The analysis above suggests that portfolios that include or exclude emerging allocations are roughly the same. For some readers, this may be a surprise, but for many readers, this may not be "news." That said, even if the data don't strictly justify an Emerging allocation, the first principle of "stay diversified" might be enough to make an allocation.
Of course, the assumptions always matter.
Since the 1992 publication of “The Cross-Section of Expected Stock Returns” by Eugene Fama and Kenneth French factor-based strategies and products have become an integral part of the global asset management landscape. While “top-down” allocation to factor premiums (such as size, value, momentum, quality, and low volatility) has become mainstream, questions remain about how to efficiently gain exposure to these premiums. Today, many generic factor products, often labeled as “smart beta”, completely disregard the impact of other factors when constructing portfolios with high exposures to any single factor. However, recent research, such as 2019 study “The Characteristics of Factor Investing” by David Blitz and Milan Vidojevic, has shown that single-factor portfolios, which invest in stocks with high scores on one particular factor, can be suboptimal because they ignore the possibility that these stocks may be unattractive from the perspective of other factors that have demonstrated that they also have higher expected returns.
We document a striking pattern in U.S. and international stock returns: double sorting on the previous month’s return and share turnover reveals significant short-term reversal among low-turnover stocks, whereas high-turnover stocks exhibit short-term momentum. Short-term momentum is as profitable and as persistent as conventional price momentum. It survives transaction costs and is strongest among the largest, most liquid, and most extensively covered stocks. Our results are difficult to reconcile with models imposing strict rationality but are suggestive of an explanation based on some traders underappreciating the information conveyed by prices.
Investors care about more than just returns. They also care about risk. Thus, prudent investors include consideration of strategies that can provide at least some protection against adverse events that lead to left tail risk (portfolios crashing). The cost of that protection (the impact on expected returns) must play an important role in deciding whether to include them. For example, buying at-the-money puts, a strategy that eliminates downside risk, should have returns no better than the risk-free rate of return, making that a highly expensive strategy.
A large body of evidence demonstrates that momentum, including time-series momentum (trend following), has improved portfolio efficiency. Research has found that there are a few ways to improve on simple trend-following strategies. Techniques that have been found to improve Sharpe ratios and reduce tail risk include volatility scaling and combining fast and slow signals as well as combining long-term reversals. These have been incorporated by many fund managers into investment strategies. Cheng, Kostyuchyk, Lee, Liu and Ma provided evidence that machine learning could be used to further improve results. With that said, a word of caution on the use of machine learning is warranted. The powerful tools and the easy access to data now available to researchers create the risk that machine learning studies will find correlations that have no causation and thus the findings could be nothing more than a result of torturing the data. To minimize that risk, it is important that findings not only have rational risk- or behavioral-based explanations for believing the patterns identified will persist in the future, but they also should be robust to many tests. In this case, investors could feel more confident in the results if their findings were robust to international equities and other asset classes (such as bonds, commodities and currencies).
This was a simple question posed to me by one of our blog readers--what impact does excluding stocks trading at 10x P/S have on a Momentum portfolio? A good question--especially for those who are "value" investors that are interested in momentum. For most systematic value investors, the prospect of adding stocks trading at over 10x P/S sounds ludicrous. Since I didn't know the exact impact, I went and ran the tests described below.
Value and momentum are two of the most powerful explanatory factors in finance. Research on both has been published for about 30 years. However, it was not until recently that the two had been studied in combination and across markets. Bijon Pani and Frank Fabozzi contribute to the literature with their study “Finding Value Using Momentum,” published in The Journal of Portfolio Management Quantitative Special Issue 2022, in which they examined whether using six value metrics that have an established academic background combined with the trend in relative valuations provide better risk-adjusted returns than Fama-French’s traditional HML (high minus low book-to-market ratio) factor. The value metrics chosen were book value-to-market value; cash flow-to-price; earnings before interest, taxes, depreciation, and amortization (EBITDA)-to-market value; earnings-to-price; profit margin-to-price; and sales-to-price. Using six different measures provides tests of robustness, minimizing the risk of data mining. However with so many dials to turn there is a risk of achieving positive returns that aren't material or achieving postive results with the potential for overfitting.
The intuition behind betting against beta is that leverage-constrained investors, instead of applying leverage, obtain an expected return higher than the market’s expected return through overweighting high-beta stocks and underweighting low-beta stocks in their portfolios. Their actions lower future risk-adjusted returns on high-beta stocks and increase future risk-adjusted returns on low-beta stocks. We take a deeper look into this idea.
We recently hosted our Democratize Quant Conference (sign up here for updates).
This post is a recap of what we heard and some resources we can make available to the public.
We will be hosting our 5th annual Democratize Quant conference later this month via Zoom. The event is 100% free but we do screen participants to enforce our "no spammers" policy. https://alphaarchitect.com/democratizequant/
How well do quantitative investors navigate around the changes to the accounting standards that are endemic to the financial data used in quantitative strategies? The numbers reported on financial statements are wholly governed by regulation and by each firm’s interpretation of those accounting standards. So how do quants stick to their empirical evidence on old data methods or do they react in terms of the strategy when the change in standards is material?
From 2017 through March 2020, the relative performance of value stocks in the U.S. was so poor, experiencing its largest drawdown in history, that many investors jumped to the conclusion that the value premium was dead. It is certainly possible that what economists call a “regime change” could have caused assumptions to change about why the premium should exist/persist.
Having conducted an inordinate amount of research on the momentum factor, we find it comforting (likely due to confirmation bias!) that independent researchers have identified the same thing we have found -- frog in the pan is a robust way to measure momentum if one is seeking to take advantage of the momentum factor.
The reported results we covered have important implications for investors in terms of portfolio construction, risk monitoring, and manager selection. Because these common factors explain almost all the returns of bond portfolios, investors should construct their bond portfolios using low-cost, passively (systematically) managed funds with these factors in mind and then carefully monitor their exposure to these systematic risks.
The main takeaway for investors is that Kelly, Moskowitz, and Pruitt demonstrated that past return characteristics are strongly predictive of a stock’s realized exposures to common risk factors, providing direct evidence that price trend strategies are in part explainable as compensation for common factor exposures—past returns predict betas on factors and those factors have high average returns.
In theory green stocks should have lower expected returns, this however, is not what we've seen. So the question is what has caused the outperformance of green stocks? And has that outperformance cost value investors their returns?
Ben and Cameron, which host the excellent Rational Reminder podcast, sit down with Jack Vogel and go through a laundry list of factor investing questions
Momentum, Reversals, and Investor Clientele Andy Chui, Avanidhar Subrahmanyam, and Sheridan TitmanReview of Financial Studies, 2021A version of this paper can be found hereWant to read [...]
A large body of evidence, including the studies “Is There Momentum in Factor Premia? Evidence from International Equity Markets,” Factor Momentum Everywhere (Summary)” and “Factor [...]
From 2017 through March 2020, the relative performance of value stocks in the U.S. was so poor, experiencing its largest drawdown in history, that many [...]
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