Systemic failures and organizational risk management in algorithmic trading: Normal accidents and high reliability in financial markets

Abstract

This article examines algorithmic trading and some key failures and risks associated with it, including so-called algorithmic ‘flash crashes’. Drawing on documentary sources, 189 interviews with market participants, and fieldwork conducted at an algorithmic trading firm, we argue that automated markets are characterized by tight coupling and complex interactions, which render them prone to large-scale technological accidents, according to Perrow’s normal accident theory. We suggest that the implementation of ideas from research into high-reliability organizations offers a way for trading firms to curb some of the technological risk associated with algorithmic trading. Paradoxically, however, certain systemic conditions in markets can allow individual firms’ high-reliability practices to exacerbate market instability, rather than reduce it. We therefore conclude that in order to make automated markets more stable (and curb the impact of failures), it is important to both widely implement reliability-enhancing practices in trading firms and address the systemic risks that follow from the tight coupling and complex interactions of markets.

Harnessing the Power of Globalization: A Study of the Association between Globalization, Transportation Energy, and Insurance and Financial Services in Europe and Central Asia

“…For instance, because of the increased interdependence of financial markets, the collapse of a single financial institution may have far-reaching consequences for the health of the whole financial system. Because of this, it may be harder for consumers to get insurance and other financial services since businesses may be less inclined to accept risks in times of economic unpredictability (Min and Borch, 2022). Local insurance and financial sectors may also suffer due to globalisation since more giant international corporations can provide more cost-effective goods and services.…”

Competitive advantage in algorithmic trading: a behavioral innovation economics approach

“…Data and Analytics and Technological Speed ) shape trading strategies, market efficiency and market liquidity. Min and Borch (2021) investigate “high-reliability practices” (i.e. Quality Control ) at algorithmic trading firms and their impact on market volatility and systemic risk.…”

Machine learning and social theory: Collective machine behaviour in algorithmic trading

“…That said, the rapidly growing importance of ML systems in society suggests the possibility of a distinct form of machine sociality, urgently calling for a social theory of ML. Min and Borch (2021) offer a description of most of these data. However, given the present article's theory ambitions and the journal's scope, I deliberately do not mobilize my empirical data explicitly here.…”

“…It is important to note that flash crashes need not emerge exclusively because of collective machine behaviours. For example, it has been argued that the distressing effects of the 2010 Flash Crash were exacerbated because some key market data were delayed during the event, which prompted ‘automated data-integrity checks’ of trading algorithms to make them shut down (MacKenzie, 2021: 229; see also Aldrich et al, 2017; Min & Borch, 2021). In other words, the interaction effects of algorithms may be conditioned and further aggravated by other factors.…”

“…Since 2014, colleagues (Kristian Bondo Hansen, Pankaj Kumar, Ann-Christina Lange, Bo Hee Min, Nicholas Skar-Gislinge and Daniel Souleles) and I have conducted 213 semi-structured interviews with people who, in various ways, are or have been working with algorithmic trading, either ML-based or not (out of the 213 interviews, I conducted 70 individually or collaboratively). Min and Borch (2021) offer a description of most of these data. However, given the present article’s theory ambitions and the journal’s scope, I deliberately do not mobilize my empirical data explicitly here.…”