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Algorithmic trading risks and their classification

Identification

Clé publique

doi:10.24182/2073-9885-2026-19-3-30-37

Auteurs

A. Milenkov · S. N. Makeev

Année déclarée

2026

Langue déclarée

en

Type source

article

Première détection

2026-11-09T19:08:58.536547+00:00

Dernière observation des métadonnées

2026-11-09T19:09:16.169176+00:00

Dernière vérification HTTP

Non effectuée

Institutions déclarées par la source

Plekhanov Russian University of Economics

Résumé documentaire — langue originale

The article analyzes the specific nature of risks arising in the context of algorithmic trading on the stock market. It is argued that traditional classifications (market, credit, operational risks) fail to capture key features of modern trading algorithms: ultra–high speed (microsecond range), complexity of neural network decision interpretation, and the potential of single errors to trigger systemic failures. A new risk classification is proposed based on eight criteria: source of origin, impact level, predictability degree, time horizon, legal personality nature, controllability, technological factor type, and regulatory sensitivity. This approach enables both threat systematization and identification of specific management measures – from algorithm auditing to regulatory reforms. The destabilizing factors affecting the stock market were also listed. They play an increasing role as algorithmic trading develops, including various kinds of failures, both technical and human–related, liquidity problems at unstable markets, regulatory aspects of stock market regulation, etc. Attention was paid to the consideration of the interaction of algorithms with each other and the effects that may occur as a result of such interaction. We are talking about so–called flash crashes, when a malfunction of one algorithm can lead to a cascading failure of other algorithms, which causes an unpredictable effect on the market as a whole. Even a failure in one particular market can lead to disruptions in global markets as a whole, given the interdependence and global analysis of data by algorithms when making trading decisions.

Identifiants

DOI

10.24182/2073-9885-2026-19-3-30-37

OPENAIRE

doi_________::af6c10f7fd503f51a42c470ce0316c88

OPENALEX

W7202270203

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Famille documentaire

Doctrine

Types déclarés par les sources

Article · article

Les mentions d’entités ne constituent pas une preuve de reconnaissance juridique. La juridiction étudiée est distincte du pays d’affiliation des auteurs.

Sources

OpenAIRE · Document 1

OpenAlex · Document 1 · Document 2

• DOI · https://doi.org/10.24182/2073-9885-2026-19-3-30-37
• OpenAlex · https://openalex.org/W7202270203

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• openalex:w3125424654 · CITES
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• openalex:w4303615142 · CITES
• openalex:w4394808055 · CITES
• openalex:w4404904865 · CITES
• openalex:w4406072337 · CITES
• openalex:w4415747657 · CITES
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