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In the last decade, algorithmic trading (AT) and high-frequency trading (HFT) have come to dominate the trading world, particularly HFT. During 2009-2010, anywhere from 60% to 70% of U.S. trading was attributed to HFT, though that percentage has declined in the last few years.
Here’s a look into the world of algorithmic and high-frequency trading: how they’re related, their benefits and challenges, their main users and their current and future state.
High-Frequency Trading – HFT Structure
First, note that HFT is a subset of algorithmic trading and, in turn, HFT includes Ultra HFT trading. Algorithms essentially work as middlemen between buyers and sellers, with HFT and Ultra HFT being a way for traders to capitalize on infinitesimal price discrepancies that might exist only for a minuscule period.
Computer-assisted rule-based algorithmic trading uses dedicated programs that make automated trading decisions to place orders. AT splits large-sized orders and places these split orders at different times and even manages trade orders after their submission.
Large sized-orders, usually made by pension funds or insurance companies, can have a severe impact on stock price levels. AT aims to reduce that price impact by splitting large orders into many small-sized orders, thereby offering traders some price advantage.
The algorithms also dynamically control the schedule of sending orders to the market. These algorithms read real-time high-speed data feeds, detect trading signals, identify appropriate price levels and then place trade orders once they identify a suitable opportunity. They can also detect arbitrage opportunities and can place trades based on trend following, news events, and even speculation.
High-frequency trading is an extension of algorithmic trading. It manages small-sized trade orders to be sent to the market at high speeds, often in milliseconds or microseconds—a millisecond is a thousandth of a second and a microsecond is a thousandth of a millisecond.
These orders are managed by high-speed algorithms which replicate the role of a market maker. HFT algorithms typically involve two-sided order placements (buy-low and sell-high) in an attempt to benefit from bid-ask spreads. HFT algorithms also try to “sense” any pending large-size orders by sending multiple small-sized orders and analyzing the patterns and time taken in trade execution. If they sense an opportunity, HFT algorithms then try to capitalize on large pending orders by adjusting prices to fill them and make profits.
Also, Ultra HFT is a further specialized stream of HFT. By paying an additional exchange fee, trading firms get access to see pending orders a split-second before the rest of the market does.
Profit Potential from HFT
Exploiting market conditions that can’t be detected by the human eye, HFT algorithms bank on finding profit potential in the ultra-short time duration. One example is arbitrage between futures and ETFs on the same underlying index.
The deeper that one zooms into the graphs, the greater price differences can be found between two securities that at first glance look perfectly correlated.
Please note that the axis for both instruments is different. The price differentials are significant, although appearing at the same horizontal levels.
So what looks to be perfectly in sync to the naked eye turns out to have serious profit potential when seen from the perspective of lightning-fast algorithms.
In the U.S. markets, the SEC authorized automated electronic exchanges in 1998. Roughly a year later, HFT began, with trade execution time, at that time, being a few seconds. By 2010, this had been reduced to milliseconds—see the speech by the Bank of England’s Andrew Haldane’s “Patience and finance”—and today, one-hundredth of a microsecond is enough time for most HFT trade decisions and executions. Given ever-increasing computing power, working at nanosecond and picosecond frequencies may be achievable via HFT in the relatively near future.
Bloomberg reports that while in 2010, HFT “accounted for more than 60% of all U.S. equity volume,” that proved to be a high-water mark. By 2013, that percentage had fallen to roughly 50%. Bloomberg further noted that where, in 2009, “high-frequency traders moved about 3.25 billion shares a day. In 2012, it was 1.6 billion a day” and “average profits have fallen from about a tenth of a penny per share to a twentieth of a penny.”
HFT trading ideally needs to have the lowest possible data latency (time-delays) and the maximum possible automation level. So participants prefer to trade in markets with high levels of automation and integration capabilities in their trading platforms. These include NASDAQ, NYSE, Direct Edge, and BATS.
HFT is dominated by proprietary trading firms and spans across multiple securities, including equities, derivatives, index funds, and ETFs, currencies and fixed income instruments. A 2011 Deutsche Bank report found that of then-current HFT participants, proprietary trading firms made up 48%, proprietary trading desks of multi-service broker-dealers were 46% and hedge funds about 6%. Major names in the space include proprietary trading firms like KWG Holdings (formed of the merger between Getco and Knight Capital) and the trading desks of large institutional firms like Citigroup (C), JP Morgan (JPM) and Goldman Sachs (GS).
HFT Infrastructure Needs
For high-frequency trading, participants need the following infrastructure in place:
- High-speed computers, which need regular and costly hardware upgrades;
- Co-location. That is, a typically high-cost facility that places your trading computers as close as possible to the exchange servers, to further reduce time delays;
- Real-time data feeds, which are required to avoid even a microsecond’s delay that may impact profits; and
- Computer algorithms, which are the heart of AT and HFT.
Benefits of HFT
HFT is beneficial to traders, but does it help the overall market? Some overall market benefits that HFT supporters cite include:
- Bid-ask spreads have reduced significantly due to HFT trading, which makes markets more efficient. Empirical evidence includes that after Canadian authorities in April 2012 imposed fees that discouraged HFT, studies suggested that “the bid-ask spread rose by 9%,” possibly due to declining HFT trades.
- HFT creates high liquidity and thus eases the effects of market fragmentation.
- HFT assists in the price discovery and price formation process, as it is based on a large number of orders
Challenges Of HFT
Opponents of HFT argue that algorithms can be programmed to send hundreds of fake orders and cancel them in the next second. Such “spoofing” momentarily creates a false spike in demand/supply leading to price anomalies, which can be exploited by HFT traders to their advantage. In 2013, the SEC introduced the Market Information Data Analytics System (MIDAS), which screens multiple markets for data at millisecond frequencies to try and catch fraudulent activities like “spoofing.”
Other obstacles to HFT’s growth are its high costs of entry, which include:
- Algorithms development
- Setting up high-speed trade execution platforms for timely trade execution
- Building infrastructure that requires frequent high-cost upgrades
- Subscription charges towards data feed
The HFT marketplace also has gotten crowded, with participants trying to get an edge over their competitors by constantly improving algorithms and adding to infrastructure. Due to this “arms race,” it’s getting more difficult for traders to capitalize on price anomalies, even if they have the best computers and top-end networks.
And the prospect of costly glitches is also scaring away potential participants. Some examples include the “Flash Crash” of May 6, 2010, where HFT-triggered sell orders led to an impulsive drop of 600 points in the DJIA index. Then there’s the case of Knight Capital, the then-king of HFT on NYSE. It installed new software on Aug 1, 2012, and accidentally bought and sold $7 billion worth of NYSE stocks at unfavorable prices. Knight was forced to settle its positions, costing it $440 million in one day and eroding 40% of the firm’s value. Acquired by another HFT firm, Getco, to form KCG Holdings, the merged entity still continues to struggle.
So, some major bottlenecks for HFT’s future growth are its declining profit potential, high operational costs, the prospect of stricter regulations and the fact that there is no room for error, as losses can quickly run in the millions.
The Current State of HFT
HFT as some growth potential overseas. Stock exchanges across the globe are opening up to the concept and they sometimes welcome HFT firms by offering all necessary support. On the other hand, lawsuits have been filed against exchanges for the alleged undue time-advantage that HFT firms have. Amid rising opposition, Italy was the first country to introduce a special tax on HFT in 2013, which was soon followed by France.
A study by U.S. authorities assessed the impact of HFT on a rapid bout of volatility in the Treasury market on October 15, 2014. Though it found“that there was no single cause of the turbulence,” the study didn’t rule out the potential of future risks being caused by HFT, whether in terms of impacts on pricing, liquidity or trading volumes.
The Bottom Line
The growth of computer speed and algorithm development has created seemingly limitless possibilities in trading. But, AT and HFT are classic examples of rapid developments that, for years, outpaced regulatory regimes and allowed massive advantages to a relative handful of trading firms. While HFT may offer reduced opportunities in the future for traders in established markets like the U.S., some emerging markets could still be quite favorable for high-stakes HFT ventures.
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