Some people are concerned that existing market structure regulation and liquidity incentives have skewed financial markets in favor of algorithmic and high-frequency trading (HFT). This type of market activity involves the use of computer programs to automatically trade securities in financial markets. This is a problem, critics say, because it creates unfair informational asymmetries between different types of market participants and because it increases the risk of a “flash crash”—a sudden market downturn driven by computer-automated trading.
According to these critics, additional regulation should be introduced to level the playing field. However, this approach neglects to recognize that problems with market fragmentation, price synchronization, information dissemination, and market technology long predate the advent of HFT. In fact, these problems have persisted for at least 40 years—despite repeated good-faith efforts to find regulatory solutions. What’s more, there is evidence to suggest that HFT has led to increased liquidity, lower spreads and transaction costs, more efficient price discovery, and wider participation in financial markets.
That still leaves legitimate concerns about how to ensure market integrity and avoid flash crashes. Yet, further regulation may be counterproductive, since it risks creating an adversarial environment that gives market participants an incentive to hide errors associated with HFT. Instead, a cooperative solution should be pursued: firms could confidentially report human/automation interface errors to a neutral third party, which would anonymize, aggregate, and analyze such incidents in order to identify patterns that could help prevent a major market-disrupting event.
A successful model for such an approach already exists in the airline industry, where NASA’s Aviation Reporting System gathers and publishes data on human and technology errors—including those caused by automation—with the aim of preventing catastrophes and educating flight crews.