Latency Arbitrage still exists in a world with NBBO regulations. Research consistently finds that not only does the strategy work in theory, but that it is consistently put into practice by HFT firms to the detriment of other market participants. If a firm can calculate the NBBO ahead of other market participants and the market regulator, it can still legally front-run the market, and risklessly extract rents from end-customers. The NBBO formula is not computationally expensive, and its underlying data is necessarily publicly available to all trading firms. This occurs in the real world, in the order of $billions annually.
The UK's Financial Conduct Authority:
>We use stock exchange message data to quantify the negative aspect of high-frequency trading, known as “latency arbitrage.” The key difference between message data and widely-familiar limit order book data is that message data contain attempts to trade or cancel that fail. This allows the researcher to observe both winners and losers in a race, whereas in limit order book data you cannot see the losers, so you cannot directly see the races. We find that latency-arbitrage races are very frequent (one per minute for FTSE 100 stocks), extremely fast (the modal race lasts 5-10 millionths of a second), and account for a large portion of overall trading volume (about 20%). Race participation is concentrated, with the top-3 firms accounting for over half of all race wins and losses. Our main estimates suggest that eliminating latency arbitrage would reduce the cost of trading by 17% and that the total sums at stake are on the order of $5 billion annually in global equity markets
The University of Michigan's Economics department:
>We illustrate this process and the potential for latency arbitrage in Figure 1. Given order information from exchanges, the SIP takes some finite time, say δ milliseconds, to compute and disseminate the NBBO. A computationally advantaged trader who can process the order stream in less than δ milliseconds can simply out-compute the SIP to derive NBBO,a projection of the future NBBO that will be seen by the public. By anticipating future NBBO, an HFT algorithm can capitalize on cross-market disparities before they are reflected in the public price quote, in effect jumping ahead of incoming orders to pocket a small but sure profit. Naturally this precipitates an arms race, as an even faster trader can calculate an NBBO* to see the future of NBBO, and so on.
>Conservative estimates suggest that at least 4% of dark trading occurs at stale reference prices. High-frequency trading firms (HFTs) almost always benefit from such stale prices, being on the profitable side of the trades between 96 and 99% of the time. Furthermore, stale trading does not happen at random but is driven by the behaviour of HFTs. HFTs as a group almost never provide marketable liquidity in the dark and rather behave strategically to exploit their speed advantage by submitting marketable orders to execute against stale quotes.
I described the canonical front-running example in my first comment, as it gives most non-finance readers a quick overview of how HFTs work, without needing to describe regulations, strategies, NBBO, SIP, etc.
The specific strategy currently employed by HFTs is somewhat immaterial in the broader context of a discussion about front-running. For as long as a firm can legally front-run the market with any strategy, it can undermine the market and risklessly extract profits.
The UK's Financial Conduct Authority:
>We use stock exchange message data to quantify the negative aspect of high-frequency trading, known as “latency arbitrage.” The key difference between message data and widely-familiar limit order book data is that message data contain attempts to trade or cancel that fail. This allows the researcher to observe both winners and losers in a race, whereas in limit order book data you cannot see the losers, so you cannot directly see the races. We find that latency-arbitrage races are very frequent (one per minute for FTSE 100 stocks), extremely fast (the modal race lasts 5-10 millionths of a second), and account for a large portion of overall trading volume (about 20%). Race participation is concentrated, with the top-3 firms accounting for over half of all race wins and losses. Our main estimates suggest that eliminating latency arbitrage would reduce the cost of trading by 17% and that the total sums at stake are on the order of $5 billion annually in global equity markets
https://www.fca.org.uk/publication/occasional-papers/occasio...
The University of Michigan's Economics department:
>We illustrate this process and the potential for latency arbitrage in Figure 1. Given order information from exchanges, the SIP takes some finite time, say δ milliseconds, to compute and disseminate the NBBO. A computationally advantaged trader who can process the order stream in less than δ milliseconds can simply out-compute the SIP to derive NBBO,a projection of the future NBBO that will be seen by the public. By anticipating future NBBO, an HFT algorithm can capitalize on cross-market disparities before they are reflected in the public price quote, in effect jumping ahead of incoming orders to pocket a small but sure profit. Naturally this precipitates an arms race, as an even faster trader can calculate an NBBO* to see the future of NBBO, and so on.
http://strategicreasoning.org/wp-content/uploads/2013/02/ec3...
The Bank for International Settlements:
>Conservative estimates suggest that at least 4% of dark trading occurs at stale reference prices. High-frequency trading firms (HFTs) almost always benefit from such stale prices, being on the profitable side of the trades between 96 and 99% of the time. Furthermore, stale trading does not happen at random but is driven by the behaviour of HFTs. HFTs as a group almost never provide marketable liquidity in the dark and rather behave strategically to exploit their speed advantage by submitting marketable orders to execute against stale quotes.
https://www.bis.org/publ/work1115.htm