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Record W3106739737 · doi:10.3390/jrfm13120306

Corporate Bond Market in Poland—Prospects for Development

2020· article· en· W3106739737 on OpenAlexvenueno aff
Jakub Kubiczek

Bibliographic record

VenueJournal of risk and financial management · 2020
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicCredit Risk and Financial Regulations
Canadian institutionsnot available
Fundersnot available
KeywordsMarket liquidityIssuerCorporate bondBond marketBondBusinessProfitability indexMarket microstructureFactor marketMonetary economicsFinancial systemEconomicsMarket economyFinanceOrder (exchange)

Abstract

fetched live from OpenAlex

The Polish corporate bond market does not have a history as long as the American one, however, it is characterized by stable annual growth. The growth of the market is related to the growth of its liquidity and is determined by a number of entities, both on the demand and the supply side. The aim of the study was to present the structure of the Catalyst market and bond trading in Poland. The study also discusses the market’s development and identifies the factors that determine this development. Based on reports concerning trading on the Catalyst market, a huge growth was noticed in the 10 years since the market’s establishment. Forecasts indicate that the growth will continue. The outbreak of the SARS-CoV-2 pandemic will cause the market development to be slower than the model’s forecast, although the data for the first nine months of 2020 suggest that the upward trend will be maintained. Moreover, for the market to continue to thrive, a rating must be compulsory for corporate bond issuers. A comparison of the ratings of individual issuers enables investors to analyze the risk and profitability of corporate bonds in an easier way.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.332
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.029
GPT teacher head0.205
Teacher spread0.176 · 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 teacher head, 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

Citations8
Published2020
Admission routes1
Has abstractyes

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