Research on the Influence of Shadow Banking Development on the Scale of Social Financing in China
Notice bibliographique
Résumé
Background: With the continuous improvement and innovation of China’s market-oriented economy, some traditional financial institutions have released new financial products through financial innovation, and the transaction leverage level of most financial institutions and some non-financial institutions is also rising year by year. The latest Quarterly Monitoring Report of China’s Shadow Banks issued by Moody’s, an international rating agency, shows that the assets of China’s broad shadow banks in the first quarter decreased by about 540 billion yuan to 58.7 trillion yuan, continuing the downward trend in recent years. At the same time, China’s economy rebounded rapidly, and the proportion of shadow bank assets in nominal GDP fell to 55.4% from 58.3% at the end of 2020, the lowest level in eight years. The report shows that in the first quarter of 2021, the overall economic leverage ratio measured by the proportion of the adjusted total social financing to the nominal GDP has declined, because the nominal GDP growth in the quarter was 21.2%, far exceeding the growth of 10.1% of the total social financing. Even so, China’s shadow banks are still huge. Also, some non-financial enterprises began to operate shadow banking business, the number of shadow banking enterprises coming into the financial market is growing fast, which is the background and environment of the emergence and development of enterprise shadow banking studied in this paper. In 2020, due to the impact of the epidemic, the Chinese government will actively adjust its macro policies and release liquidity, which will further drive the development of shadow credit business. In the first quarter of 2020, the overall shadow bank assets increased by 100 billion yuan, rebounding compared with 2019. Among them, financial products and asset management products accounted for the main part, with a total increase of 350 billion yuan. Objective: This paper focuses on using different kinds of products operated by shadow banking enterprises to represent the shadow banking scale of enterprises, and explores its impact on the total amount of social financing, based on which a series of policy recommendations are given. Methods: Collect the operation data of shadow banking enterprises in the ten years from 2010 to 2019, use ols regression analysis and intermediary effect test to analyze the development of enterprises’ shadow banking scale and China’s social financing scale. Results: The development of shadow banking will affect the total scale of social financing. There is a non-linear correlation between the development of shadow banking and the total scale of social financing. Conclusion: The improvement of enterprises’ shadow banking scale will promote the expansion of social financing scale as a whole, and will have a negative effect on the productive investment of various enterprises themselves.
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Prédiction distillée sur la base complète
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Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
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