Sectoral Counter-Cyclical Approach to Financial Risk Management Based on CSR for Sustainable Development of Companies

Abstract

This research determines the contribution of Corporate Social Responsibility (CSR) to reducing financial risks and, consequently, to the sustainable development of companies in different sectors of the economy and at different phases of the economic cycle (using Russia as an example). The informational and empirical base comprises data on the dynamics of stock prices of sectoral indices of the Moscow Exchange’s total return “gross” (in Russian rubles): oil and gas, electricity, telecommunications, metals and mining, finance, consumer sector (retail trade), chemicals and petrochemicals, and transportation, as well as the “Responsibility and Openness” index in 2019 (before the crises), in 2020 (COVID-19 crisis), 2022 (sanction crisis), and 2024 (Russia’s economic growth). Economic–mathematical models, compiled through regression analysis, showed that the contribution of CSR to reducing the financial risks of companies is highly differentiated among economic sectors and phases of the economic cycle. The research presents a new sectoral perspective on counter-cyclical management of the financial risks of companies through CSR, enabling a deeper study of the cause-and-effect relationships of such management for the sustainable development of companies from different economic sectors. This is the theoretical significance of this research, its novelty, and its contribution to the literature. The research has practical significance, revealing previously unknown best practices for the sustainable development of companies from different economic sectors of Russia across different phases of the economic cycle. The systematized experience will be useful for forecasting the financial risks of companies during future economic crises in Russia and improving the practice of planning and organizing the financial risk management of Russian companies through CSR. The authors’ conclusions have managerial significance because they will help enhance the flexibility and efficiency of corporate financial risk management by considering the sectoral specifics and cyclical nature of the economy when implementing CSR.

1. Introduction

From the economic perspective, the sustainable development of companies is the stability of their functioning at all phases of the economic cycle, including in the conditions of economic crises. The exacerbation of risks is one of the main attributes of any crisis (Gupta et al. 2024). During an economic crisis, the activities of economic entities are largely determined by financial risks, which generally represent a broad spectrum of negative financial phenomena (Shaikh and Khan 2025). Small and medium-sized enterprises often receive the most support from the state, expressed through simplified tax systems that reduce the tax burden and simplify financial accounting, as well as through the provision of anti-crisis subsidies. This support helps mitigate the impact of financial risks on the activities of small and medium-sized businesses during economic crises.

In contrast, large businesses, especially companies with publicly traded shares, must independently manage increased financial risks during economic crises. For these companies, the primary manifestation of financial risk is the decline in stock value on the market—this definition of financial risk is used in the present scientific material (Azar-Ibrahim et al. 2025). The methods of managing financial risks for companies are quite diverse. Therefore, from a scientific standpoint, to organize these methods, it is appropriate to distinguish between two alternative approaches to managing the financial risks of companies.

The first approach involves cost reduction. The basic premise of this approach is the assumption that the potential for revenue generation and, consequently, the return on investment is limited during an economic crisis. Thus, without the ability to attract additional investments through stock issuance, companies initiate an anti-crisis corporate savings program to maintain profitability: saving on raw materials and supplies, human resources, innovations and technologies, etc.

Numerous practical applications of this approach have led to significant scientific criticism, pointing out that cost reduction generates a short-term effect, helping the company survive the crisis but limiting its post-crisis recovery potential due to the loss of top talent, diminished competitive advantages from abandoning innovations, and loss of stakeholder trust due to the rollback of environmental initiatives. Recent experiences from the latest international economic crises have provided convincing evidence that the second approach to managing companies’ financial risks—through corporate social responsibility (CSR)—is more promising.

This modern approach is based on the opposite assumption that during an economic crisis, the potential for growth and any income is determined by the loyalty of stakeholders to the company’s activities. Therefore, to reduce financial risks, companies need to manage stakeholder loyalty, enhancing and strengthening it. CSR plays a central role in this process as a highly effective method of managing loyalty and, consequently, financial risk management for companies (Cardillo and Basso 2025). The advantage of CSR is that it makes it possible to reduce financial risks in both phases of the economic cycle—during the crisis phase and the growth phase (Ali et al. 2024).

The problem lies in the uncertainty regarding the differences in CSR’s contribution to reducing financial risks of companies in different sectors of the economy at different phases of the economic cycle. Without a clear understanding of the magnitude of CSR’s contribution, it is impossible to build highly effective corporate financial risk management strategies. Addressing this problem is crucial because it hinders the sustainable development of companies. This has defined the goal of the present article, which is to determine the contribution of CSR to reducing financial risks and, consequently, to sustainable development in different sectors of the economy and at different phases of the economic cycle (using Russia as an example).

The novelty of this research lies in a new sectoral perspective on the counter-cyclical management of financial risks of companies through CSR, which reveals previously unknown features of sustainable development of companies from various economic sectors at different phases of the economic cycle. Russia is the first country to overcome challenges from Western sanctions, which makes its experience especially useful for research (Duong et al. 2024).

In the research part of this paper, to determine the influence of CSR on the financial risks of companies by the sectors of the Russian economy, we perform regression modeling of the significance of values of sectoral indices on the Moscow Exchange index—MOEX Index (2024g) “Responsibility and openness”: MRRT. The paper uses the authors’ definition of CSR as the volume of companies’ expenditures for responsible business, which is measured from the position of the MRRT profitability index.

The contribution of CSR to reducing financial risks and its different values in four economic and time periods are revealed: (1) 2019 (before the crisis phenomena during the upward wave of the economic cycle in Russia); (2) 2020 (the COVID-19 crisis); (3) 2022 (the sanctions crisis); (4) 2024 (the growth of the Russian economy).

To take into account the influence of international economic sanctions as a factor of financial risk to business activity in modern economic conditions, we compiled a forecast of the CSR of Russian companies (MRRT index) for 2025 and compiled alternative scenarios of changes in financial risks by the sectors of the Russian economy for 2025.

2. Literature Review

2.1. Financial Risks as a Challenge for the Sustainable Development of Companies

This research focuses on the financial risks of companies (large businesses with publicly traded shares), understood as the reduction in market value of these companies’ stocks, based on the works of Hajek and Munk (2024) and Wang et al. (2024). The subject of this research is the management of financial risks of companies, defined as the process of implementing managerial measures aimed at reducing financial risks (Sajjad et al. 2024; Zarova and Tursunov 2022).

This research adopts a two-phase model of the economic cycle, comprising a growth (expansion) phase and a crisis (recession) phase. According to Gibbens et al. (2024) and Rogachev et al. (2018), the sustainable development of companies is understood as the successful management of their financial risks, ensuring stability or growth in their stock market valuations.

A literature review conducted by Fan and Gao (2024) and Yang (2024) indicates that the return on CSR in the form of reduced financial risks is specific to each country. Therefore, to obtain the most accurate and detailed results explaining the cause-and-effect relationships between companies’ CSR and their financial risks, this research is conducted using the example of Russia.

Russia’s experience is notable because Russian companies have faced two significant crises in recent years: the COVID-19 crisis in 2020 and the sanctions crisis in 2022. Every crisis is accompanied by a sharp increase in financial risks. Russian companies demonstrated high resilience to both crises, as evidenced by their transition to economic growth in 2021 and in 2023–2024. This makes the experience of Russian companies in managing financial risks valuable for companies in other countries.

2.2. Managing the Financial Risks of Companies Through CSR

The theoretical foundation of this research is the scientific concept of managing the financial risks of companies through CSR (Karbekova et al. 2023). The economic essence of managing financial risks through CSR lies in the fact that CSR increases stakeholder loyalty towards companies and their activities (Kitsai et al. 2023; Samieva et al. 2023).

The conceptual basis of this research consists of the works of such scholars as Doho et al. (2023), Kamath et al. (2024), Kumaran (2023), and Lekha Shree and Kanniammal (2023), in which sectorial stock indices, which unify the values of market indicators of the companies from general sectors, are used to study the economy of sectors and to compare the sectors.

Thanks to higher loyalty, companies can increase sales volumes even during an economic crisis, maintain sales volumes while raising prices due to CSR efforts, and attract additional investments for development through stock market issuance. In each industry, the nature of CSR as a method of managing financial risks is specific:

• In the oil and gas industry, managing financial risks through CSR involves organizing oil well drilling and gas field development with a high level of safety for workers’ health and the environment (Alekseev et al. 2019);
• In the electric power industry, managing financial risks through CSR involves developing clean energy to support the decarbonization of the economy (Velinov et al. 2020);
• In the telecommunications industry, managing financial risks through CSR involves creating knowledge-intensive jobs for digital professionals and developing telecommunications infrastructure to reduce the digital divide, for example, by providing low-income consumers with affordable access to telecommunications (Arkin et al. 2020);
• In the metals and mining industry, managing financial risks through CSR involves organizing environmentally friendly extraction processes and preserving natural resources (metal ores) for future generations (Karlibaeva and Nyangarika 2023);
• In the financial sector, managing financial risks through CSR involves developing climate finance, enhancing cybersecurity and the reliability of financial services, and maintaining jobs despite the automation of financial services (Kantor et al. 2023);
• In the consumer sector (retail trade), managing financial risks through CSR involves strengthening quality guarantees for retail products, increasing their availability and convenience of purchase through the development of e-commerce (Abdurakhmanova et al. 2021; Turginbayeva et al. 2020);
• In the chemical and petrochemical industry, managing financial risks through CSR involves organizing remote employment for workers in hazardous industries, installing more advanced treatment facilities in chemical plants, and reducing production and consumption waste of chemical products (Ye and Dela 2023);
• In the transport industry, managing financial risks through CSR involves reducing the cost of transport and logistics services, improving the quality and convenience of these services, and making them accessible even to residents of previously hard-to-reach areas, such as rural locations, thereby developing transport infrastructure (Sultanova and Babakhanova 2023).

Certain issues of counter-cyclical management of companies, in particular, to raise their resilience to economic crises through financial risk management, were studied in the works of Safón and Iborra (2023), Sandoval et al. (2022), Thiemann (2022), and Wang et al. (2023). The cited literature lacks scientific evidence of differences in CSR’s contribution to reducing companies’ financial risks across different phases of the economic cycle. Therefore, in the existing literature, managing companies’ financial risks for the sustainable development of economic sectors through CSR is not considered to be tied to the economic cycle.

2.3. The State of CSR in Russia

In the business environment of modern Russia, significant attention is paid to CSR, according to the progressive global trends and based on the leading experience of the world market leaders. This is manifested especially clearly among large companies, which stock is traded in the stock exchange and which activities are performed in the national Russian and international economic space, due to which many of these companies are transnational. It should be noted that production departments of Russian companies are located in the territory of Russia.

The high level of Russian companies’ involvement with the leading global initiatives in the sphere of manifestation of CSR is demonstrated by the fact that most of these companies officially state—on their websites—their adherence to CSR and sustainable development. Russian companies annually publish (voluntarily and widely) on their official websites the corporate reporting on CSR and sustainable development, including detailed information on the implementation of each of the 17 Sustainable Development Goals of the UN (Gyiazov et al. 2023).

A specific feature of the CSR practice in the business environment of modern Russia is that measures that are implemented by Russian companies involve the manifestation of CSR for the economic system of Russia. This significantly differentiates the Russian practice of CSR from a range of foreign, including developed, countries, in the business environment of which a widespread aspect is companies’ manifesting CSR not only for the economic systems of the countries of their main offices’ location but also countries of the location of their production branches (these are usually developing countries (Fedchenko et al. 2024).

Due to this, CSR in international practice generates a global effect, which is manifested indirectly and remotely in the economic systems of countries of the location of main offices of the companies that manifest CSR. In Russian practice, CSR ensures the national (at the scale of the country) and local (at the scale of region, city, or village, i.e., an individual territory) effect (Karpova and Pogodina 2024).

A widespread aspect in the Russian business environment is the implementation of measures, and publication of plans and reports on CSR and sustainable development not only for the company on the whole with the provision of generalized and aggregated data and conclusions but also for each production department of the company with detailed and explicit data with the disclosure of the contribution of CSR to improvement of the quality of life, economic development, and improvement of the state of the environment of the territory in which production is carried out (Galkin 2024). The measures of manifestation of CSR that are most widespread in the business environment of modern Russia are as follows:

• Social measures, which include the creation of additional jobs, raising wages compared to average wages in the region, corporate training, and additional life and health insurance for employees (Ryazantsev et al. 2024);
• Economic measures, which include investments in the development of local infrastructure, digital modernisation, import substitution, preservation of price stability in the conditions of inflation, and tax payments in local budgets (Batashova et al. 2024);
• Environmental measures, which include the creation of green jobs, placement of investments in the greening of territories, reduction in natural resources spending, energy saving, reduction in production waste, start of the circular use of natural resources, transition to environmentally friendly (biodegradable) packaging, and environmentally safe transportation of products (Vitsko et al. 2024).

An overview of CSR practices, implemented in Russia’s business environment, and reports on CSR and sustainable development, published by Russian companies, showed that these practices are strongly differentiated not only among sectors but within any general sector. Therefore, Russian companies’ expenditures for CSR are a universal means of measuring and comparing CSR practices in Russia’s business environment (Kuksov 2022).

The most reliable indicator of the scale of these expenditures within each sector of the Russian economy is the corresponding index of profitability. In particular, this is the index of the Moscow Exchange—MOEX Index (2024g) “Responsibility and openness”: MRRT. Based on this, we formulated the authors’ definition of CSR as the volume of companies’ expenditures for responsible business in support of an increase in the quality of life, economic development, and improvement of the state of the environment of the territory of the company’s location, measured from the positions of the MRRT profitability index.

The advantage of the authors’ definition is that it allows—with high precision—measuring the level of CSR of companies in various sectors of the Russian economy and using financial measuring of CSR, which takes it closer to financial risks companies which can also be measured in each sector of the Russian economy with the help of the corresponding sectoral profitability indices of companies. The authors’ definition allows for the systemic study of how Russian companies manage financial risks and practices of CSR with the help of profitability indices of companies.

3. Gap Analysis

The conducted content analysis of the published scientific literature on the considered topic shows that this research problem is sufficiently studied. Numerous scientific works extensively reveal the theory and practice of managing the financial risks of companies through CSR. However, the specifics of managing financial risks through CSR in different economic sectors remain unknown. This represents a gap in the literature and raises the following research question:

RQ: “What is the contribution of CSR to reducing financial risks in various economic sectors at different phases of the economic cycle?”.

Based on available scientific publications that disclose the specifics of managing companies’ financial risks through CSR in different economic sectors, particularly in Ai et al. (2024), Gao (2024), and Raimi et al. (2024), this research proposes the hypothesis H, which states that the contribution of CSR to reducing companies’ financial risks is highly differentiated among economic sectors and the phases of the economic cycle. To test the proposed hypothesis, the research examines the experience of managing financial risks of Russian companies through CSR in different economic sectors and at various phases of the economic cycle within its two-phase model.

4. Results

To determine the impact of CSR on the financial risks of companies by industries in Russia, the authors conducted a regression analysis of the data in Table A2. The obtained results are reflected in Table 1, Table 2, Table 3 and Table 4, where multiple R shows the determination of each dependent variable with independent variable MRRT; significance F reflects the level of significance at which the results of the regression analysis are reliable and correct; F-observed shows the factual value of F during the F-test on the reliability of the results of the regression analysis; coefficients are coefficients of regression; standard error shows errors of the regression models; and t-State shows factual value of t during the t-test on the reliability of the results of the regression analysis.

Table 1. Results of the regression analysis in 2019.

Indicators		MEOGTR	MEEUTR	METLTR	MEMMTR	MEFNTR	MECNTR	MECHTR	METNTR
Multiple R		0.9680	0.9381	0.9333	0.9012	0.9679	0.7499	0.8237	0.7483
Significance F		2.9436 × 10−147	2.0248 × 10−113	1.2 × 10−109	7.69 × 10−90	4.2577 × 10−147	2.57207 × 10−45	1.43 × 10−61	4.9 × 10−45
F-observed		3597.4376	1775.1138	1635.6196	1046.2501	3585.7469	310.9457	510.7959	307.9999
Coefficients	a	−3841.9588	−266.7926	−696.4171	3149.8212	−290.8516	4524.4384	6999.4721	680.8269
	MRRT	6.4151	1.2286	1.7241	2.9289	3.8183	1.2179	6.3355	0.6096
Standard error	a	232.9030	63.4995	92.8304	197.1730	138.8483	150.3902	610.4136	75.6406
	MRRT	0.1070	0.0292	0.0426	0.0905	0.0638	0.0691	0.2803	0.0347
t-Stat	a	−16.4960	−4.2015	−7.5020	15.9749	−2.0947	30.0847	11.4668	9.0008
	MRRT	59.9786	42.1321	40.4428	32.3458	59.8811	17.6337	22.6008	17.5499

Source: calculated and compiled by the authors.

Table 2. Results of regression analysis for 2020.

Indicator		MEOGTR	MEEUTR	METLTR	MEMMTR	MEFNTR	MECNTR	MECHTR	METNTR
Multiple R		0.9321	0.7068	0.4222	0.3264	0.6716	0.3758	0.2206	0.7560
Significance F		2.43 × 10−111	3.72 × 10−39	3.16 × 10−12	1.29 × 10−7	3.82 × 10−34	8.30 × 10−10	0.0004	1.535 × 10−47
Observed F		1641.2982	247.5840	53.7879	29.5658	203.7253	40.7892	12.6889	330.7649
Coefficients	a	−1516.2993	824.0550	2455.8347	5926.6541	−1669.9188	2272.5401	20,976.3526	−223.1091
	MRRT	5.1207	0.9651	0.7104	3.1771	4.8979	2.8852	1.8942	0.9465
Standard error	a	284.5904	138.1025	218.1014	1315.5832	772.6401	1017.1570	1197.2674	117.1797
	MRRT	0.1264	0.0613	0.0969	0.5843	0.3432	0.4518	0.5317	0.0520
t-statistic	a	−5.3280	5.9670	11.2601	4.5050	−2.1613	2.2342	17.5202	−1.9040
	MRRT	40.5129	15.7348	7.3340	5.4374	14.2732	6.3866	3.5622	18.1869

Source: calculated and compiled by the authors.

Table 3. Results of regression analysis for 2022.

Indicator		MEOGTR	MEEUTR	METLTR	MEMMTR	MEFNTR	MECNTR	MECHTR	METNTR
Multiple R		0.9587	0.8494	0.5053	0.9347	0.9390	0.8561	0.4652	0.9805
Significance F		1.32 × 10−127	8.86 × 10−66	1.95 × 10−16	2.76 × 10−105	1.27 × 10−108	7.01 × 10−68	7.33 × 10−14	1.75 × 10−164
Observed F		2614.7542	595.8221	78.8420	1589.9516	1715.6225	631.2587	63.5303	5721.6725
Coefficients	a	3114.8040	1215.9815	2722.9867	−3275.7411	−3397.8040	2790.5066	66,668.0524	−4.3705
	MRRT	3.5571	0.5403	0.4270	8.0743	6.3290	2.7764	−4.9891	0.6676
Standard error	a	141.7648	45.1125	97.9909	412.6654	311.3934	225.1941	1275.6025	17.9869
	MRRT	0.0696	0.0221	0.0481	0.2025	0.1528	0.1105	0.6259	0.0088
t-statistics	a	21.9716	26.9544	27.7881	−7.9380	−10.9116	12.3916	52.2640	−0.2430
	MRRT	51.1347	24.4095	8.8793	39.8742	41.4201	25.1249	−7.9706	75.6417

Source: calculated and compiled by the authors.

Table 4. Results of regression analysis for 2024.

Indicator		MEOGTR	MEEUTR	METLTR	MEMMTR	MEFNTR	MECNTR	MECHTR	METNTR
Multiple R		0.9177	0.7336	0.8297	0.8402	0.6030	0.9087	0.3512	0.6789
Significance F		3.81 × 10−53	3.23 × 10−23	3.29 × 10−34	8.01 × 10−36	3.17 × 10−14	2.06 × 10−50	4.18 × 10−5	6.88 × 10−19
Observed F		682.6172	149.1277	282.7742	307.2545	73.1209	606.8537	18.0121	109.4414
Coefficients	a	1965.5639	1260.5276	−1999.6414	−333.2809	9560.7505	−2343.7590	41,293.7937	822.8065
	MRRT	6.1255	0.8604	2.9469	6.3961	2.4703	6.2593	13.0524	0.8623
Standard error	a	588.4781	176.8382	439.8689	915.8853	725.1085	637.7685	7719.4580	206.8878
	MRRT	0.2345	0.0705	0.1752	0.3649	0.2889	0.2541	3.0755	0.0824
t-statistic	a	3.3401	7.1281	−4.5460	−0.3639	13.1853	−3.6749	5.3493	3.9771
	MRRT	26.1269	12.2118	16.8159	17.5287	8.5511	24.6344	4.2441	10.4614

Source: calculated and compiled by the authors.

Results of the analysis from Table 1 show that before the crisis phenomena at the upward wave of the economic cycle in Russia, financial risks of companies were closely and strongly with CSR in all considered sectors of the Russian economy, including oil and gas (correlation: 96.80%), finance (96.79%), electric energy (93.81%), telecommunications (93.33%), metals and mining (90.12%), consumer sector (74.99%), chemical and petrochemicals (82.37%), and transport (74.83%).

The arithmetic mean of the correlation coefficients equals 87.88%, which is a sign of a very close connection between the financial risks of Russian companies and their CSR in 2019, before the crisis phenomena during the upward wave of the economic cycle in Russia. The following regression model (system of regression equations) of the dependence of sectorial indices on CSR in 2019, i.e., before the crisis phenomena and during the upward wave of the economic cycle in Russia, was obtained.

$$\left\{\begin{array}{l}\mathrm{M}\mathrm{E}\mathrm{O}\mathrm{G}\mathrm{T}\mathrm{R}=-3841.9588+6.4151\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{E}\mathrm{U}\mathrm{T}\mathrm{R}=-266.7926+1.2286\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{T}\mathrm{L}\mathrm{T}\mathrm{R}=-696.4171+1.7241\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{M}\mathrm{M}\mathrm{T}\mathrm{R}=3149.8212+2.9289\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{F}\mathrm{N}\mathrm{T}\mathrm{R}=-2908.516+3.8183\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{C}\mathrm{N}\mathrm{T}\mathrm{R}=4524.4384+1.2179\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{C}\mathrm{H}\mathrm{T}\mathrm{R}=6999.4721+6.3355\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{T}\mathrm{N}\mathrm{T}\mathrm{R}=680.8269+0.6096\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T}.\end{array}\right.$$

(1)

Model (1) shows that growth of expenditures for CSR by RUB 1 in the conditions of the COVID-19 crisis in 2020 lead to a decrease in financial risks of Russian companies in the sphere of oil and gas of RUB 6.4151, in the sphere of electric energy—RUB 1.2286, in the sphere of telecommunications—RUB 1.7241, in the sphere of metals and mining—RUB 2.9289, in the sphere of finance—RUB 3.8183, in the consumer sector—RUB 1.2179, in the sphere of chemicals and petrochemicals—RUB 6.3355, and in the sphere of transport—RUB 0.6096. The F-test and t-test were passed for all regression equations in the model (1), which confirms their reliability and high quality.

The conducted stationarity test (augmented Dickey–Fuller test, ADF) showed that variables in all regression equations in the model (1) are stationary. Residual analysis (calculations according to the Durbin–Watson coefficient: Durbin–Watson test) was conducted according to the statistics of residuals, collected in Table A3, and showed the following results:

• In the equation for MEOGTR, d = 752,280.8/14,415,761.5 = 0.0522;
• In the equation for MEEUTR, d = 116,871.3/1,071,588.5 = 0.1091;
• In the equation for METLTR, d = 177,770.8/2,290,170.1 = 0.0776;
• In the equation for MEMMTR, d = 685,485.7/10,331,955.0 = 0.0663;
• In the equation for MEFNTR, d = 728,376.1/5,123,522.8 = 0.1422;
• In the equation for MECNTR, d = 433,671.2/6,010,721.4 = 0.0721;
• In the equation for MECHTR, d = 5,947,387.9/99,022,936.8 = 0.0601;
• In the equation for METNTR, d = 87,707.6/1,520,538.80 = 0577.

Since the test statistics in all cases did not exceed the critical value (at the level of significance of 0.01 at n = 244 and m = 1), there is no correlation of residuals, i.e., the residuals are independent. Therefore, the assumption is confirmed, and the Durbin–Watson test was successfully passed for the model (1). In aggregate, the conducted tests show the correctness of the model (1).

According to the analysis in Table 2, during the COVID-19 crisis in 2020, the financial risks of companies were most closely and strongly associated with CSR in the following sectors of the Russian economy: oil and gas (correlation: 93.21%), transportation (75.60%), and electric power (70.68%). A moderate connection between the financial risks of companies and their CSR was observed in the finance (67.16%), telecommunications (42.22%), consumer sector (37.58%), metals and mining (32.64%), and chemicals and petrochemicals sectors (22.06%).

The arithmetic mean of the correlation coefficients was 55.14%, indicating a fairly strong relationship between the financial risks of Russian companies and their CSR in 2020 during the COVID-19 crisis. The following regression model (regression equation system) was derived to express the dependence of sectoral indices on CSR in 2020 (i.e., under the conditions of the COVID-19 crisis in Russia):

$$\left\{\begin{array}{l}\mathrm{M}\mathrm{E}\mathrm{O}\mathrm{G}\mathrm{T}\mathrm{R}=-1516.2993+5.1207\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{E}\mathrm{U}\mathrm{T}\mathrm{R}=824.0550+0.9651\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{T}\mathrm{L}\mathrm{T}\mathrm{R}=2455.8347+0.7104\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{M}\mathrm{M}\mathrm{T}\mathrm{R}=5926.6541+3.1771\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{F}\mathrm{N}\mathrm{T}\mathrm{R}=-1669.9188+4.8979\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{C}\mathrm{N}\mathrm{T}\mathrm{R}=2272.5401+2.8852\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{C}\mathrm{H}\mathrm{T}\mathrm{R}=\mathrm{20,976.3526}+1.8942\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{T}\mathrm{N}\mathrm{T}\mathrm{R}=-223.1091+0.9465\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T}.\end{array}\right.$$

(2)

Model (2) indicates that with an increase in CSR expenditure by 1 ruble during the COVID-19 crisis in 2020, the financial risks of Russian companies decreased by RUB 5.1207 in the oil and gas sector, by RUB 0.9651 in the electric power sector, by RUB 0.7104 in the telecommunications sector, by RUB 3.1771 in the metals and mining sector, by RUB 4.8979 in the finance sector, by RUB 2.8852 in the consumer sector, by RUB 1.8942 in the chemicals and petrochemicals sector, and by RUB 0.9465 in the transportation sector. Fisher’s F-test and Student’s t-test were passed for all regression equations in the model (2), confirming their reliability and high quality.

The conducted stationarity test (augmented Dickey–Fuller test, ADF) showed that variables in all regression equations in the model (2) are stationary. Residual analysis (calculations according to the Durbin–Watson coefficient: Durbin–Watson test) was conducted according to the statistics of residuals, collected in Table A4, and showed the following results:

• In the equation for MEOGTR, d = 894,335.0/32,965,482.0 = 0.0271;
• In the equation for MEEUTR, d = 207,655.1/7,762,864.4 = 0.0267;
• In the equation for METLTR, d = 309,059.0/19,361,368.6 = 0.0160;
• In the equation for MEMMTR, d = 4,534,133.7/704,458,766.6 = 0.0064;
• In the equation for MEFNTR, d = 3,532,639.1/242,981,642.0 = 0.0145;
• In the equation for MECNTR, d = 1,926,712.8/421,109,421.8 = 0.0046;
• In the equation for MECHTR, d = 10,147,837.7/583,446,800.9 = 0.0174;
• In the equation for METNTR, d = 206,205.7/5,588,871.0 = 0.0369.

Since test statistics in all cases did not exceed the critical value (at the level of significance of 0.01 at n = 250 and m = 1), the correlation of residuals is absent, i.e., residuals are independent. Therefore, the assumption is confirmed, and the Durbin–Watson test was successfully passed for the model (2). In aggregate, the conducted tests point to the correctness of the model (2).

The results of the analysis in Table 3 indicate that during the sanctions crisis in 2022, the financial risks of companies were most closely and strongly associated with CSR in the following sectors of the Russian economy: transportation (98.05%), oil and gas (correlation: 95.87%), finance (93.90%), and metals and mining (93.47%). A moderate connection between companies’ financial risks and their CSR was observed in the consumer sector (85.61%), electric power (84.94%), telecommunications (50.53%), and chemicals and petrochemicals (46.52%).

The arithmetic mean of the correlation coefficients during the sanctions crisis in 2022 was 81.11%, indicating an increased correlation between the financial risks of Russian companies and their CSR compared to the COVID-19 crisis in 2020. The following regression model (regression equation system) was derived to express the dependence of sectoral indices on CSR in 2022 under the conditions of the sanctions crisis in Russia:

$$\left\{\begin{array}{l}\mathrm{M}\mathrm{E}\mathrm{O}\mathrm{G}\mathrm{T}\mathrm{R}=3114.8040+3.5571\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{E}\mathrm{U}\mathrm{T}\mathrm{R}=1215.9815+0.5403\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{T}\mathrm{L}\mathrm{T}\mathrm{R}=2722.9867+0.4270\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{M}\mathrm{M}\mathrm{T}\mathrm{R}=-3275.7411+8.0743\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{F}\mathrm{N}\mathrm{T}\mathrm{R}=-3397.8040+6.3290\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{C}\mathrm{N}\mathrm{T}\mathrm{R}=2790.5066+2.7764\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{C}\mathrm{H}\mathrm{T}\mathrm{R}=\mathrm{66,668.0524}-4.9891\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T}\\ \mathrm{M}\mathrm{E}\mathrm{T}\mathrm{N}\mathrm{T}\mathrm{R}=-4.3705+0.6676\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T}.\end{array}\right.$$

(3)

Model (3) indicates that with an increase in CSR expenditure by 1 ruble during the sanctions crisis in 2022, the financial risks of Russian companies decreased by RUB 3.5571 in the oil and gas sector, by RUB 0.5403 in the electric power sector, by RUB 0.4270 in the telecommunications sector, by RUB 8.0743 in the metals and mining sector, by RUB 6.3290 in the finance sector, by RUB 2.7764 in the consumer sector, increased by RUB 4.9891 in the chemicals and petrochemicals sector, and decreased by RUB 0.6676 in the transportation sector. Fisher’s F-test and Student’s t-test were passed for all regression equations in the model (3), confirming their reliability and high quality.

The conducted stationarity test (augmented Dickey–Fuller test, ADF) showed that variables in all regression equations in the model (3) are stationary. Residual analysis (calculations according to the Durbin–Watson coefficient: Durbin–Watson test) was conducted according to the statistics of residuals, and collections in Table A5, and showed the following results:

• In the equation for MEOGTR, d = 2,893,469.5/40,340,900.1 = 0.0717;
• In the equation for MEEUTR, d = 353,611.3/4,085,107.0 = 0.0866;
• In the equation for METLTR, d = 1,511,454.4/19,274,395.5 = 0.0784;
• In the equation for MEMMTR, d = 10,504,632.2/341,826,058.0 = 0.0307;
• In the equation for MEFNTR, d = 6,690,311.7/194,638,088.3 = 0.0344;
• In the equation for MECNTR, d = 3,805,991.1/101,794,223.4 = 0.0374;
• In the equation for MECHTR, d = 1,123,228,498.1/3,266,176,986.7 = 0.3439;
• In the equation for METNTR, d = 124,922.0/649,411.3 = 0.1924.

Since test statistics in all cases did not exceed the critical value (at the level of significance of 0.01 at n = 232 and m = 1), the correlation of residuals is absent, i.e., the residuals are independent. Therefore, the assumption is confirmed, and the Durbin–Watson test was successfully passed for the model (3). In aggregate, the conducted tests point to the correctness of the model (3).

According to Table 4, during the growth of the Russian economy in 2024, the financial risks of companies were most closely and strongly associated with CSR in the following sectors: oil and gas (correlation: 91.77%), consumer sector (90.87%), metals and mining (84.02%), and telecommunications (82.97%). A moderate connection between companies’ financial risks and their CSR was observed in the electric power (73.36%), transportation (67.89%), finance (60.30%), and chemicals and petrochemicals (35.12%) sectors.

The arithmetic mean of the correlation coefficients in Russia in 2024 was 73.29%, indicating a less tight connection between the financial risks of Russian companies and their CSR during the economic growth phase compared to the sanctions crisis in 2022 but a stronger connection compared to the COVID-19 crisis in 2020. The following regression model (regression equation system) was derived to express the dependence of sectoral indices on CSR in 2024, during the growth phase of the Russian economy:

$$\left\{\begin{array}{l}\mathrm{M}\mathrm{E}\mathrm{O}\mathrm{G}\mathrm{T}\mathrm{R}=1965.5639+6.1255\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{E}\mathrm{U}\mathrm{T}\mathrm{R}=1260.5276+0.8604\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{T}\mathrm{L}\mathrm{T}\mathrm{R}=-1999.6414+2.9469\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{M}\mathrm{M}\mathrm{T}\mathrm{R}=-333.2809+6.3961\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{F}\mathrm{N}\mathrm{T}\mathrm{R}=9560.7505+2.4703\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{C}\mathrm{N}\mathrm{T}\mathrm{R}=-2343.7590+6.2593\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{C}\mathrm{H}\mathrm{T}\mathrm{R}=\mathrm{412,393.7939}+13.0524\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T};\\ \mathrm{M}\mathrm{E}\mathrm{T}\mathrm{N}\mathrm{T}\mathrm{R}=822.8065+0.8623\mathrm{M}\mathrm{R}\mathrm{R}\mathrm{T}.\end{array}\right.$$

(4)

Model (4) indicates that with an increase in CSR expenditure by RUB 1 in 2024, during the economic growth phase in Russia, the financial risks of Russian companies decreased by RUB 6.1255 in the oil and gas sector, by RUB 0.8604 in the electric power sector, by RUB 2.9469 in the telecommunications sector, by RUB 6.3961 in the metals and mining sector, by RUB 2.4703 in the finance sector, by RUB 6.2593 in the consumer sector, by RUB 13.0524 in the chemicals and petrochemicals sector, and by RUB 0.8623 in the transportation sector. Fisher’s F-test and Student’s t-test were passed for all regression equations in the model (4), confirming their reliability and high quality.

The conducted stationarity test (augmented Dickey–Fuller test, ADF) showed that variables in all regression equations in the model (4) are stationary. The residual analysis (calculations according to the Durbin–Watson coefficient: Durbin–Watson test) was conducted according to the statistics of residuals collected in Table A6 and showed the following results:

• In the equation for MEOGTR, d = 521,816.1/8,324,948.0 = 0.0627;
• In the equation for MEEUTR, d = 85,536.1/751,749.1 = 0.1138;
• In the equation for METLTR, d = 309,954.6/4,651,225.3 = 0.0666;
• In the equation for MEMMTR, d = 890,478.8/20,165,228.2 = 0.0442;
• In the equation for MEFNTR, d = 2,259,052.5/12,639,413.3 = 0.1787;
• In the equation for MECNTR, d = 893,446.2/9,777,932.3 = 0.0914;
• In the equation for MECHTR, d = 29,909,976.3/1,432,499,811.2 = 0.0209;
• In the equation for METNTR, d = 124,452.3/1,028,941.4 = 0.1210.

Since test statistics in all cases did not exceed the critical value (at the level of significance of 0.01 at n = 130 and m = 1), the correlation of residuals is absent, i.e., residuals are independent. Therefore, the assumption is confirmed, and the Durbin–Watson test was successfully passed for the model (4). In aggregate, the conducted tests point to the correctness of the model (4).

A systemic view of CSR’s contribution to reducing the financial risks of companies across various economic sectors in Russia during different phases of the economic cycle is illustrated in Figure 1.

Figure 1. The contribution of CSR to the reduction in companies’ financial risks by industries in Russia at different phases of the economic cycle (rubles). Source: calculated and developed by the authors.

As shown in Figure 1, the contribution of CSR to reducing the financial risks of companies in Russia’s oil and gas sector is greatest in 2024 (during the growth phase: RUB 6.1), though it is also significant in 2020 (RUB 5.1) and 2022 (RUB 3.6). In the electric power sector, the contribution of CSR to reducing financial risks of companies in Russia is greatest in 2020 (during the crisis phase: RUB 1.0) and is similarly moderate in 2022 (RUB 0.5) and 2024 (RUB 0.9).

In the telecommunications sector, the contribution of CSR to reducing the financial risks of companies in Russia is greatest in 2024 (financial risk during the growth phase: RUB 2.9) and is much weaker during the crisis phases in 2020 (RUB 0.7) and 2022 (RUB 0.4). In the metals and mining sector, the contribution of CSR to reducing the financial risks of companies in Russia is greatest in 2022 (financial risk during the sanctions crisis: RUB 8.1), though it is also significant in 2020 (financial risk during the crisis phase: RUB 3.2) and 2024 (financial risk during the growth phase: RUB 6.4).

In the finance sector, the contribution of CSR to reducing the financial risks of companies in Russia is greatest during the crisis phase—in 2022 (RUB 6.3) and 2020 (RUB 4.9). It is also significant during the growth phase in 2024 (RUB 2.5). In the consumer sector, the contribution of CSR to reducing the financial risks of companies in Russia is greatest in 2024 (financial risk during the growth phase: RUB 6.3). It is also significant in 2020 (RUB 2.9) and 2022 (RUB 2.8).

In the chemicals and petrochemicals sector, the contribution of CSR to reducing the financial risks of companies in Russia is greatest in 2024 (during the growth phase: RUB 13.1). However, it is less significant during the crisis phases—moderate in 2020 (RUB 1.9) and negative in 2022 (RUB −5.0). In the transportation sector, the contribution of CSR to reducing financial risks of companies in Russia is moderate and generally equivalent during the crisis phases—in 2020 (RUB 0.9) and 2022 (RUB 0.7). During the growth phase in 2024, it is also moderate (RUB 0.9).

The variation in the regression coefficients exceeded 50% in all three studied periods: 68.10% in 2020 (COVID-19 crisis), 185.45% in 2022 (sanctions crisis), and 82.98% in 2024 (economic growth in Russia). This proves hypothesis H and confirms that the contribution of CSR to reducing the financial risks of companies is highly differentiated across various sectors of the Russian economy and across the phases of the economic cycle.

To take into account the influence of international economic sanctions, as a risk factor (unpredictability of variability and uncertainty) of entrepreneurial activities in the modern economic conditions, a forecast of CSR of Russian companies (MRRT index) is compiled for 2025. For this, given the arithmetic mean for the sample (130 observations) for 2024 (RUB 2508.2057) and standard deviation (RUB 95.7718), 100 random numbers were generated. They are given in Appendix A Table A7. The histogram of their normal distribution is presented in Figure 2.

Figure 2. Forecast of CSR of Russian companies (MRRT index) for 2025. Source: calculated and compiled by the authors.

Forecast values of the MRRT index from Figure 2 were inserted, one by one, into the model (4), which allowed compiling two groups of alternative scenarios of changes in CSR of Russian companies by sectors of the Russian economy in 2025 (Table 5).

Table 5. Results of the scenario analysis: alternative forecasts for 2025.

Group of unlikely scenarios—their total probability: 1 + 4 + 4 + 4 + 7 = 20%
Indices	Values in 2024	Probability: 1%		Probability: 4%		Probability: 4%		Probability: 4%		Probability: 7%
		Values, RUB	Growth, %	Values, RUB	Growth, %	Values, RUB	Growth, %	Values, RUB	Growth, %	Values, RUB	Growth, %
MRRT	2508.21	2195.61	−12.46	2250.94	−10.26	2306.27	−8.05	2361.60	−5.85	2416.92	−3.64
MEOGTR	17,329.59	15,414.76	−11.05	15,753.68	−9.09	16,092.61	−7.14	16,431.53	−5.18	16,770.45	−3.23
MEEUTR	3418.48	3149.53	−7.87	3197.13	−6.47	3244.74	−5.08	3292.34	−3.69	3339.94	−2.30
METLTR	5391.80	4470.60	−17.09	4633.65	−14.06	4796.70	−11.04	4959.75	−8.01	5122.80	−4.99
MEMMTR	15,709.38	13,709.97	−12.73	14,063.86	−10.47	14,417.76	−8.22	14,771.65	−5.97	15,125.54	−3.72
MEFNTR	15,756.73	14,984.52	−4.90	15,121.20	−4.03	15,257.88	−3.17	15,394.56	−2.30	15,531.24	−1.43
MECNTR	13,355.93	11,399.27	−14.65	11,745.60	−12.06	12,091.92	−9.46	12,438.25	−6.87	12,784.58	−4.28
MECHTR	74,032.00	69,951.82	−5.51	70,674.01	−4.54	71,396.19	−3.56	72,118.38	−2.58	72,840.56	−1.61
METNTR	2985.58	2716.04	−9.03	2763.75	−7.43	2811.46	−5.83	2859.16	−4.23	2906.87	−2.64
Group of highly probable scenarios—their total probability: 12 + 30 + 23 + 10 + 5 = 80%
Indices	Values in 2024	Probability: 12%		Probability: 30%		Probability: 23%		Probability: 10%		Probability: 5%
		Values, RUB	Growth, %	Values, RUB	Growth, %	Values, RUB	Growth, %	Values, RUB	Growth, %	Values, RUB	Growth, %
MRRT	2508.21	2472.25	−1.43	2527.58	0.77	2582.91	2.98	2638.24	5.18	2693.57	7.39
MEOGTR	17,329.59	17,109.37	−1.27	17,448.29	0.68	17,787.21	2.64	18,126.13	4.60	18,465.05	6.55
MEEUTR	3418.48	3387.55	−0.90	3435.15	0.49	3482.75	1.88	3530.36	3.27	3577.96	4.67
METLTR	5391.80	5285.86	−1.96	5448.91	1.06	5611.96	4.08	5775.01	7.11	5938.06	10.13
MEMMTR	15,709.38	15,479.43	−1.46	15,833.32	0.79	16,187.21	3.04	16,541.10	5.29	16,894.99	7.55
MEFNTR	15,756.73	15,667.92	−0.56	15,804.60	0.30	15,941.28	1.17	16,077.96	2.04	16,214.63	2.91
MECNTR	13,355.93	13,130.90	−1.68	13,477.23	0.91	13,823.55	3.50	14,169.88	6.09	14,516.20	8.69
MECHTR	74,032.00	73,562.75	−0.63	74,284.93	0.34	75,007.12	1.32	75,729.30	2.29	76,451.49	3.27
METNTR	2985.58	2954.58	−1.04	3002.29	0.56	3050.00	2.16	3097.71	3.76	3145.42	5.35

Source: calculated and compiled by the authors.

As shown in Table 5, in the group of unlikely scenarios (their total probability equals 20%), the most probable (7%) scenario is the one involving the reduction in Russian companies’ expenditures for CSR by 3.64% in 2025 (down to RUB 2416.92) This will lead to a significant growth of financial risks of Russian companies: in the oil and gas sphere—by 3.23% (MEOGTR = RUB 16,770.45), in the sphere of electric energy—by 2.30% (MEEUTR = RUB 3339.94), in the sphere of telecommunications—by 4.99% (METLTR = RUB 5122.80), in the sphere of metals and mining—by 3.72% (MEMMTR = RUB 15,125.54), in the sphere of finance—by 1.43% (MEFNTR = RUB 15,531.24), in the consumer sector—by 4.28% (MECNTR = RUB 12,784.58), in the sphere of chemical and petrochemicals—by 1.61% (MECHTR = RUB 72,840.56), and in the transport sphere—by 2.64% (METNTR = RUB 2906.87).

In the group of highly probable scenarios (their total probability equals 80%), the most probable (30%) scenario is the one involving an increase in the expenditures of Russian companies for CSR by 0.77% in 2025 (up to RUB 2527). This will lead to a reduction in financial risks of Russian companies: in the oil and gas sphere—by 0.68% (MEOGTR = RUB 17,448.29), in the sphere of electric energy—by 0.49% (MEEUTR = RUB 3435.15), in the sphere of telecommunications—by 1.06% (METLTR = RUB 5448.91), in the sphere of metals and mining—by 0.79% (MEMMTR = RUB 15,833.32 RUB), in the sphere of finance—by 0.30% (MEFNTR = RUB 15,804.60), in the consumer sector—by 0.91% (MECNTR = RUB 13,477.23), in the sphere of chemicals and petrochemicals—by 0.34% (MECHTR = RUB 74,284.93), and in the transport sphere—by 0.56% (METNTR = RUB 3002.29).

5. Materials and Methods

To reliably study the experience of Russian companies across different economic sectors, this research utilizes data on the dynamics of the gross total return indices (in Russian rubles) of sectoral stocks:

• MOEX Index (2024f) for oil and gas: MEOGTR;
• MOEX Index (2024c) for electric power: MEEUTR;
• MOEX Index (2024h) for telecommunications: METLTR;
• MOEX Index (2024e) for metals and mining: MEMMTR;
• MOEX Index (2024d) for finance: MEFNTR;
• MOEX Index (2024b) for the consumer sector: MECNTR;
• MOEX Index (2024a) for chemicals and petrochemicals: MECHTR;
• MOEX Index (2024i) for transportation: METNTR.

Financial risks of companies are measured using the values of these indices. The higher the index value, the lower the financial risks of the companies. Conversely, a decrease in the index values indicates an increase in financial risks. The sample of companies for the studied indices is presented in Table A1. The dynamics of the values of these indices are shown in Table A2.

The statistical data were collected through systematization of the daily value of the above stock indices and their unification into one table with data—a time row. Data reliability is confirmed by the fact that they were taken from the official website of the Moscow Exchange. An important value of the collected data for the research goals is explained by the fact that the statistics used fully conform to the authors’ definition of CSR and ensure quantitative measurability of CSR and risks of companies, as well as their compatibility due to the common—financial—measuring.

To determine the impact of CSR on the financial risks of companies across Russian economic sectors, the authors applied regression analysis. The selected statistical method is suitable for the search for the answer to the research question posed and for data analysis, since it allows establishing not only interconnection (as the correlation method) but also mutual dependence (through regression coefficients) of the indicators and, thus, mathematically describing the regularities of changes in the financial risks of Russian companies in the course of an increase in their expenditures for CSR.

This method models the regression dependence of the values of the indices above (dependent variables) on the MOEX Index (2024g) for “Responsibility and Openness”: MRRT (independent variable). The research model is as follows:

ME = a + b × MRRT

(5)

The reliability of the regression models is determined using Fisher’s F-test, which shows the quality of the coefficient of determination, and Student’s t-test, which reflects the reliability of regression coefficients. Correlation coefficients are also considered. The general adequacy of models consists of satisfying the Gauss-Markov conditions, which are set onto residuals (homoskedasticity and stationarity), which are also tested for robustness.

To have a clear idea of the correctness of the regression equations, we additionally conducted, first, stationarity analysis with the help of the augmented Dickey–Fuller test (ADF), which allows establishing whether variables show unit roots or are stationary. Stationarity is the fundamental assumption in time series analysis; as is known, a violation of this assumption may lead to imprecise and unreliable results.

Second, the residual test assesses the consent criterion and determines the potential problems with the model. The residual test is performed in this paper by studying residuals for autocorrelation with calculations according to the Durbin–Watson coefficient (Durbin–Watson test).

The values of the selected stock indices for this research are calculated from 2013 to 2024. To determine the most preferred periods for conducting this research, for the most complete accounting of the cyclicity factor, the authors refer to the dynamics of Russia’s economic growth rates from 2013 to 2024 (Figure 3).

Figure 3. Gross domestic product in Russia from 2013 to 2024 (constant prices in per cent change). Source: developed by the authors based on the materials from the International Monetary Fund (2024).

According to Figure 3, a negative economic growth rate in Russia was observed in 2015. However, this was not an officially recognized international economic crisis. Given the remoteness of this period, it is not considered relevant for this research. In recent years, Russia experienced the COVID-19 crisis in 2020, when the GDP declined by −2.654%, and the sanctions crisis in 2022, when the GDP fell by −1.201%. It is also pertinent to consider the current experience of 2024 as a phase of economic growth for Russia: the World Bank predicts a GDP growth rate of 3.163% for Russia in 2024. To cover historical information, we additionally took into account the data for 2019, i.e., before the crisis phenomena on the upward wave of the economic cycle in Russia.

The contribution of CSR to reducing financial risks is measured by the regression coefficient b in the research model (1). The proposed hypothesis is tested using variance analysis. Hypothesis H is considered proven if the variance of the regression coefficients exceeds 50% in all three studied periods: 2020 (COVID-19 crisis), 2022 (sanctions crisis), and 2024 (economic growth of Russia).

To take into account the influence of international economic sanctions as a risk factor (unpredictability of variability and uncertainty) of entrepreneurial activities in modern economic conditions, a forecast of CSR of Russian companies (MRRT index) for 2025 is compiled. For this, given the arithmetic mean for the sample for 2024 and standard deviation, 100 random numbers are generated, and a histogram of their normal distribution is built. Given the probabilities, alternative scenarios are compiled by inserting forecast values into the regression model for 2024.

6. Discussion and Conclusions

The research contributes to the literature, including the works of Karbekova et al. (2023), Kitsai et al. (2023), and Samieva et al. (2023), by advancing the scientific concept of managing financial risks of companies through CSR, refining the specifics of this management across different economic sectors and at various phases of the economic cycle (in its two-phase model). The new scientific results obtained in this research are systematized and compared with the existing literature in Table 6.

Table 6. Contribution of the research to the scientific literature on corporate financial risk management through CSR.

Industry	Contribution (regression coefficient) of CSR to financial risk management and sustainable development of companies in the industry
	Contribution growth, %		Qualitative interpretation of the contribution
	2022/2020	2024/2020
Oil and gas	−30.53	72.20	The contribution is greatest in the growth phase
Electric power	−44.01	59.22	The contribution is greatest in the COVID-19 crisis
Telecommunications	−39.90	590.21	The contribution is greatest in the growth phase
Metals and mining	154.14	−20.79	The contribution increases over time, regardless of the phase of the cycle
Finance	29.22	−60.97	The contribution is greatest in the context of the sanctions crisis
Consumer sector	−3.77	125.45	The contribution is most significant in the growth phase
Chemicals and petrochemicals	−363.39	−361.62	The contribution is greatest in the growth phase
Transportation	−29.47	29.16	The contribution is moderately pronounced and equal in both phases of the cycle

Source: developed by the authors.

As shown in Table 6, unlike Alekseev et al. (2019), the research substantiated that in the oil and gas sector, the contribution (regression coefficient) of CSR to the financial risk management and sustainable development of companies was greatest during the growth phase. It decreased by 30.53% in 2022 compared to 2020 but increased by 72.20% in 2024 compared to 2020.

In contrast to Velinov et al. (2020), the authors substantiated that in the electric power sector, the contribution (regression coefficient) of CSR to financial risk management and sustainable development of companies was greatest during the COVID-19 crisis: it decreased by 44.01% in 2022 compared to 2020 but increased by 59.22% in 2024 compared to 2020.

Compared to Arkin et al. (2020), it is proven that in the telecommunications sector, the contribution (regression coefficient) of CSR to financial risk management and sustainable development of companies was greatest during the growth phase: it decreased by 39.90% in 2022 compared to 2020 but sharply increased by 590.21% in 2024 compared to 2020.

Unlike Karlibaeva and Nyangarika (2023), the authors proved that in the metals and mining sector, the contribution (regression coefficient) of CSR to financial risk management and sustainable development of companies increased over time, regardless of the cycle phase: it sharply increased by 154.14% in 2022 compared to 2020. However, it decreased by 20.79% in 2024 compared to 2020.

In contrast to Kantor et al. (2023), it is proven that in the finance sector, the contribution (regression coefficient) of CSR to financial risk management and sustainable development of companies was most significant during the sanctions crisis: it increased by 29.22% in 2022 compared to 2020 but decreased by 60.97% in 2024 compared to 2020.

Compared to Abdurakhmanova et al. (2021) and Turginbayeva et al. (2020), it is substantiated that in the consumer sector, the contribution (regression coefficient) of CSR to financial risk management and sustainable development of companies was greatest during the growth phase: it decreased by 3.77% in 2022 compared to 2020 but increased by 125.45% in 2024 compared to 2020.

In contrast to Ye and Dela (2023), the research proved that in the chemicals and petrochemicals sector, the contribution (regression coefficient) of CSR to financial risk management and sustainable development of companies was greatest during the growth phase: it sharply decreased by 363.39% in 2022 compared to 2020 and by 361.62% in 2024 compared to 2020.

In contrast to Sultanova and Babakhanova (2023), the authors found that in the transportation sector, the contribution (regression coefficient) of CSR to financial risk management and sustainable development of companies was moderately expressed and equivalent in both phases of the cycle: it decreased by 29.47% in 2022 compared to 2020 but increased by 29.16% in 2024 compared to 2020.

The main conclusion of this article is that the contribution of CSR to reducing companies’ financial risks is highly differentiated across economic sectors and phases of the economic cycle. As a result, the article proves hypothesis H, confirms the assumptions and continues the scientific discussion (Ai et al. 2024; Gao 2024; Raimi et al. 2024), provides an answer to the research question (RQ), and fills a gap in the literature. The theoretical significance of the results obtained in the article in continuation of the series of works by Safón and Iborra (2023), Sandoval et al. (2022), Thiemann (2022), and Wang et al. (2023) lies in revealing a new sectoral perspective on counter-cyclical managing companies’ financial risks through CSR, which allowed for an in-depth study of the causal relationships of this management for the sustainable development of companies.

The practical significance is that the results obtained by the authors revealed previously unknown best practices for the sustainable development of companies from different Russian economic sectors under the conditions of COVID-19 (2020) and the sanctions crisis (2022), as well as during economic growth (2024). The systematized experience, presented in the form of economic–mathematical models, will be useful for forecasting companies’ financial risks in future economic crises in Russia and for improving the practice of planning and organizing counter-cyclical management of Russian companies through CSR.

The managerial significance of the author’s conclusions is that they will help to increase the flexibility and efficiency of corporate financial risk management by more fully accounting for sectoral specificity and economic cyclicity when implementing CSR. Implications for Russian companies are connected with the necessity to use not a general (which does not take into account sectoral specifics) but a sectoral counter-cyclical approach to financial risk management based on CSR for the sustainable development of companies.

A limitation of the results obtained is that they were obtained based on the data until 2024, while the revealed trend of an increase in the significance of CSR for reducing the financial risks of Russian companies in the course of aggravation of the world sanctions crisis allows expecting further growth of the contribution of CSR to reducing the financial risks of Russian companies in 2025.

The compiled authors’ forecast and scenarios for 2025 are based on the 2024 experience, while, given the announced increase in international economic sanctions in 2025, the fight against financial risks might require from Russian companies much larger expenditures for CSR. That is why, to deal with the mentioned limitations, future research should collect and analyze newer data on MRRT and sectoral stock indices in Russia to specify changes in CSR’s contribution to the financial risk management of Russian companies.