, vol. 57, no. 1. DOI: 10.1016/j.im.2019.103237 EDN: CJHUWI
Ma Li, Zhai Xin, Zhong Weiguo, Zhang Zhi-Xue. Deploying human capital for innovation: a study of multi-country manufacturing firms. International Journal of Production Economics, 2019, vol. 208, pp. 241–253. DOI: 10.1016/j.ijpe.2018.11.022
Spence M. Job market signaling. The Quarterly Journal of Economics, 1973, vol. 87, no. 3, pp. 355–374. DOI: 10.2307/1882010
Chen Wilbur, Srinivasan S. Going digital: implications for firm value and performance. Review of Accounting Studies, 2024, vol. 29, no. 2, pp. 1619–1665. DOI: 10.1007/s11142-023-09753-0 EDN: RKVBLY
Chen Pengyu, Hao Yuanyuan. Digital transformation and corporate environmental performance: the moderating role of board characteristics. Corporate Social Responsibility and Environmental Management, 2022, vol. 29, no. 5, pp. 1757–1767. DOI: 10.1002/csr.2324 EDN: RZOPCZ
Weritz P., Braojos J., Matute J., Benitez J. Impact of strategic capabilities on digital transformation success and firm performance: theory and empirical evidence. European Journal of Information Systems, 2024, vol. 34, no. 3, pp. 415–435. DOI: 10.1080/0960085X.2024.2311137
Samsuden N.S., Kohar U.H.A., Khatib S.F.A., Abbas A.F. Digital capabilities and business performance: a systematic literature review. Sustainability, 2024, vol. 16, no. 24. DOI: 10.3390/su162411108 EDN: HFOAFM
Akbary N.M.M., Trinugroho I., Risfandy T., Pamungkas P. Digital transformation and efficiency: evidence from Indonesian banks. International Journal of Business and Society, 2025, vol. 26, no. 1, pp. 135–150. DOI: 10.33736/ijbs.9555.2025 EDN: UGPPSL
Pierce J.R., Aguinis H. The too-much-of-a-good-thing effect in management. Journal of Management, 2013, vol. 39, no. 2, pp. 313–338. DOI: 10.1177/0149206311410060
Haans R.F.J., Pieters C., He Zi-Lin. Thinking about U: theorizing and testing U- and inverted U-shaped relationships in strategy research. Strategic Management Journal, 2016, vol. 37, no. 7, pp. 1177–1195. DOI: 10.1002/smj.2399
Erdey L., Liu Laisha, Nagy A. The nonlinear relationship between digital affordances and firm-level export performance: the moderating role of organizational ambidexterity. Managerial and Decision Economics, 2024, vol. 45, no. 4, pp. 1944–1964. DOI: 10.1002/mde.4117 EDN: ACRIWE
Wang Yanhe, Wei Wei, Liu Zhuodong et al. Interpretable machine learning framework for corporate financialization prediction: a SHAP-based analysis of high-dimensional data. Mathematics, 2025, vol. 13, no. 15. DOI: 10.3390/math13152526 EDN: WZZWHM
Cheng Yiran, Zhou Xiaorui, Li Yongjian. The effect of digital transformation on real economy enterprises' total factor productivity. International Review of Economics and Finance, 2023, vol. 85, pp. 488–501. DOI: 10.1016/j.iref.2023.02.007 EDN: JCQWLZ
Cherkasova V.A., Slepushenko G.A. [The impact of digitalization on the financial performance of Russian companies]. Finansy: teoriya i praktika, 2021, vol. 25, no. 2, pp. 128–142. (In Russ.) DOI: 10.26794/2587-5671-2021-25-2-128-142 EDN: DFHRIY
Katukov D., Smorodinskaya N. [Russia's technological sovereignty and leadership: strategic intentions and domestic constraints]. Obshchestvo i ekonomika, 2025, no. 12, pp. 38–59. (In Russ.) DOI: 10.7868/S3034543X25120032 EDN: NLAFST
Simola H. Made in Russia? Assessing Russia's potential for import substitution. BOFIT Policy Brief, 3/2022. URL: Link
Lee Han-Sol, Kazuhiro Kumo. The impact of sanctions on firms' financial performance in Russia. Eurasian Business Review, 2026, February. DOI: 10.1007/s40821-025-00337-3
Kiril'chuk S.P., Ablitarov E.R. [Digital core as the frontier of firm capitalization]. Vestnik Akademii znanii, 2025, no. 4, pp. 276–280. (In Russ.) EDN: GIDYVJ
Rousseeuw P.J. Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 1987, vol. 20, pp. 53–65. DOI: 10.1016/0377-0427(87)90125-7
Shapley L.S. A value for n-person games. In: Contributions to the Theory of Games. Vol. II. Princeton, Princeton University Press, 1953, pp. 307–317. DOI: 10.1515/9781400881970-018
Lundberg S.M., Erion G., Chen Hugh et al. From local explanations to global understanding with explainable AI for trees. Nature Machine Intelligence, 2020, vol. 2, no. 1, pp. 56–67. DOI: 10.48550/arXiv.1905.04610
Bussmann N., Giudici P., Tanda A., Yu Ellen Pei-Yi. Explainable machine learning to predict the cost of capital. Frontiers in Artificial Intelligence, 2025, vol. 8, no. 1578190. DOI: 10.3389/frai.2025.1578190 EDN: TKYAMS
Grinsztajn L., Oyallon E., Varoquaux G. Why do tree-based models still outperform deep learning on typical tabular data? Advances in Neural Information Processing Systems, 2022, vol. 35, pp. 507–520. DOI: 10.52202/068431-0037
