MétaCan
Menu
Back to cohort
Record W4405328436 · doi:10.25683/volbi.2019.47.222

ФОНДОВАЯ БИРЖА КАК ИНСТРУМЕНТ ПРИВЛЕЧЕНИЯ КАПИТАЛА В СПЕЦИАЛИЗИРОВАННЫЕ ЭНЕРГЕТИЧЕСКИЕ КОМПАНИИ

2019· article· ru· W4405328436 on OpenAlexaboutno aff
А.А. Балабин

Bibliographic record

VenueБизнес, образование, право · 2019
Typearticle
Languageru
FieldEngineering
TopicMaritime Ports and Logistics
Canadian institutionsnot available
Fundersnot available
KeywordsComputer science

Abstract

fetched live from OpenAlex

Существенной проблемой является привлечение инвесторов в средние и малые высокотехнологичные компании. Узкая специализация и небольшие размеры предприятия де‑ лают вложения капитала в них рискованными. Традиционно считается, что акции таких компании не могут быть биржевым товаром, они могут рассчитывать только на частные инвестиции (от партнеров по бизнесу, хедж‑фондов, бизнес‑ангелов и т. п.). В статье рассматривается опыт привлечения капитала компаниями энергетического сектора на бирже Осло. Эти компании не относятся к крупным, но производят ряд важных специализированных товаров и услуг для крупнейших международных энергетических компаний (например, оказывают практически во всех регионах мира услуги по морскому бурению, геологоразведке, проектированию месторождений, транспортировке нестандартных грузов). На сегодняшний день биржа Осло привлекла значительное количество не только норвежских, но и канадских, американских и иных зарубежных компаний‑эмитентов. Анализируются состав, размеры, виды деятельности компаний энергетического сектора, акции которых торгуются на бирже. Рассматриваются исторические, институциональные и технологические особенности биржи Осло, которые позволили ей привлечь и обслуживать этих эмитентов на своей торговой площадке. Показано, что успехи средних и малых специализированных компаний энергетического сектора на бирже связаны с применением разнообразных способов размещения акций. Для этих компаний классические биржевые способы размещения (через публичную продажу акций неограниченному кругу инвесторов) дают слабый результат. В то же время наибольшее значение имеют частные размещения акций среди стратегических инвесторов, осуществляемые при посредничестве биржи. Attracting investors to medium and small specialized compa‑ nies is a significant problem. Narrow focus and small size of such companies make capital investments in them risky. Traditionally, it is believed that the shares in such businesses cannot be the sub‑ ject of stock trading and can be targeted only by private inves‑ tors, i.e. business partners, hedge funds, “business angels”, etc. The article discusses an interesting experience of raising capital by energy sector companies at Oslo Stock Exchange. These com‑ panies are not big‑sized and they produce a number of important specialized goods and services for major international energy companies, e.g. providing services for offshore drilling, geologi‑ cal exploration, field design, and transportation of non‑standard cargo in almost all regions of the world. For today, the Oslo stock exchange has attracted a significant number of issuers which are not only Norwegian, but also Canadian, American and other foreign companies. The composition, size, activities of publicly traded companies operating in energy sector are ana‑ lyzed here. The historical, institutional and technological features of the Oslo Stock Exchange, which helped to attract and serve these issuers on given trading platform, are considered. It is shown that the success at the stock exchange achieved by medium and small high‑tech energy sector companies is associated with the use of various methods of placing shares. Classic public offer‑ ings targeted any investors get weak results for these companies. And placement of shares by private issues for strategic investors through the stock exchange is of the greatest importance.

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 imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0040.007
Scholarly communication0.0120.007
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0420.016

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 head0.006
GPT teacher head0.183
Teacher spread0.177 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueБизнес, образование, правоSame topicMaritime Ports and LogisticsFrench-language works237,207