The Regulation of Financial Innovations: High Frequency Trading - Algorithmic Trading
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This leads inevitably to a latency arms race in which the designers of trading algorithms adopt any available method to shave milliseconds or even microseconds — one millionth of a second — from response time.
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Most exchanges and trading forums have catered to the high-frequency traders, providing premium access options and interface features that preserve or enhance the advantage of speed. But there is another way to neutralize small speed advantages: change the way markets time the matching of buy and sell orders.
For example, when a trader submits a request to buy a share of Apple at a specific price, the exchange matches it immediately if there is an offer from someone else to sell at the same price or less.
This immediacy is what allows a trader able to react more swiftly to new information say news about the latest iPhone to profit off of slower rivals. In a frequent call market, on the other hand, orders to buy and sell are matched at fixed intervals such as once every second.
International Workshop on Algorithmic and High Frequency Trading | Banque de France
By ensuring that speed no longer categorically prevails, the incentive for shaving milliseconds and microseconds is virtually eliminated. Orders within the interval compete instead based on price, leading to a more efficient overall set of trades. Regulators have taken notice. New York Attorney General Eric Schneiderman has publicly endorsed the frequent call market — also known as a frequent batch auction — to even the playing field.
International Workshop on Algorithmic and High Frequency Trading
At present, however, no stock exchange operates as a full-fledged frequent call market. One major hurdle to adoption is perception: the view that faster is always better. Another problem that some have raised is that it would only be viable if all exchanges adopted the method simultaneously because otherwise traders would always pick the venue offering the most immediacy.
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But is this true? In modern markets, innovation, and technology challenge these core principles of regulation. The engineering of computer-driven automated trade execution, the development of algorithmic trading, and the introduction of high frequency trading strategies accompany a number of important shifts in financial market intermediation.
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First, a universe of private trading platforms known as alternative trading systems ATSs increasingly compete with and displace conventional exchanges. Dark pools mitigate information leakage, enabling institutional investors to execute large block trade transactions without fear that imitators will replicate or that predators may prey on their trades.
Second, dark pools intermediate trading with limited regulatory oversight. These private pools function in a manner similar to conventional securities exchanges and clearinghouse platforms; yet, dark pools are subject to a lighter-touch regulatory framework. As a result, hidden dark pool trades enjoy reduced regulatory, compliance, and transaction costs.
Third, fragmentation has fractured trading markets. This Article argues that gaps in governance, contentious conflicts of interest, and increasingly intense fragmentation in trading markets create a growing source of underexplored concerns. An examination of the current regulatory framework reveals noteworthy perils including the potential for high volatility and significant market disruption.
These concerns necessitate exploring ex ante the various enforcement measures, regulations, governance protocols, and information gathering tools already employed in markets to mitigate systemic risk concerns. This Article cautions regulators and commentators to emphasize dynamic, sustainable macroprudential regulatory solutions. Suggested Citation: Suggested Citation.
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Algorithmic Trading. These practices including the following: General Risk Assessment and Response — Firm should undertake a holistic review of their trading activity and consider implementing a cross-disciplinary committee to assess and react to the evolving risks associated with algorithmic strategies. Software Testing and System Validation — Testing of algorithmic strategies prior to being put into production is an essential component of effective policies and procedures. Trading Systems — Firms should develop their policies and procedures to include review of trading activity after an algorithmic strategy is in place or has been changed.