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This Article argues that the liability framework governing securities trading is unable to effectively deter and compensate harms in algorithmic markets. Theory underscores the significance of robust laws to safeguard information flows and the trading process. Without this assurance, investors internalize the costs of privately policing markets and will rationally discount the capital they invest. A detailed body of regulation seeks to ensure that markets function safely, benchmarking compliance using the three familiar standards grounding liability: (i) intent; (ii) negligence; and (iii) strict liability. This Article shows that this framework is ineffective in markets that rely on algorithms – or pre-programmed computerized instructions – for trading. It makes two claims. First, a basic level of error is endemic to the operation of algorithmic markets. Especially when designed to trade in fractions of a second, algorithms must be programmed in advance of trading and anticipate how markets are likely to behave. This predictive dynamic means that error and imprecision are inevitable, irrespective of constraints that liability imposes. Secondly, liability standards fare poorly in high-speed algorithmic markets where errors can spread rapidly across multiple exchanges and security types. Even small, “reasonable,” risk taking can give rise to outsized harms, diminishing the protection provided under the negligence standard. Strict liability also fails. With error inextricably a part of predictive, pre-set algorithms, liability can arise too frequently to function as an informative signal of bad behavior. Further, small errors can create large-scale losses that may be too high for any single firm to pay. Finally, punishing only intentional bad actors leaves a swath of the market unsanctioned for careless behavior. With each standard falling short, the current design of the liability framework can leave markets facing pervasive costs of mistake, manipulation and disruption. In concluding, this weakening of laws points to a need for structural solutions in automated markets. This Article explores avenues for reform to institutionalize better behavior and fill the gaps left by the law.
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