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<article article-type="research-article" dtd-version="1.3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xml:lang="ru"><front><journal-meta><journal-id journal-id-type="publisher-id">emjume</journal-id><journal-title-group><journal-title xml:lang="ru">Экономика и управление</journal-title><trans-title-group xml:lang="en"><trans-title>Economics and Management</trans-title></trans-title-group></journal-title-group><issn pub-type="ppub">1998-1627</issn><issn pub-type="epub">3033-7984</issn><publisher><publisher-name>СПбУТУиЭ</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="doi">10.35854/1998-1627-2025-3-372-384</article-id><article-id custom-type="elpub" pub-id-type="custom">emjume-2447</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="ru"><subject>НАУЧНЫЕ ИССЛЕДОВАНИЯ МОЛОДЫХ УЧЕНЫХ</subject></subj-group><subj-group subj-group-type="section-heading" xml:lang="en"><subject>SCIENTIFIC RESEARCH OF YOUNG SCIENTISTS</subject></subj-group></article-categories><title-group><article-title>О влиянии показателей финансовой системы на экономический рост в Китае, Индии и ряде других стран</article-title><trans-title-group xml:lang="en"><trans-title>The impact of financial system indicators on economic growth in China, India and a number of other countries</trans-title></trans-title-group></title-group><contrib-group><contrib contrib-type="author" corresp="yes"><contrib-id contrib-id-type="orcid">https://orcid.org/0009-0000-8038-6035</contrib-id><name-alternatives><name name-style="eastern" xml:lang="ru"><surname>Клюкин</surname><given-names>Б. Д.</given-names></name><name name-style="western" xml:lang="en"><surname>Klyukin</surname><given-names>B. D.</given-names></name></name-alternatives><bio xml:lang="ru"><p>Борис Денисович Клюкин, аспирант</p><p>119991; Ленинские горы, д. 1; Москва</p></bio><bio xml:lang="en"><p>Boris D. Klyukin, postgraduate student</p><p>119234; 1 Leninskie Gory; Moscow</p></bio><email xlink:type="simple">bdklyukin@gmail.ru</email><xref ref-type="aff" rid="aff-1"/></contrib></contrib-group><aff-alternatives id="aff-1"><aff xml:lang="ru">Московский государственный университет<country>Россия</country></aff><aff xml:lang="en">Lomonosov Moscow State University<country>Russian Federation</country></aff></aff-alternatives><pub-date pub-type="collection"><year>2025</year></pub-date><pub-date pub-type="epub"><day>24</day><month>04</month><year>2025</year></pub-date><volume>31</volume><issue>3</issue><fpage>372</fpage><lpage>384</lpage><permissions><copyright-statement>Copyright &amp;#x00A9; Клюкин Б.Д., 2025</copyright-statement><copyright-year>2025</copyright-year><copyright-holder xml:lang="ru">Клюкин Б.Д.</copyright-holder><copyright-holder xml:lang="en">Klyukin B.D.</copyright-holder><license license-type="creative-commons-attribution" xlink:href="https://creativecommons.org/licenses/by/4.0/" xlink:type="simple"><license-p>This work is licensed under a Creative Commons Attribution 4.0 License.</license-p></license></permissions><self-uri xlink:href="https://emjume.elpub.ru/jour/article/view/2447">https://emjume.elpub.ru/jour/article/view/2447</self-uri><abstract><sec><title>   Цель</title><p>   Цель. Изучить взаимосвязь между темпами экономического роста и показателями финансовой системы в Китае, Индии, остальными оригинальными членами БРИКС, Ассоциации государств Юго-Восточной Азии (ASEAN), ряда стран Запада и вычислить пороговые значения, структуру финансовой системы, приводящую к экономическому росту.</p></sec><sec><title>   Задачи</title><p>   Задачи. Для достижения поставленной цели необходимо собрать и рассчитать панель данных из ряда стран и регионов, содержащую информацию об их темпах экономического роста и состоянии финансовых систем (последние представлены показателями рынка акций и банковской системы); исследовать данные посредством визуального анализа, в частности построить точечные диаграммы и гистограммы частот; провести статистический анализ, то есть рассчитать корреляционные коэффициенты, построить кластеры и провести их метаанализ; сформулировать вывод об оптимальной комбинации и уровне факторов рынка акций, банковской системы, способствующей экономическом росту.</p></sec><sec><title>   Методология</title><p>   Методология. Первичный анализ данных произведен с использованием матриц точечных диаграмм и гистограмм частот. Для обобщения основных характеристик панели данных выполнен корреляционный анализ. Основной метод исследования — кластеризация методом K-средних и метаанализ результатов.</p></sec><sec><title>   Результаты</title><p>   Результаты. Собрана, дополнена и рассчитана панель данных включающая в себя 17 стран (Россию, Бразилию, Германию, Гонконг, Израиль, Индию, Индонезию, Китай, Малайзию, Сингапур, США, Таиланд, Турцию, Филиппины, Южную Африку, Южную Корею и Японию), десять экономических переменных (четыре — характеризующие банковские системы, то есть относительный размер банковских депозитов, широкой денежной массы, активов Центрального банка и внутренних кредитов частному сектору; четыре — характеризующие рынок ценных бумаг, в частности волатильность цен на акции, оборачиваемость фондового рынка, размер объема торгов акций и фондового рынка к валовому внутреннему продукту (ВВП); две — характеризующие экономический рост, то есть годовой темп прироста ВВП/душу (ППС, 2021) и средневзвешенный годовой темп прироста ВВП/душу (ППС, 2021) с 1996 по 2020 гг. Такая панель использована для проведения кластерного анализа, и на основании его метаданных сделаны обоснованные выводы.</p></sec><sec><title>   Выводы</title><p>   Выводы. Метаанализ кластеров показал, что максимальный положительный эффект от финансовой системы для экономики достигается при относительном размере депозитов и кредитов в экономике на уровне ВВП, а отклонения вниз или вверх от данного уровня введут к замедлению экономики. Аналогичный эффект наблюдается при превышении 10 % ВВП активов центральных банков. Оборачиваемость фондового рынка должна быть равна больше 100 %, а до превышения этого предельного уровня положительного эффекта для экономики не наблюдается, волатильность фондового рынка в целом не оказывает влияния на экономический рост. Установлено, что негативный эффект от превышения порогового значения относительного размера кредитов значительно уменьшается при условии, что оборачиваемость фондового рынка находится в оптимальном диапазоне.</p></sec></abstract><trans-abstract xml:lang="en"><sec><title>   Aim</title><p>   Aim. The work is aimed to study the relationship between economic growth rates and financial system indicators in China, India, the remaining original BRICS countries, the Association of Southeast Asian Nations (ASEAN), a number of Western countries, as well as to calculate threshold values and the structure of the financial system that induces the economic growth.</p></sec><sec><title>   Objectives</title><p>   Objectives. In order to achieve this goal, it is necessary to collect and calculate a data panel from a number of countries and regions containing information on their economic growth rates and the state of their financial systems (the latter are represented by indicators of the stock market and the banking system); analyze the data through visual analysis, in particular, construct scatter diagrams and frequency histograms; conduct statistical analysis, that is, calculate correlation coefficients, construct clusters and conduct their meta-analysis; formulate a conclusion on the optimal combination and level of stock market factors, the banking system that contributes to economic growth.</p></sec><sec><title>   Methods</title><p>   Methods. The primary data analysis was performed using scatter diagram matrices and frequency histograms. A correlation analysis was performed to summarize the main characteristics of the data panel. The main research method was K-means clustering and meta-analysis of the results.</p></sec><sec><title>   Results</title><p>   Results. A data panel was compiled, supplemented and calculated, to include 17 countries (Russia, Brazil, Germany, Hong Kong, Israel, India, Indonesia, China, Malaysia, Singapore, USA, Thailand, Turkey, the Philippines, South Africa, South Korea, and Japan), ten economic variables (four variables characterizing banking systems, that is, the relative extent of bank deposits, broad money supply, central bank assets and internal loans to the private sector; four variables characterizing the securities market, in particular, stock price volatility, stock market turnover, the size of stock and stock market trading volume to gross domestic product (GDP); two variables characterizing economic growth, that is, the annual growth rate of GDP per capita (purchasing power par, PPP, 2021), and the weighted average annual growth rate of GDP per capita (PPP, 2021) from 1996 to 2020. This panel was used to conduct a cluster analysis, and based on its metadata, reasonable conclusions were made.</p></sec><sec><title>   Conclusions</title><p>   Conclusions. The cluster meta-analysis showed that the maximum positive effect of the financial system on the economy is achieved when the relative size of deposits and loans in the economy is at the GDP level, and downward or upward deviations from this level induce a slowdown in the economy. A similar effect is noted when central bank assets exceed 10 % of GDP. The stock market turnover should be greater than 100 %, and no positive effect on the economy is registered before this threshold is exceeded, while stock market volatility as a whole does not affect economic growth. It was established that the negative effect of exceeding the threshold value of the relative size of loans is significantly reduced, provided that the stock market turnover is in the optimal range.</p></sec></trans-abstract><kwd-group xml:lang="ru"><kwd>Китай</kwd><kwd>Индия</kwd><kwd>кластерный анализ</kwd><kwd>пороговые значения</kwd><kwd>оборачиваемость фондового рынка</kwd><kwd>относительный размер банковских депозитов</kwd><kwd>относительный размер внутренних кредитов частному сектору</kwd></kwd-group><kwd-group xml:lang="en"><kwd>China</kwd><kwd>India</kwd><kwd>cluster analysis</kwd><kwd>threshold values</kwd><kwd>stock market turnover</kwd><kwd>relative size of bank deposits</kwd><kwd>relative size of internal loans to the private sector</kwd></kwd-group></article-meta></front><back><ref-list><title>References</title><ref id="cit1"><label>1</label><citation-alternatives><mixed-citation xml:lang="ru">Gözgör G., Gözgör K. 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