Fatores Macroeconômicos e a Taxa de Falência Agregada das FinTechs
Bibliographic record
Abstract
The innovations provided by FinTechs are consolidating themselves as the main transforming agent of financial services, especially because the technology provided by such businesses, promotes the expansion of access to various products in this segment, stimulates employment, income and economic growth. But even though such startups are interested in the success of their business, many fail and become insolvent. Understanding the factors that contribute to the mortality of these organizations can be an important issue for founders, funding institutions or policymakers, since anticipating a risk situation is essential to trigger preventive or alternative actions that ultimately reduce the cost of an inevitable insolvency. Given the context presented, this study analyzes through a regression model with dynamic panel data, the relationship between the macroeconomic factors and the aggregate rate of bankruptcy of the FinTechs of the ten largest countries in total number of startups according to the Startup Ranking website (2022): United States, United Kingdom, Canada, Australia, India, Germany, France, Brazil, Spain and Indonesia, for the period between 2010 and 2020. The sample consists of 9.970 FinTechs that declared bankruptcy and 137.993 active FinTechs, totaling 147.963 FinTechs. The results showed that stock market activities, the unemployment rate, the opening rate of new FinTechs, the corruption perception index and the quality of regulations are determinants of the aggregate failure of FiTechs and suggest that macroeconomic factors can influence the level of insolvency of developed countries differently from emerging countries.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".