The topic has sparked investor attention because of the rise of systematic factor investing strategies available via the ETF structure. It seems as if everyone is a “quant” these days, slinging money around like drunken pirates, destroying the price discovery process along the way.
A popular narrative is the following:
There is too much capital chasing the same factors
Frictional costs associated with factor strategies are simply too high to make them profitable after transaction costs.
On #1, we recommend reading this post, which suggests that there is little evidence that factor funds are radically shifting asset prices.
On #2, we’ve addressed the transactions cost debate in fairly great detail in the following pieces:
The summary from our analysis of the research published on the topic is that factor strategies have capacity limits due to transaction costs, but that the capacity on these strategies might be much higher than academics have previously considered. How was that conclusion reached? Simple: practitioners have better data that can more accurately answer the question than academics.(1)
So what’s new on the topic?
We didn’t really think there was much to add at this point until Cliff Asness highlighted that his team at AQR has been hard at work — yet again!
Frazzini, Israel, and Moskowitz, who already have an incredible paper that tackles the question on net-of-cost returns of the well-known asset pricing anomalies, have reframed their original analysis to address the broader question of transaction costs and how their analysis compares to prior work on the subject.
The bottom line up front:
Trading costs are a lot less than previously estimated.
The model for transaction costs estimated from the data outperforms other models proposed in the literature.
The Core Differentiation of This Trading Costs Study: Better Data
A huge driver for trading costs is the price impact, especially for larger investors. For example, if I try to buy $1mm worth of stock in a single day when the average daily value traded in $100,000, it is likely that my order will have a major impact on the price. Historically, getting access to data on market impact costs has been difficult. Fortunately, the authors access a unique dataset of over a trillion dollars of live trades from a large institutional money manager. The execution data is available for over 1.7 trillion dollars in trade value covering nearly 10,000 stocks between 1998 and 2016.
The authors believe that this sample of live trading data can accurately capture the marginal investor’s trading costs with regard to these strategies. The authors also explain that their trading approach is meant to provide liquidity to the market, not be a liquidity taker. In other words, for longer horizon investment strategies the manager chooses to sit near the bid (for buys) and near the ask for sells. An example in the updated paper highlights their approach. Note that the “Limit price” for a buy trade is generally at or below the bid and that executions often take place near the bid (versus the ask).
The authors highlight an obvious caveat of their study:
One caveat of our data is that we only have costs for one particular investor, albeit a large one, that may not generalize to other investors or other trading strategies not deployed by our manager.
So with that setup in mind and an understanding that the results are applicable to a sophisticated patient trader, we can examine the core results.
Trading Costs May Be A Lot Less Than Previously Thought
The authors describe the methodology for calculating trading costs in this paper. They reconstruct long-short anomaly portfolios following the techniques documented in the literature (e.g., SMB, HML, UMD, etc.) and apply trading costs to these portfolios based on their live trading data. The chart below maps out the average impact for US trades over a 24 hour period following the initial trade:
The authors summarize the key finding:
…our estimate of market impact is just under 9 basis points on average for all trades completed within a day. Most (around 85%) of that price impact appears to be permanent, as only 1.26 basis points on average are reversed over the next 24 hours.
The authors also examine the market impact costs across countries. As expected, there are different costs for different countries.
Later in the paper, the authors identify that the key driver behind market impact cost heterogeneity(2) is the size of the trade as a fraction of daily volume traded (which makes sense!).
The key takeaway is that the transaction cost estimates are much lower than previously identified by academic researchers using “TAQ” data:
In particular, the linear cost models employed by Korajczyk and Sadka (2004), Lesmond, Schill, and Zhou (2003), and Novy-Marx and Velikov (2017) imply trading costs that are an order of magnitude larger than our model or live costs suggest.
The best way to “see” this result is via Figure 7, which compares the author’s estimates (“FIM 2017”) against TAQ estimates:
The best way to benchmark the “believability” of the various approaches is to see how the techniques estimate the costs of trading known index portfolios. Here are the estimated costs to trade several well-known index portfolios (S&P 500 and the Russell 2000):
And here is the visual version for those who are number-challenged:
The approaches based on TAQ data are all multiples of the actual ETF costs incurred to trade these indexes, whereas the model using the calibrated version based on data from the paper (labeled “Trade data, FIM”) generate a near-perfect result. Clearly, the authors have identified a better empirical approach to estimating transaction costs.
This Trading Costs Paper is Simply Mind Blowing
The discussion above is merely meant to highlight some of the wavetops. But this paper digs deep on so many important issues for investors/traders. I highly recommend you read the entire body of work. Incredible insights on building better pre-trade models, understanding transaction costs, and really shedding light on an aspect of the market that has simply been out of reach for academic researchers in the past.
Using 1.7 trillion dollars of live trade execution data from a large institutional money manager across 21 developed equity markets over a 19-year period, we measure the real-world tradingcosts and price impact function of a large trader. We provide a novel description of how costs vary across trade type, stock characteristics, trade size, time, and exchanges globally to test various theories of price impact. We find actual tradingcosts to be an order of magnitude smaller than previous studies suggest, and describe the tradingprocess leading to these costs. A model calibrated to match the distribution of actual costs across trade size, stocks, and time outperforms other models from the literature in out of sample tests that attempt to describe independent costs from brokers and realized costs of traded index funds. Our model based on realized costsfrom live trades portrays very different implementation costs than previous studies suggest.
There is always a concern that practitioners have a conflict of interest when presenting findings that go in their favor. There is no way to completely eliminate this criticism except to say that practitioners are heavily incentivized to maintain a brand/reputation, which might counteract their conflict of interests.
After serving as a Captain in the United States Marine Corps, Dr. Gray earned an MBA and a PhD in finance from the University of Chicago where he studied under Nobel Prize Winner Eugene Fama. Next, Wes took an academic job in his wife’s hometown of Philadelphia 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 firm dedicated to an impact mission of empowering investors through education. He is a contributor to multiple industry publications and regularly speaks to professional investor groups across the country. Wes has published multiple academic papers and four books, including Embedded (Naval Institute Press, 2009), Quantitative Value (Wiley, 2012), DIY Financial Advisor (Wiley, 2015), and Quantitative Momentum (Wiley, 2016).
Dr. Gray currently resides in Palmas Del Mar Puerto Rico with his wife and three children. He recently finished the Leadville 100 ultramarathon race and promises to make better life decisions in the future.
Performance figures contained herein are hypothetical, unaudited and prepared by Alpha Architect, LLC; hypothetical results are intended for illustrative purposes only. Past performance is not indicative of future results, which may vary. There is a risk of substantial loss associated with trading stocks, commodities, futures, options and other financial instruments. Full disclosures here.