Swedroe: Trading Cost Truths

In our book, “Your Complete Guide to Factor-Based Investing,” Andrew Berkin and I establish five criteria (persistence, pervasiveness, robustness to various definitions, implementability, and intuitive risk- or behavioral-based explanations for why we should expect performance to persist) that allowed us to reduce John Cochrane’s “zoo” of factors from the roughly 600 in the literature to just a small number.

The factors we recommend investors consider when designing portfolios are market beta, size, value, momentum, profitability/quality, carry and momentum (both cross-sectional and time-series).

The role of liquidity is an important one, as, while there may be anomalies that result in mispricings, to profit from those anomalies, investors must be able to exploit them after taking into account all the expenses of the effort. Andrea Frazzini, Ronen Israel and Tobias Moskowitz contribute to the literature on implementation costs with their April 2018 study, “Trading Costs.”

Best Trading Costs Paper

In his review of the paper, Alpha Architect’s Wes Gray called it the “best research paper ever written on trading costs.” The study’s database consisted of $1.7 trillion of live-executed equity trades from a large money manager, AQR Capital Management. It covered the period August 1998 to June 2016, as well as 21 developed equity markets and almost 10,000 stocks.

Frazzini, Israel and Moskowitz measured the real-world trading costs and price-impact function incurred by AQR, which trades portfolios based on many of the anomalies discovered in the academic literature. Thus, the study provides a unique look into how trading costs vary globally across trade type, size and exchange.

The authors identified the real-time price impact of a trade at various trade sizes. They also observed whether the trade was a buy or sell, the market price at trade initiation, the amount traded, and the execution price for each share traded. This data allowed them to calculate a precise measure of price impact. They were also able to differentiate between types of trades (e.g., to initiate a position; that is, to buy long or sell short versus to cover a position; that is, to sell long or buy to cover), which is unique to the study and provides a more accurate picture of real trading costs to long/short portfolios common in the literature.

It’s important to note AQR’s trades were all made in a manner seeking to lower execution costs using a proprietary trading algorithm that, importantly, does not make any buy or sell decisions. The algorithm decides how patiently to trade (minutes versus days), but not what to trade (or not to trade). In the authors’ sample, the average realized trade horizon for completion was slightly less than one day; 99% of trades were executed within three days; and the maximum trade horizon was 9.8 days. All trades from short-term (daily or intraday) signal/models were excluded from the analysis.

The authors explain: “The trading algorithm directly and anonymously accesses market liquidity through electronic exchanges and, in order to minimize market impact, tries to provide rather than demand liquidity by not demanding immediacy using a system of limit orders (with prices generally set to buy at the bid or below and sell at the ask or above) that dynamically break up total orders (parent orders) into smaller orders (child orders), where both the sizes of child orders and the time in which they are sent are randomized.”

Study Results

Following is a summary of their findings:

• Trading costs (bid/offer spreads and commissions), including market impact costs, have exhibited a steady decline over time across markets, though they did jump during the financial crisis (2007-2009) before resuming their decline. Some of the decline is driven by technological events, such as moving to decimalization in traded prices.
• The average bid/ask spread at the time of order arrival is 21.33 basis points (bps). However, it was rare for AQR’s trades to incur the full spread, or even half the spread, because of the passive limit orders. The main cost the firm’s trades faced was market (price) impact.
• The estimate of market impact is just under 9 bps on average for all trades completed within a day. The median cost is a bit lower, at just more than 6 bps, suggesting trading costs are positively skewed by more expensive trades.
• When weighting trades by their dollar value, the value-weighted mean is higher, at just more than 15 bps, for market impact. The largest trades are the most expensive trades.
• Costs are larger for smaller stocks and stocks with greater idiosyncratic risk, consistent with theories of market-maker inventory risk raising price impact. Without controlling for trade size, the average large-cap stock trade generates almost 9 bps of market impact costs compared to almost 19 bps for small-cap stocks.
• Buying to go long generates about 12.5 bps of price impact, while buying to cover has 15.5 bps of price impact. Short-selling is slightly more expensive, by 0.6 bps, on average, than selling long, but the difference is not statistically significant. There is no marked difference in trading costs between selling a long position versus selling short. If short-selling is indeed costlier, it is likely to be a function of opportunity cost (i.e., not being able to short) or of lending fees for stocks on special.
• The most important variable determining price impact is the size of the trade, measured as the fraction of daily volume traded in a stock, where larger trades generate greater price impact. The relationship between price impact and trade size is nonlinear, with impact rising with trade size at a decreasing rate.
• More volatile firms have higher transaction costs, and more volatile market environments also are associated with larger price impact costs.
• The average trade experiences an additional 4 bps of market impact on the stocks traded relative to stocks of similar characteristics (book-to-market ratio, market cap and momentum) not traded by the algorithm that day. This likely is due to immediacy of demand and, thus, a temporary outcome that will be reversed.
• The patterns and estimates were similar across the 21 different equity markets studied.

Based on their findings, Frazzini, Israel and Moskowitz built a market-impact cost model to estimate the cost of trading live funds based on passive indexes. Examining Vanguard’s S&P 500 index fund and BlackRock’s iShares Russell 2000 ETF (IWM), the model predicts their costs accurately, suggesting their cost estimates are in line with other large traders.

The authors also collected trading cost data from three different brokers (ITG, Deutsche Bank and J.P. Morgan) and a consulting firm (Ancerno, formerly Abel-Noser) that collectively cover trades from more than 2,000 institutions across 2,000 brokers globally. They found their estimates match average costs across trade size, time and country from these other sources.

The authors then compared their estimates of trading costs with various other models in the literature and found that the estimates of costs produced by their model “are much closer to real-world trading costs facing a large trader and match those from other sources.”

Not only were they much closer, they were much lower than the cost estimates from other models. For example, Frazzini, Israel and Moskowitz found their estimate of annual costs on the S&P 500 is 4.81 bps, almost exactly what they obtained by looking at the returns of Vanguard’s 500 Index Fund. For the Russell 2000, they estimated 12.36 bps, which also matches the costs of the iShares ETF.

It is important to note that the authors’ cost estimates represent those of the average sophisticated institutional trader, who serves as an arbitrageur in markets, and therefore more closely resemble the costs of the marginal investor. They do not represent the costs of retail investors, or even the average investor.

Key Implications

The preceding findings have important implications for investors. First, they provide evidence allowing investors to estimate how various trading strategies based on asset-pricing anomalies survive trading costs at different fund sizes.

Second, they showed that trading costs for strategies based on a host of anomalies are substantially lower than the costs suggested in the literature, at least for patient institutional traders.

Third, because trading costs are much lower than previously thought, while factor-based strategies have capacity limits due to transaction costs, their capacity on these strategies might be much higher than academics have previously considered. (The authors constructed long/short anomaly portfolios following the techniques documented in the literature—e.g., SMB, HML, UMD, etc.—and applied trading costs to these portfolios based on their live trading data.) Finally, in the interest of full disclosure, my firm, Buckingham Strategic Wealth, recommends AQR funds in constructing client portfolios.

Larry Swedroe is the director of research for The BAM Alliance, a community of more than 140 independent registered investment advisors throughout the country.