TY - JOUR
T1 - Systemic Failures and Organizational Risk Management in Algorithmic Trading
T2 - Normal Accidents and High Reliability in Financial Markets
AU - Min, Bo Hee
AU - Borch, Christian
PY - 2022/4
Y1 - 2022/4
N2 - 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.
AB - 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.
KW - Algorithmic trading
KW - Financial regulation
KW - Flash crash
KW - High-reliability organizations
KW - Normal accidents theory
KW - Risk
KW - Algorithmic trading
KW - Financial regulation
KW - Flash crash
KW - High-reliability organizations
KW - Normal accidents theory
KW - Risk
U2 - 10.1177/03063127211048515
DO - 10.1177/03063127211048515
M3 - Journal article
SN - 0306-3127
VL - 52
SP - 277
EP - 302
JO - Social Studies of Science
JF - Social Studies of Science
IS - 2
ER -