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Record 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 on OpenAlexvenueno aff
Diana Burkaltseva, Shakizada Niyazbekova, Lyudmila Borsch, Mir Аbdul Kayum Jallal, Nataliya Apatova, Ardak Nurpeisova, Ayagoz Zhansagimova

Bibliographic record

VenueJournal of risk and financial management · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicMarket Dynamics and Volatility
Canadian institutionsnot available
Fundersnot available
KeywordsStock marketMarket capitalizationStock exchangePrimary marketStock market bubbleRestricted stockStock (firearms)Capital marketBusinessEconomicsMarket depthMarket makerCapitalizationFinancial economicsFinance

Abstract

fetched live from 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.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.060
Threshold uncertainty score0.168

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
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 head0.059
GPT teacher head0.283
Teacher spread0.224 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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