High-frequency traders have been characterized as “cheetah traders,” an uncomplimentary reference to their character and speed. There have always been some traders who are faster than others, so there must be attributes other than speed that define today’s high-frequency-trading professionals. Speed is an important component of high-frequency-trading success, but there is a strong case that it is a change in paradigm that drives the popularity of high-frequency trading.
Financial analyst conferences are one setting in which low-frequency traders converse on subjects as broad and complex as monetary policy, stock valuations, and financial statement analysis. High-frequency trading conferences are reunions during which computer scientists meet to discuss, among other things, IP connections, machine learning, and game theory. Given these traders’ dissimilar backgrounds, high-frequency traders operate under a different paradigm from that of their low-frequency-trading peers. The authors explore how this different background translates into a new investment paradigm for high-frequency traders. They also provide six options to help low-frequency-trading professionals survive in the current high-frequency-trading era.
How Is This Research Useful to Practitioners?
High-frequency trading is certainly popular, and these traders have some competitive advantages relative to their low-frequency-trading peers. The current speed advantage will gradually disappear, as other advantages have disappeared in previous technological revolutions. But high-frequency traders’ strategic trading behavior is robust, and strategic traders have little trouble adapting to different environments. The presence of “big data” allows them to train their algorithms before deployment. Advances in machine learning and microstructure theory will compensate for the loss of the speed advantage.
Part of the success of high-frequency traders is the reluctance of low-frequency traders to adopt (or even recognize) the “volume-clock” paradigm, characterized by strategic decisions made in a volume-clock metric rather than chronological time. The authors argue that high-frequency traders operate in event-based time, such as transactions or volume, whereas low-frequency traders operate in chronological time, which has made low-frequency traders vulnerable.
They conclude that low-frequency-trading professionals have several options for surviving this new high-frequency-trading era. The following options are offered by the authors: (1) adopt the high-frequency-trading event-based-time paradigm, (2) develop statistics to monitor high-frequency-trading activity and take advantage of the weaknesses, (3) join the herd by varying approaches to low-frequency trading, (4) use smart brokers that specialize in searching for liquidity and avoiding “footprints” to better disguise trades, (5) trade on exchanges that incorporate technology to monitor order-flow toxicity, and (6) avoid seasonal effects that can be easily found and tracked by high-frequency traders.
How Did the Authors Conduct This Research?
High-frequency-trading strategies seek to profit from low-frequency traders’ errors. Low-frequency-trading decisions are typically made in chronological time, which leaves “footprints” that can be easily tracked by high-frequency traders. To demonstrate this phenomenon, the authors gathered a sample of E-mini S&P 500 Index futures trades between 7 November 2010 and 7 November 2011. They divided the day into 24 hours and, for every hour, added the volume traded at each second, irrespective of the minute.
This analysis allows the authors to observe the volume distribution within each minute and search for low-frequency traders executing their massive trades in a chronological-time space. The largest volume trade concentrations within a minute tend to occur during the first few seconds of almost every hour of the day.
A mildly sophisticated high-frequency-trading algorithm will evaluate the order imbalance at the beginning of every minute and realize that this component is persistent and reveals that a trader is front-running certain algorithms while still executing the largest part of the trade. Because high-frequency traders operate on a volume clock, they can act as soon as the pattern is identified and anticipate the side and sign of low-frequency traders’ massive orders for the rest of the hour.
Most academic and practitioner models are devised in chronological time, so their implementation will lead to patterns that high-frequency traders can exploit to their advantage. This is just one example of how the chronological-time paradigm has exposed low-frequency traders in the current trading environment.
High-frequency- and low-frequency-trading professionals have dissimilar backgrounds, which has led high-frequency traders to operate under a different paradigm. The authors provide several reasonable options for low-frequency traders to survive among high-frequency traders, including the recommendation that low-frequency-trading firms adopt the high-frequency-trading event-based-time paradigm.