Effects of Shareholder Groups on the Factoring Institutions Profitability: Evidence from Germany
Bibliographic record
Abstract
The significant role of trade credit in financing large companies and small and medium-sized enterprises leads to high stocks of account receivables within the balance sheets of German firms. As a result the importance of working capital financing is growing and the demand for accounts receivables financing (factoring) increases. The German factoring industry is dominated by banks. In addition to bank-owned financial institutions, many non-bank financial institutions are represented on the market. In a context of a continuing market consolidation, it is of interest whether there are differences in terms of profitability depending on shareholder groups (financial institution, non-financial institution, non holding). The German factoring market is an extremely growing market with further growth potential in an ongoing market consolidation. A further market consolidation is probable because the administrative expenses of small financial institutions and institutions without any holding are high. However, subsidiaries of a financial holding or non-financial holding show significantly lower administrative expenses. The results show that the profitability of the financial institutions is significantly influenced by the shareholders and the size of the institution. Financial institutions of a financial holding (bank-owned) are significantly less profitable than institutions without any holding or institutions of a non-financial holding. A similar picture emerges in the achieved margins of factors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".