Aggressive High-Frequency Trading in Equities

02/18/2015 04:38 pm ET | Updated Apr 19, 2015

Co-authored with Steve Krawciw

Aggressive high-frequency trading (HFT) is a classification of electronic trading strategies that rely on ultra-fast infrastructure and market orders to take advantage of news, predictive analytics or short-lived information asymmetries. Unlike passive HFTs that tend to provide market-making services, aggressive HFTs' models attempt to reach the markets prior to others to capitalize on short-term market inefficiencies. Aggressive HFT Index tracks aggressive HFT activity in real time and has developed statistical insights into aggressive HFT behavior, some of which are summarized in this article.

Among the S&P 500 stocks, for instance, aggressive HFTs are more prevalent in equities with
a) Higher prices
b) Lower dividend yield
c) Higher volatility, measured as a standard deviation of daily returns

On average, a $100-dollar difference in stock prices attracted 3% more aggressive HFTs by volume. Not surprisingly, Google (NASDAQ:GOOGL) is the stock with the highest average participation of aggressive HFTs registering close to 38% average daily aggressive HFT participation in 2014. With a stock price well over $500 per share at the time this article was written, Google is one of the most expensive issues comprising the S&P 500. An explanation for the phenomenon may lie in the value of fixed costs of trading relative to prices of stocks: for high-priced stocks, fixed costs account for a smaller percentage of the gains, allowing the traders to keep a larger share of the gains.

Stocks with lower dividend yields also attract aggressive HFTs. On average, a 1% decrease in dividends accounts for a 1.1% increase in aggressive HFT participation, and explains 8.5% of variation in aggressive HFT participation among the S&P 500 stocks. Companies paying high dividends tend to be mature businesses and may detract aggressive HFTs seeking a volatile environment. This suggests that firms may be able to manage aggressive HFT participation in their stocks by adjusting their dividend policy, in conjunction with other factors.

Stocks with higher volatility also have a higher proportion of aggressive HFTs. A 1% increase in volatility measured as an annualized standard deviation of daily returns based on closing prices translates into a 0.23% increase in aggressive HFT participation.

It is not immediately clear, however, whether aggressive HFTs seek out high volatility, whether aggressive HFT participation induces higher volatility in stocks, or both. However, a 1% increase in aggressive HFT participation translates into 0.17% in additional annualized volatility across all the stocks in the S&P 500 index. Overall, differences in aggressive HFT participation account for 2% of variation in volatility among all of the S&P 500 stocks. Aggressive HFT participation is, therefore, a highly predictive metric of volatility: in comparison, the celebrated and often-used GARCH model only accounts for 5% of variation in volatility among the same group of stocks. With higher volatility come higher average returns, so, indirectly, aggressive HFT contributes to better performance of stocks. Specifically, a 1% increase in aggressive HFT participation drives up average annualized returns by 0.23% among all of the S&P 500 stocks. Even long-only portfolio managers may want to take aggressive HFT participation into account in order to fine-tune the risk allocations in their portfolios.
The aggressive HFT is here to stay, and understanding its presence is more necessary than ever before. Today, participation of aggressive HFTs can be readily included in portfolio and trading decisions, as well as risk management and, specifically, collateral valuation. As Big Data analytics and computer technology continue to proliferate in Finance, the applications surrounding aggressive HFTs will only expand further.

Steve Krawciw is CEO of Irene Aldridge is Managing Director of Able Alpha Trading, LTD., and and author of High-Frequency Trading: A Practical Guide to Algorithmic Strategies and Trading Systems (2nd edition, Wiley, 2013). Irene Aldridge will present her latest research on the ultra-fast dynamics in the markets at Big Data Finance 2015 conference scheduled to take place at NYU Courant on March 6, 2015.