The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 5/2023.  Finance.

Anna S. Popkova

Cand. Sci. (Econ.), Associate Professor, Head of the Department for Monitoring of Socio-Economic Development, Institute of Economics of the National Academy of Sciences of Belarus, Minsk, Belarus

DIGITAL FINANCIAL INCLUSION BANK: CHINA'S EXPERIENCE

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Abstract

The article discusses the experience of WeBank, China's digital financial inclusion bank recognized as the best digital bank in the world. The indicators of WeBank's successful activity over the past five years are analyzed. It is revealed that the main clients of WeBank are socially vulnerable groups, small and medium-sized enterprises. The main success factors of WeBank have been identified and studied. They include digital innovation in customer service, development of the platform business model, social media integration, significant cost reduction, quick and affordable credit products, enhanced security measures, crossfunctional teamwork of employees, and regular research into user behavior. It is proposed to implement the Chinese digital banking experience in the Eurasian Economic Union member states.

Keywords: digital bank, inclusive financing, digital financial technologies.

JEL: Е51, G21, M14, L31

EDN: ENBMCY

DOI: https://doi.org/10.52180/2073-6487_2023_5_114_135

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Manuscript submission date: 31.08.2023  

For citation:

Popkova A.S. Digital Financial Inclusion Bank: China’s Experience // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2023. № 5. Pp. 114-135. (In Russ.). https://doi.org/10.52180/2073-6487_2023_5_114_135 EDN: ENBMCY

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The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 4/2023.  Finance.

Olga S. Vinogradova

Cand. Sci. (Econ.), Associate Professor, Department of Finance and Credit, Faculty of Economics, Lomonosov Moscow State University, Moscow, Russia

ORCID: 0000-0002-9575-9794

Filipp S. Kartaev

Dr. Sci. (Econ.), Head of the Department of Micro- and Macroeconomic Analysis, Faculty of Economics, Lomonosov Moscow State University, Moscow, Russia

ORCID: 0000-0001-5973-3776

IDENTIFICATION OF PROCYCLICALITY BY SPECTRAL ANALYSIS

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Abstract

The article presents an approach to identifying the procyclicality in the dynamics of macroeconomic indicators. The proposed method has been tested on the economic data for Russia (1996-2020). Regulation that takes into account the cyclical nature of the economy makes it possible to reduce the resonant effect of dynamic changes in the key determinants of the base cycle, as it implies identification of the moment when the dynamics of macroeconomic indicators change, which is a signal for a change in the monetary policy. The results obtained can be used to fine-tune the measures of the stabilization monetary policy while implementing the inflation targeting regime.

Keywords: procyclicality, monetary policy, macroeconomic indicators, spectral analysis, cyclical component.

JEL: E3, E37, E44

EDN: HTHOSS

DOI: https://doi.org/10.52180/2073-6487_2023_4_65_88

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Manuscript submission date: 10.07.2023

For citation:

Vinogradova O.S., Kartaev F.S. Identification of procyclicality by spectral analysis // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2023. № 4. Pp. 65-88. (In Russ.). https://doi.org/10.52180/2073-6487_2023_4_65_88 EDN: HTHOSS

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The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 4/2023.  Finance.

Olga A. Lvova

Dr. Sci. (Econ.), Associate Professor at the Financial Management Department, School of Public Administration, Lomonosov Moscow State University, Moscow, Russia

ORCID: 0000-0001-9835-3418

BUSINESS EVALUATION WITHIN CRISIS MANAGEMENT: APPROACHES FOR OWNERS, CREDITORS, INSOLVENCY PRACTITIONERS AND INVESTORS

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Abstract

The article suggests approaches to business evaluation by various stakeholders for crisis management purposes. For owners and managers, an identification system of business sustainability decline has been developed, including market, operational, investment, financial, managerial and organizational indicators. For creditors, a strategic map is proposed,

containing market, process, resource and financial triggers identified during express diagnostics and advanced trigger analysis. For insolvency practitioners, the methodology of financial analysis has been modified, supplemented by an algorithm for debtor’s transactions

analysis and identifying signs of “objective bankruptcy”. For potential investors, a scenario assessment model has been developed, which makes it possible to determine the benefits, opportunity costs, the feasibility of merger or acquisition of a distressed company, the cost of its restoration and integration, and the synergetic effect of such M&A.

Keywords: financial analysis, business sustainability, signs (indicators, triggers) of crisis, insolvency (bankruptcy), insolvency practitioner, debtor, creditor, investor, mergers and acquisitions with distressed companies, strategic map, financial ratios.

JEL: D25, G21, G33, G34, H12, M42

EDN: KTQLRS

DOI: https://doi.org/10.52180/2073-6487_2023_4_89_111

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Manuscript submission date: 29.06.2023  

For citation:

Lvova O.A. Business evaluation within crisis management: approaches for owners, creditors, insolvency practitioners and investors // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2023. № 4. Pp. 89-111. (In Russ.). https://doi.org/10.52180/2073-6487_2023_4_89_111 EDN: KTQLRS

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The Bulletin of the Institute of Economics of the Russian Academy of Sciences № 3/2023.  Finance.

Roman S. Kuznetsov

postgraduate student, Saint Petersburg State University of Economics, Saint Petersburg, Russia

Tatiana G. Tumarova

Cand. Sci. (Econ.), Professor, Director of the Graduate Institute of Saint Petersburg State University of Economics, Saint Petersburg, Russia

PAO GAZPROM STOCK PRICE PREDICTION USING LSTM NEURAL NETWORKS

84-98

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Abstract

Artificial intelligence and machine learning are increasingly being used in many areas of economy and finance. Developments in technology and the increasing accessibility of computing facilities allow for the wider use of various programming tools. For many companies operating in the field of stock trading and other derivatives, availability of an effective forecasting mechanism is an important competitive advantage, and increasing this advantage is now possible by using neural network models such as LSTM. The authors present the results of testing the LSTM model on the basis of actual data (quotations of Gazprom shares at Moscow Exchange): quotation values were predicted and the trend of Gazprom stocks at Moscow Exchange, starting from September 2019, was revealed.

Keywords: stock exchange, neural networks, LSTM, forecasting, PAO Gazprom shares.

JEL: F21, F37, F47

EDN: RTZFON

DOI: https://doi.org/10.52180/2073-6487_2023_3_84_98

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Manuscript submission date: 22.03.2023  

For citation:

Kuznetsov R.S., Tumarova T.G. PAO Gazprom stock price prediction using LSTM neural networks // Vestnik Instituta Ekonomiki Rossiyskoy Akademii Nauk. 2023. № 3. Pp. 84-98. (In Russ.). https://doi.org/10.52180/2073-6487_2023_3_84_98 EDN: RTZFON

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