Recently, a frenzy to delay orders consumed exchanges. First came IEX, whose innovation was to delay all orders by 350 microseconds. Following IEX market share gains, reported here, many venues are competing to provide a similar offering to traders. This article looks at the mechanics of the delay and how it really helps trading venues gain market share, and at whose expense. In a nutshell, execution delay can be very risky and costly to investors.
In all fairness, the delay innovation is not really IEX’s brainchild: foreign exchange electronic broker EBS, a subsidiary of ICAP, introduced the 250-microsecond-delay loop years ago. The official reason for the delay, as explained by IEX, is that the delay stops aggressive HFT from arbitraging price discrepancies between dark pools, giving dark-pool prices a chance to adjust to market levels prior to execution. And that works great for ICAP in dark-pool-like distributed foreign exchange execution and worked fine for IEX when IEX was a dark pool. Fast-forward to the present, IEX is a lit SEC-registered exchange subject to national best bid/offer (NBBO), and the same delay essentially produces stale quotes and explicitly allows for front-running.
How does that work? To start, a brief primer on NBBO. In equities, all exchanges are subject to the national best bid/offer (NBBO) rules. The requirement, a product of regulation national market systems (Reg NMS, 2005), stipulates that all trading venues have to continuously submit to the government the best limit buy and sell prices (best bid and best offer/ask) available on their respective venues. This is done simultaneously for all securities traded. The best bid and best offer quotes then enter the security information processor (SIP) run by the US Securities and Exchange Commission. From there, the quotes are aggregated in real time, the very best bid and the very best offer are picked out from all submitted data. These NBBO numbers are then distributed back to trading venues with the identification of the exchanges that have the best quotes.
And here comes the fun part: An exchange that has a local best bid and best offer that is inferior to the NBBO in a given security cannot execute the incoming market orders for this particular security. Instead, the exchange with the inferior NBBO is required to route the market orders to the exchange that has the best NBBO quotes for market orders at that particular time. If at any time, the exchange receives limit orders that are better than the prevailing NBBO, that exchange will now own the NBBO, and all the market orders will be routed there. The order routing may or may not be free of charge, depending on the venue.
Suppose IEX has a limit order for IBM at $33.96. IEX delays all incoming market orders by 350 microseconds (µs) “to deter high-frequency traders”—a nonstarter measure due to the NBBO dynamics, as discussed in our new book, Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading and Flash Crashes (Wiley, 2017). Suppose another market order has already arrived to IEX ready to claim that available liquidity that you are seeing on screen. Since the market orders are delayed, you are seeing phantom liquidity, as those orders are already spoken for by the orders that have arrived before you even looked on screen—IEX simply provides artificial or “stale” quotes by forcing delays in execution. Not only that, IEX distorts the dynamics of the entire market.
Consider this scenario: There is some unexpected news and the market is moving super-fast. IEX has a backlog of quotes, all of which have already been spoken for by incoming market orders, sitting in their respective 350-microsecond-delay pens. IEX quotes are thus the best in the markets, as all of the other exchanges have moved way beyond these levels. IEX is going into the SIP (NBBO collector) as the best available quote of the moment, forcing a ton of new orders to be routed their way due to the NBBO requirement. The end result? IEX obtains a huge share of the orders by law, most of which are executed at the subpar prices, IEX captures untold commissions, and investors feel ripped-off more than ever.
IEX introduces other opportunities for front-running as well. All of the quotes in the SIP are already at least 1 ms delayed due to the back and forth of quote transmission and another 0.5 ms or so in SIP own quote aggregation. IEX introduces another 1 ms or so delay into the SIP quotes when accounting for data transmission speeds in excess of IEX’s own delay loop. So now you have the following hierarchy: real best quotes, quotes delayed by 1 ms by IEX, and quotes delayed by 2 ms by SIP. Most of the time, markets are reasonably quiet and 1 ms delay will not matter much. However, when the markets move rapidly—for example, in response to news—the following high-frequency arbitrage opportunity presents itself. Suppose the true best bid/offer for IBM is $150.09/$150.25, and IEX is still quoting $150.45/$150.87 into the SIP. Since the SIP-based national best bid is at $150.45, exchanges with true market values cannot execute market orders, and instead are obligated to forward them to IEX, where the NBBO currently resides. IEX, as a result, is accumulating a backlog of market sell orders with limited liquidity to support them all. Feeding $150.00 and lower-priced bids into the IEX system, therefore, while placing limit orders to sell at $150.09 prevailing in other markets results in virtually risk-free short-term arbitrage opportunity, stemming simply from IEX design.
Is the execution delay innovation fair to the investors clamoring to avoid much-maligned high-frequency traders? As everywhere else in the financial markets, it is mostly a situation of “Buyers beware”. Perhaps more innovation is needed to address the problems introduced by the new forms of exchanges, and, potentially, the NBBO itself. A promising new solution may arise from Big Data research in Finance -- after all, the core issue with the delay/no-delay execution is the complexity of data processing and its instantaneous effects.
Irene Aldridge is Managing Director, Head of Research at AbleMarkets, a Big Data for Capital Markets company, specializing in real-time and near-real time Software-as-a-Service improving execution, portfolio allocation and risk management. She is a co-author of #1 New Release and Number 1 International Bestseller in Financial Risk Management category “Real-Time Risk: What Investors Should Know About Fintech, High-Frequency Trading and Flash Crashes” (Wiley, 2017), and an author of “High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems” (Wiley, 2nd edition, 2013). She can be seen at the upcoming 5th annual Big Data Finance Conference at NYU Center for Data Science on May 19, 2017.