Algorithmic trading risks and their classification

Abstract

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.