Forecasting the development of leasing market (on the example of Ukraine)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The purpose of the study consists in the investigation of the leasing market and determining the prospects of its development in Ukraine, which will make possible for lessors to justify the choice of their strategies. There were forecasted values of the analyzed indicators of leasing market for the following three periods: the third quarter of 2016, fourth quarter of 2016, first quarter of 2017. It was proposed to calculate the integral development index of leasing services in Ukraine based on the amount of leasing companies in Ukraine, the amount of financial leasing contracts, the share of long-term lease agreements, the value of financial leasing contracts, the proportion of borrowed funds in the structure of leasing transactions financing, the share reward the lessor for the leased property in the structure of the lease payments, in the amount of leasing companies in Ukraine, the amount of financial leasing contracts, the share of long-term lease agreements, the value of financial leasing contracts, the proportion of borrowed funds in the structure of leasing transactions financing, the share reward the lessor for the leased property in the structure of the lease payments. The authors defined the growth of Ukrainian leasing market in the first quarter of 2017. The proposed integral development index is applicable both on regional and international level. The results of study can be used for substantiation of the choice of lessors’ strategies by developing alternative strategic decisions, the optimal use of which should lead to a further growth of the leasing market. Keywords: leasing, leasing companies, methods of multivariate statistical analysis, forecasting, market of leasing services. JEL Classification: C53, G17, G21
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it