Trading with Algorithms in the Environment

///Trading with Algorithms in the Environment

Trading with Algorithms in the Environment

By | 2017-01-18T11:41:43+00:00 February 3rd, 2014|Uncategorized|1 Comment
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(Last Updated On: January 18, 2017)

Trading on Algos


This paper studies the impact of algorithmic trading (AT) on asset prices. We find that the heterogeneity of algorithmic traders across stocks generates predictable patterns in stock returns. A trading strategy that exploits the AT return predictability generates a monthly risk-adjusted performance between 50-130 basis points for the period 1999 to 2012. We find that stocks with lower AT have higher returns, after controlling for standard market-, size-, book-to-market-, momentum, and liquidity risk factors. This effect survives the inclusion of many cross-sectional return predictors and is statistically and economically significant. Return predictability is stronger among stocks with higher impediments to trade and higher predatory/opportunistic algorithmic traders. Our paper is the first to study and establish a strong link between algorithmic trading and asset prices.

Data Sources:

TAQ 1999-2012.

Alpha Highlight:

Focus on low algo-trading stocks:


The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.


Strategy Summary:

  1. This paper examines the negative relation between algorithmic trading (AT) and stock returns.
  2. First, it constructs a proxy for AT using public Trade and Quote (TAQ) data from 5,978 stocks samples from Jan 1999 to Oct 2012.
    • The AT measure is computed each month by dividing the monthly number of stock quotes by the number of trades for each stock.
  3. Stocks are divided into different portfolios based on AT.
    • It runs regression on risk adjusted returns (3, 4 and 5 factor models) and finds that low-AT portfolio outperforms the high-AT portfolio (Table 4).
    • The long/short portfolio that goes long low-AT stocks and goes short high-AT stocks generates an alpha between 0.50% and 1.30%.
    • Tables 5 and 6 shows that the AT effect is not temporary and but very persistent.
  4. Two potential explanations for this AT effect:
    1. Humans have a cognitive inability to execute their trades efficiently and quickly. Thus, high AT-firms (which quickly parses new information) can potentially reduce delays in information diffusion, and thus have lower return.
      • Paper finds little support for this theory.
    2. Heterogeneity of algorithmic traders: some algorithmic traders are liquidity providers or market makers, while other algorithmic traders follow arbitrage, order anticipation, and momentum ignition trading strategies (“opportunistic” or “predatory” strategies).
      • Evidence suggests that the AT effect is likely driven by the heterogeneity of algorithmic traders, as the the predatory algorithms are more prevalent and have higher returns in stocks with more institutional ownership.

Strategy Commentary:

  • The role of AT in today’s financial market is important, about 53% of U.S. daily equity trading volume is done via AT.
  • AT effect is stronger among stocks with lower market capitalization and stocks with higher transaction costs.
  • Effect is strongest in illiquid stocks (alpha increases to 180 basis points a month).
    • Would be nice to see the returns on the long and short books.  If the alpha is mainly located in the illiquid short book, the strategy may not be tradable.

Earn Higher Returns in Low AT, but expect to get picked off by the machines!

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About the Author:

After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes,, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.