Subject. This article analyzes industry stock indexes and news publications about sanctions. Objectives. The article aims to assess the impact of the tone of news publications about sanctions in official and unofficial sources of information on the return of industry stock indexes, taking into account the different levels of industry digitalization (digital and non-digital industries). Methods. For the study, I used the FinBERT model, HSE Digitalization Index, and the GARCH model(1,1). Results. The article confirms the statistical significance of the effect of news about sanctions on stock indexes of various industries. The hypothesis of a stronger influence in digital industries was not confirmed. The article finds a more significant impact of the news from official media compared to the unofficial ones. Conclusions and Relevance. The impact of sanctions news on industry stock indexes is observed in most of the industries considered, and it does not depend on the level of digitalization of the industry. Information from the official sources is of priority importance for stock market participants in comparison with the unofficial sources. The findings of the study can be relevant and useful to investors, stock market participants, analysts, and portfolio managers when making strategic decisions, including whether to buy or sell assets on the stock market.
Keywords: sanctions, news tone, text analysis, digitalization, stock market
References:
Kvint V.L., Babkin A.V., Shkarupeta E.V. [Strategizing the formation of a platform operating model to increase the level of digital maturity of industrial systems]. Ekonomika promyshlennosti, 2022, vol. 15, no. 3, pp. 249–261. (In Russ.) DOI: 10.17073/2072-1633-2022-3-249-261 EDN: CUHBYC
Kolmykova T.S., Kovalev P.P. [Digital business transformation in the context of a continuous improvement strategy]. Upravlencheskii uchet, 2022, no. 7-2, pp. 250–256. (In Russ.) DOI: 10.25806/uu7-22022250-256 EDN: JKGEUT
Kurbanova Z.S., Ismailova N.P. [Neural networks in the context of digitalization of education and science]. Mir nauki, kul'tury, obrazovaniya, 2023, no. 3, pp. 309–311. (In Russ.) DOI: 10.24412/1991-5497-2023-3100-309-311 EDN: KUDKEP
Yakovleva E.A., Vinogradov A.N., Aleksandrova L.V., Filimonov A.P. [The role of artificial intelligence technologies in the digital transformation of the economy]. Voprosy innovatsionnoi ekonomiki, 2023, vol. 13, no. 2, pp. 707–726. (In Russ.) DOI: 10.18334/vinec.13.2.117710 EDN: PKODZB
Kuznetsov V.O., Krasavina E.V., Sologub V.A., Zabaikin Yu.V. [The exploitation of digitalization tools to increase the capitalization of a company: using the example of the food industry]. Ekonomika: vchera, segodnya, zavtra, 2023, vol. 13, no. 10-1, pp. 246–256. (In Russ.) DOI: 10.34670/AR.2023.83.51.030 EDN: WBGNKB
Kurbangalieva D.L., Khairullina A.D. [A modern approach to quantifying the factors influencing an organization's capitalization]. Kazanskii ekonomicheskii vestnik, 2022, no. 3, pp. 49–55. (In Russ.) EDN: QOLBIC
Azieva R.Kh. [Investment attractiveness of Russian oil and gas companies in the context of digitalization of the economy]. Voprosy regional'noi ekonomiki, 2022, no. 3, pp. 3–11. (In Russ.) EDN: JEEIQV
Kapoguzov E.A., Chupin R.I. [Sanctions 2022: the possibilities and limitations of reactionary regulation by the Russian State]. Journal of Economic Regulation, 2022, vol. 13, no. 1, pp. 67–74. (In Russ.) DOI: 10.17835/2078-5429.2022.13.1.067-074 EDN: IKMMDK
Smorodinskaya N.V., Katukov D.D. [Russia under sanctions: the limits of adaptation]. Vestnik Instituta ekonomiki Rossiiskoi akademii nauk, 2022, no. 6, pp. 52–67. (In Russ.) DOI: 10.52180/2073-6487_2022_6_52_67 EDN: UJYCYG
Dolgov S.I., Savinov Yu.A., Kirillov V.N., Taranovskaya E.V. [The possibilities of countering sanctions in international trade]. Rossiiskii vneshneekonomicheskii vestnik, 2022, no. 4, pp. 36–54. (In Russ.) DOI: 10.24412/2072-8042-2022-4-36-54 EDN: NVTVTS
Chiang T.C. Economic policy uncertainty, risk and stock returns: Evidence from G7 stock markets. Finance Research Letters, 2019, vol. 29, pp. 41–49. DOI: 10.1016/j.frl.2019.03.018
Dzielinski M. Measuring economic uncertainty and its impact on the stock market. Finance Research Letters, 2012, vol. 9, iss. 3, pp. 167–175. DOI: 10.1016/j.frl.2011.10.003
Kundu S., Paul A. Effect of economic policy uncertainty on stock market return and volatility under heterogeneous market characteristics. International review of economics & finance, 2022, vol. 80, pp. 597–612. DOI: 10.1016/j.iref.2022.02.047 EDN: GUHEQD
Bekaert G., Hoerova M., Duca M.L. Risk, uncertainty and monetary policy. Journal of Monetary Economics, 2013, vol. 60, iss. 7, pp. 771–788. DOI: 10.1016/j.jmoneco.2013.06.003
Kahneman, D., Tversky, A. Prospect theory: An analysis of decision under risk. Econometrica, 1979, vol. 47, no. 2, pp. 263–291.
Lenchuk E.B. [Technological modernization as the basis of anti-sanctions policy]. Problemy prognozirovaniya, 2023, no. 4, pp. 54–66. (In Russ.) DOI: 10.47711/0868-6351-199-54-66 EDN: HDROZI
Dorzhieva V.V. [Digital Transformation of industry and industrial policy under external constraints]. Voprosy innovatsionnoi ekonomiki, 2023, vol. 13, no. 2, pp. 637–648. (In Russ.) DOI: 10.18334/vinec.13.2.117692 EDN: IEYDZJ
Izmailova M.A. [Implementation of ESG strategies of Russian companies in the context of sanctions restrictions]. MIR (Modernizatsiya. Innovatsii. Razvitie), 2022, vol. 13, no. 2, pp. 185–201. (In Russ.) DOI: 10.18184/2079-4665.2022.13.2.185-201 EDN: BHXXJT
Devlin J., Chang M.-W., Lee K., Toutanova K. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics, Human Language Technologies, 2019, vol. 1, pp. 4171–4186.
Fedorova E.A., Musienko S.O., Fedorov F.Yu. [Development of Russian Political Uncertainty Index (RPUI): Textual analysis]. Ekonomicheskaya nauka sovremennoi Rossii, 2019, no. 2, pp. 52–64. (In Russ.) DOI: 10.33293/1609-1442-2019-2(85)-52-64 EDN: PGOMGR