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Enregistrement W4200625511 · doi:10.3390/jrfm15010003

Methodological Foundations of the Risk of the Stock Markets of Developed and Developing Countries in the Conditions of the Crisis

2021· article· en· W4200625511 sur OpenAlexvenueno aff
Diana Burkaltseva, Shakizada Niyazbekova, Lyudmila Borsch, Mir Аbdul Kayum Jallal, Nataliya Apatova, Ardak Nurpeisova, Ayagoz Zhansagimova

Notice bibliographique

RevueJournal of risk and financial management · 2021
Typearticle
Langueen
DomaineEconomics, Econometrics and Finance
ThématiqueMarket Dynamics and Volatility
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésStock marketMarket capitalizationStock exchangePrimary marketStock market bubbleRestricted stockStock (firearms)Capital marketBusinessEconomicsMarket depthMarket makerCapitalizationFinancial economicsFinance

Résumé

récupéré en direct d'OpenAlex

The development of a methodology for the growth of the stock market through a deep transformation of the economic development system and introduction of digital technologies. The article is devoted to the study of the development of stock markets’ actual problems that affect the redistribution of capital between sectors of the economy by using tools and mechanisms between the financial and stock markets. The purpose of the study is to improve the regulatory segment of the stock market in order to further develop stock exchanges, integrate them into the global economic system, and attract investment in the economy. To achieve the goal, the following tasks were performed: the development of the London Stock Exchange was analyzed and the profit growth was determined; a comparative capitalization of the main stock instruments was carried out; internal factors of influence on the stock market were determined. The problem of the international stock market in all countries with emerging markets, first of all, lies in the improvement of the institutional environment, which is a prerequisite for the stability of the stock market. To analyze the development of stock markets, natural technical sciences were used to identify objective patterns, and determine the state and motives, using various methods and techniques: logic, generalizations, specific methods of cognition, comparison, and graphics. In the development of the stock market, the economy establishes certain natural actions in the real sector of the economy through a regulatory system of measures, and the needs of investments in the real sector of the economy, methods, and tools are used to achieve the desired results. Statistical, analytical, and dynamic methods were used. The essential foundations of the importance of stock markets and the economy are revealed; we discuss the penetration of knowledge and the conduction of a deep transformation through the introduction of digital technologies in all spheres of the economy, including the transformation of the development of the stock and financial markets nowadays. Results. The assessment of the state of securities markets in developed and developing countries is made on the example of the largest stock exchanges in Brazil and the United Kingdom. The features of the effective functioning of stock markets are revealed. The hypothesis is put forward about the insufficiency of research on the stock market of developed and developing countries and the mechanisms used having an insufficient impact on the development of the economy. From this point of view, an analysis of the dynamics of the current state of the issuers’ number and the dynamics of profitability of developed markets is carried out, the comparative capitalization volumes of the stock markets of Great Britain and Brazil are evaluated, and the weaknesses of their functioning are identified. Conclusions. The conducted research shows that countries, where stock markets are successfully functioning and developing, are catalysts for economic development and the accumulation of funds. Each country applies models of stock market development and a strategy for its regulation in accordance with the concept of a functioning market and its maturity.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,003
score de la tête « metaresearch » (Gemma)0,001
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,060
Score d'incertitude au seuil0,168

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0030,001
Méta-épidémiologie (sens strict)0,0000,000
Méta-épidémiologie (sens large)0,0000,000
Bibliométrie0,0000,000
Études des sciences et des technologies0,0000,000
Communication savante0,0000,000
Science ouverte0,0000,000
Intégrité de la recherche0,0000,000
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,059
Tête enseignante GPT0,283
Écart entre enseignants0,224 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations6
Publié2021
Routes d'admission1
Résumé présentoui

Explorer davantage

Même revueJournal of risk and financial managementMême sujetMarket Dynamics and VolatilityTravaux en français237 207