Financial Soundness Indicator, Financial Cycle, Credit Cycle and Business Cycle－Evidence from Taiwan
Bibliographic record
Abstract
Business cycle is the repeated expansions (from trough to peak) and contractions (from peak to trough) of real economic activity. Credit cycle is the cyclical process of the bank credit, ranging from short/long-term, loan to enterprise and loan to individual. Financial cycle reflects ups and downs in asset prices and financial institution's balance sheet. This paper examines the linkage among cycles as well as their lead-lag relationship. Theoretically, credit cycle is one of reasons driving business cycle, and financial cycle is a fundamental cause of credit cycle. Based on Taiwan’s quarterly data, this paper firstly identifies cyclical behavior of indicators of real economic activity, bank credit and assets prices in recent decade by defining expansion phases and contraction phases of cyclical variables. Second, this paper calculates concordance index to examine the degree of synchronization among cycles. Third, while the soundness for assets and liabilities of financial institution may drive financial cycle, this paper employs IMF’s Financial Soundness Indicator (FSI) as predictor of expansion and contraction phase of cyclical variables. Specifically, the paper assesses the health of bank’s balance sheet variables by Probit estimation on linkage between FSIs and expansion/contraction phase of cycle. Based on empirical evidence, the knowledge about the extent of assets/liability condition of financial institution corresponding to the expansion and contraction phase of financial, credit and business cycle is enhanced. Authority concerning about financial stability should oversight the performance of FSIs and then engage in prompt corrective actions when the level and volatility of those indicators sharply.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".