When choosing a factor-based strategy, advisors should carefully scrutinize the fund’s construction rules (e.g., the number of securities held) and implementation strategy (e.g., the frequency of rebalancing and the use of patient, algorithmic trading).
To understand the importance of this issue, let’s go back to our book, “Your Complete Guide to Factor-Based Investing,” in which Andrew Berkin and I laid out five criteria that must be met before considering investing in a factor. The factor must show evidence of being a unique/independent source of risk that has generated a premium that is:
- Persistent – It holds across long periods of time and different economic regimes.
- Pervasive – It holds across countries, regions, sectors and even asset classes.
- Robust – It holds for various definitions (for example, there is a value premium, whether it is measured by price-to-book, earnings, cash flow or sales).
- Investable – It holds up not just on paper but also after considering actual implementation issues, such as trading costs.
- Intuitive – There are logical risk-based or behavioral-based explanations for its premium and why it should continue to exist.
We then laid out the evidence that, among the “zoo” of equity factors documented in the academic literature, only a handful pass all the tests: market beta, size, value, momentum and profitability/quality. It is particularly important to consider transactions costs (investability) because market impact costs may substantially erode a strategy's expected excess returns. Transactions costs are impacted by not only turnover (due to the need to rebalance portfolios) but also concentration of turnover and thus demands on liquidity.
Feifei Li, Tzee-man Chow, Alex Pickard and Yadwinder Garg contribute to the literature with their study “Transaction Costs of Factor-Investing Strategies,” which appears in the Spring 2019 issue of the Financial Analysts Journal. The authors evaluated 15 popular factor investing strategy implementations and identified how index construction methods, when thoughtfully designed, can reduce market impact costs.
The authors began by noting: “It stands to reason that excess returns grow scarcer as AUM rises: managers must buy more of the stocks in their opportunity set, creating upward price pressure that inexorably lowers the expected return. Conversely, when they exit existing positions, their trading generally pushes prices down, reducing the realized return.” They added: “Impressive backtest returns may be achieved by holding concentrated portfolios with high turnover rates, and strategies that require specific selection criteria can exhibit these characteristics. A backtest is not an accurate representation of an investor’s experience because it only simulates the history of a strategy and does not incur actual asset-related trading activity and associated costs.”
To determine the impact of implementation costs, the authors analyzed the behavior of stocks that were traded during the rebalancing of 49 FTSE RAFI™ Indexes, which represents live histories and approximate total assets under management (AUM) of $8 billion in 2009 to $74 billion in 2016. Following is a summary of their findings:
- There is significant evidence of market impact on the rebalancing day and a subsequent price reversal over the next four days, costs induced by the orders of the indexers.
- The magnitude of price impact is predictable because it is directly related to the security’s liquidity and the size of the trade.
- A fund incurs approximately 30 basis points (bps) of trading costs due to market impact for every 10% of a stock’s average daily volume (ADV) traded in aggregate by the factor investing index-tracking funds.
- With as little as $10 billion in AUM, momentum indexing strategies can have trading costs of 200 bps or more.
- Dividend income and dividend growth strategies also incur high costs due to high concentration, leading to high market impact costs. Costs are in the 60-80 bps range. On the other hand, quality strategies’ costs fall below 40 bps and value strategies’ below 30 bps. The results were similar in international markets.
- Mixing factors together diversifies the holdings and reduces turnover concentration.
- Factor strategies that invest in small subsets of the market (such as momentum) tend to have high turnover concentration because they routinely require that the manager completely eliminate a few existing positions and buy into new positions. Broad market indexes that reweight constituents back to predetermined and stable weights tend to have lower turnover concentration.
- Strategies that rebalance more frequently, such as quarterly versus annually, will tend to have lower turnover concentration.
Intelligent design improves investor outcomes
The research focused on the implementation costs of pure indexing (which focuses on the minimization of tracking error). We know that fund construction rules and implementation rules can matter greatly, with intelligent design and implementation able to reduce costs. For example, trading costs can be reduced through less frequent rebalancing and less concentration (both of which have the negative effect of reducing factor exposure, however). Costs can also be reduced through patient algorithmic trading and by avoiding trading on index reconstitution dates, while accepting the risk of what should be random tracking error. For example, Li et al. noted: “The number of days over which a single trade is executed also affects cost.” Spreading the trading out over a number of days while accepting random tracking error not only reduces costs but also results in much larger capacities than estimated by the authors. They acknowledged this, noting that their estimates ignored the fact that their “market impact model potentially overestimates the cost of very large changes in positions, because we assume all portfolio managers rebalance a given indexing strategy on the same trade day. Experienced managers who care about trading costs are unlikely, however, to place orders on an exchange for multiples of the underlying security’s average daily volume.”
Real-world evidence
The ability to add value through intelligent design and trading was demonstrated by Andrea Frazzini, Ronen Israel and Tobias Moskowitz in their April 2018 study “Trading Costs.” 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 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, they were able to identify 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 also were able to differentiate between types of trades (to initiate a position, i.e., to buy long or sell short, versus cover a position, i.e., 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.
AQR’s trades were all made in a manner seeking to lower execution costs using a proprietary trading algorithm that does not make any buy or sell decisions. The algorithm decides how patient 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.”
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 that 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.
- 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.
- 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.
- 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, the model predicts their costs accurately, suggesting their cost estimates are in line with other large traders.
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.
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.
Summary
The tremendous growth in assets under management in factor-based strategies raises the question of how the growth impacts implementation costs and the returns investors earn. The Li et al. study highlights the issue as well as the importance of not focusing solely on the expense ratio of a fund. As they showed, implementation costs can far exceed the fund’s expense ratio, making it critical that investors consider not only the factor exposure provided by a fund and its expense ratio, but also the fund’s construction rules and implementation strategies. Intelligent design and trading can add significant value.
Not all factor strategies are created equal. Even those targeting the same factors are not commodities (substitutes for each other). As Li et. al. noted: “Transaction costs, including implicit market impact costs, are a key element in determining the returns that investors actually earn.” Remember this when evaluating which funds you choose to gain exposures to factors.
Larry Swedroe is the director of research for The BAM Alliance, a community of more than 130 independent registered investment advisors throughout the country.
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