Research Affiliates Says This Is the Best Way to Do Factor Investing Two executives at the asset management firm lay out their “optimal” approach to building a multi-factor portfolio. Amy Whyte April 26, 2019 Illustration by II The best-performing multi-factor portfolio, according to Research Affiliates, is one that includes the momentum factor. The momentum factor — the basis of an investment strategy that has been criticized for having high trading costs that detract from actual returns — is one of six factors that a pair of Research Affiliates executives included in an “optimal” portfolio designed to deliver the best risk-adjusted returns for its cost. “Neither a focus on maximizing paper portfolio performance, which ignores the associated trading costs, nor a singular focus on low-cost implementation, which misses opportunities for better performance, will produce an optimal result for multi-factor smart beta investors,” wrote the authors of a new Research Affiliates paper on multi-factor strategy design. The authors — head of investment strategy Feifei Li and vice president for smart beta Joseph Shim — analyzed many different multi-factor portfolios to find “the most advantageous balance between effectively harvesting the factor premium and implementation cost.” The best portfolio, they concluded, was one which invested in roughly a quarter of the investable universe based on six investment factors: value, low beta, profitability, investment, momentum, and size. Including momentum reduced the portfolio’s tracking error by 84 basis points and improved its information ratio from 0.46 to 0.57. And despite the more frequent rebalancing which accompanies the inclusion of the momentum factor, Li and Shim found that adding momentum to a $10 billion portfolio actually reduced trading costs by a single basis point. What Institutional Investors Think About OCIOs[/paste:font] “Because momentum is associated with more liquid stocks, the additional liquidity compensates for the increased turnover,” they explained, adding that the low or negative correlations between momentum and the other factors results in trades which “cancel out trades initiated by value” or other factors. [II Deep Dive: Rob Arnott Says to Avoid These Blunders in Factor Investing] The inclusion of the size factor — whose validity AQR researchers recently been questioned — boosted the $10 billion portfolio’s return by 26 basis points while lowering trading costs by 7 basis points. Although the size factor made the portfolio slightly more volatile, its lower correlation with other factors reduced the portfolio’s tracking error, resulting in a higher information ratio. “Because the diversification benefit outweighs the increased cost of implementation, we recommend that investors include both the momentum and size factors in a multi-factor strategy,” Lei and Shim wrote. Beyond selecting their six factors, the Research Affiliates duo also looked at how different levels of stock concentration would affect cost and performance of portfolios ranging from $1 billion to $10 billion in size. They found that risk-adjusted returns improved as the portfolio became more concentrated — up to a point. Once the underlying factor portfolios reached the limit of 25 percent of the investable universe, the Sharpe ratio stopped climbing. Since the more concentrated portfolios also resulted in higher trading costs, especially in larger portfolios, the authors concluded that the best concentration level was around 25 percent. search Affiliates Says This Is the Best Way to Do Factor Investing Two executives at the asset management firm lay out their “optimal” approach to building a multi-factor portfolio. Amy Whyte April 26, 2019 Illustration by II The best-performing multi-factor portfolio, according to Research Affiliates, is one that includes the momentum factor. The momentum factor — the basis of an investment strategy that has been criticized for having high trading costs that detract from actual returns — is one of six factors that a pair of Research Affiliates executives included in an “optimal” portfolio designed to deliver the best risk-adjusted returns for its cost. “Neither a focus on maximizing paper portfolio performance, which ignores the associated trading costs, nor a singular focus on low-cost implementation, which misses opportunities for better performance, will produce an optimal result for multi-factor smart beta investors,” wrote the authors of a new Research Affiliates paper on multi-factor strategy design. The authors — head of investment strategy Feifei Li and vice president for smart beta Joseph Shim — analyzed many different multi-factor portfolios to find “the most advantageous balance between effectively harvesting the factor premium and implementation cost.” The best portfolio, they concluded, was one which invested in roughly a quarter of the investable universe based on six investment factors: value, low beta, profitability, investment, momentum, and size. Including momentum reduced the portfolio’s tracking error by 84 basis points and improved its information ratio from 0.46 to 0.57. And despite the more frequent rebalancing which accompanies the inclusion of the momentum factor, Li and Shim found that adding momentum to a $10 billion portfolio actually reduced trading costs by a single basis point. What Institutional Investors Think About OCIOs[/paste:font] “Because momentum is associated with more liquid stocks, the additional liquidity compensates for the increased turnover,” they explained, adding that the low or negative correlations between momentum and the other factors results in trades which “cancel out trades initiated by value” or other factors. [II Deep Dive: Rob Arnott Says to Avoid These Blunders in Factor Investing] The inclusion of the size factor — whose validity AQR researchers recently been questioned — boosted the $10 billion portfolio’s return by 26 basis points while lowering trading costs by 7 basis points. Although the size factor made the portfolio slightly more volatile, its lower correlation with other factors reduced the portfolio’s tracking error, resulting in a higher information ratio. “Because the diversification benefit outweighs the increased cost of implementation, we recommend that investors include both the momentum and size factors in a multi-factor strategy,” Lei and Shim wrote. Beyond selecting their six factors, the Research Affiliates duo also looked at how different levels of stock concentration would affect cost and performance of portfolios ranging from $1 billion to $10 billion in size. They found that risk-adjusted returns improved as the portfolio became more concentrated — up to a point. Once the underlying factor portfolios reached the limit of 25 percent of the investable universe, the Sharpe ratio stopped climbing. Since the more concentrated portfolios also resulted in higher trading costs, especially in larger portfolios, the authors concluded that the best concentration level was around 25 percent.
The best-performing multi-factor portfolio, according to Research Affiliates, is one that includes the momentum factor. That's about the only technical factor that has worked over time. From "What Works on wall Street"... 15 C H A P T E R RELATIVE PRICE STRENGTH: WINNERS CONTINUE TO WIN It may be that the race is not always to the swift, nor the battle to the strong—but that’s the way to bet. —Damon Runyon “Don’t fight the tape.” “Make the trend your friend.” “Cut your losses and let your winners run.” These Wall Street maxims all mean the same thing—bet on price momentum. Of all the beliefs on Wall Street, price momentum makes efficient market theorists howl the loudest. The defining principle of their theory is that you cannot use past prices to predict future prices. A stock may triple in a year, but according to efficient market theory, that will not affect next year. Efficient market theorists also hate price momentum because it is independent of all accounting variables. If buying winning stocks works, then stock prices have “memories” and carry useful information about the future direction of a stock. In his book, The Wisdom of Crowds: Why the Many Are Smarter than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations, James Surowiecki argues that “under the right circumstances, groups are remarkably intelligent, and are often smarter than the smartest people in them.” Surowiecki says that if four conditions are met, a crowd’s “collective intelligence” will prove superior to the judgments of a smaller 221 group of experts. The four conditions are (1) diversity of opinion; (2) independence of members from one another; (3) decentralization; and (4) a good method for aggregating opinions. He then goes on to list several accounts in which crowds were far more accurate than any individual member trying to make a correct forecast. Generally speaking, these four conditions are present in market-based price auctions, with the final price of a stock serving as an aggregator of all market opinion about the prospects for that stock. The only times this is not true is when markets are either in a bubble or a bust. At these market extremes, a uniformity of opinion occurs that impairs the ability of a group to offer good collective judgment. Conversely, another school of thought says you should buy stocks that have been most battered by the market. This is the argument of Wall Street’s bottom fishers, who use absolute price change as their guide, buying issues after they’ve performed poorly. If Surowiecki is correct, this type of approach would only work after a bubble or bust, when the collective wisdom got the answer wrong. Let’s see who is right. THE RESULTS We’ll look at buying those 50 stocks having the best and the worst one-year price changes from both the All Stocks and Large Stocks universes. This will contrast the results of buying last year’s biggest winners with last year’s biggest losers. We’ll also separate the stocks by decile for both universes. Let’s look at the winners first. (In this and future chapters, I’ll use the terms “relative strength” and “price appreciation” interchangeably. Stocks with the best relative strength are the biggest winners in terms of their previous year’s price appreciation.) Starting on December 31, 1951, we’ll buy those 50 stocks having the largest price appreciation from the previous year (Figure 15-2). I arrive at this number by dividing this year’s closing price by that from the previous 12 months. Thus, if XYZ closed this year at 10 and last year at 2, it would have a gain of 400 percent and a price index of 5 (10 divided by 2). A $10,000 investment on December 31, 1951 in those 50 stocks from All Stocks having the best one-year price appreciation is worth $4,814,164 at the end of 2003, a compound return of 12.61 percent a year (Table 15-1). This is the first fairly significant reversal of a factor’s performance since the publication of this book in 1997. Originally, buying the stocks with the best performance from the All Stocks universe did better than All Stocks, but with much higher volatility. Due to the market bubble in 2000, and the three horrible years that ensued, we see them faring slightly worse than the 13 percent return from the All Stocks universe. The volatility over the period since I last published this book was extraordinary—from February 1997 through February 2000, the 50 stocks having the best one-year price appreciation soared by nearly 500 percent, only to turn around and plunge over 89 percent during the next three years! The performance of those 50 stocks from All Stocks having the best oneyear price appreciation also had extraordinarily high risk. The standard deviation of return for the 50 best one-year price performers was 37.82 percent, the highest we’ve seen for an individual factor. The enormous risk pushed the Sharpe ratio to 35, well below All Stocks’ 46 (Figure 15-4). When examining deciles, we’ll see that performance is increased and risk is reduced when focusing on the top 10 percent of stocks by price appreciation. http://csinvesting.org/wp-content/uploads/2015/02/What-Works-on-Wall-Street-Third-Edition.pdf