MétaCan
Menu
Back to cohort
Record W4405202416 · doi:10.31203/aepa.2021.18.3.005

Relationship between Chinese Financial Cycle and Business Cycle

2021· article· en· W4405202416 on OpenAlexaboutno aff
Kun Feng, Ki Seong Lee

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicInsurance and Financial Risk Management
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness cycleCredit cycleGreat ModerationEconomicsFinanceFinancial crisisFinancial systemQuarter (Canadian coin)BusinessMonetary economicsMacroeconomics

Abstract

fetched live from OpenAlex

The data used in this research are from the first quarter of 2001 to the third quarter of 2020 to analyze the relationship between the financial cycle and the business cycle in China. The research results are as follows: First, the cycling period of housing price cycles and equity price cycles are relatively short in comparison with the ratios of total credit to the private credit to GDP. Second, the research shows that the selected indicators faithfully manifest the medium-term cycle. Third, in terms of the financial cycle, because of the peak, there is concern that this could lead to a financial crisis. Fourth, the financial cycle and business cycle were synchronized before the global financial crisis of 2008. However recently they are at odds. The monetary policy and financial stability have always been the main research questions of researchers. Because of the several financial crises, their importance has become more prominent. In addition, since the Great Moderation, leading researchers at the Bank for International Settlements (BIS) have studied the relationship between the stability of the real economy and financial stability with the concepts of financial cycle and business cycle. They pay attention to the expansion and contraction of the real economy caused by the financial sector. For the real economy, it not only needs to deal with the impact of the financial sector, but to distinguish the internal factors of the business cycle and the financial cycle. The point is that the two cycles are not always in sync. In the contraction period of the business cycle, the financial cycle can be in the expansion period, and vice versa. Thus, monetary policy aimed at price stability is often at odds with macroprudential policy aimed at financial security. It is an important task to properly integrate monetary policy and macroprudential policy according to different conditions. According to the research results, the enlightenment is obtained as follows: First, the financial cycle is inevitable, and it refers to the expansion and contraction of credit according to the certain period of the economic cycle. If the financial cycle is excessively amplified, the credit and asset prices will have boom-bust. Therefore, in order to mitigate the fluctuation of the financial cycle, it is necessary to create a sound macroeconomic environment by shaking off excessively low interest rates and implementing targeted policies. Second, in the financial cycle, it is very important to monitor the asset price development so as to avoid excessive asset prices bubble. Third, as the information and financial technology developing rapidly, although the changes of financial structure are inevitable, the procyclicality of the financial system has greatly increased, and the duration and intensity of the financial cycle have also greatly increased. Therefore, the country should begin to find measures to decrease the procyclicality of financial cycle.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.070
Threshold uncertainty score0.139

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.025
GPT teacher head0.227
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicInsurance and Financial Risk ManagementFrench-language works237,207