The article aims to explore the possibility that changes in fundamentals play a role in the attenuation of stock market anomalies, offering an alternative explanation to the prevailing arbitrage-based explanation
Summary: no difference in average returns between large-cap and small-cap portfolios. There you have it. The small-cap sacred cow has been slaughtered.
We recently hosted our 6th Annual Democratize Quant Conference. This post is a recap of what we heard and some resources we can make available to the public.
John Campbell, Stefano Giglio, and Christopher Polk, authors of the March 2023 study “What Drives Booms and Busts in Value?,” sought to determine which factors drive value’s booms and busts. They interpreted the returns to the standard value strategy through the lens of Robert Merton’s intertemporal CAPM (ICAPM).
Eric Balchunas, Cliff Asness, Kai Wu, Que Nguyen, Corey Hoffstein
Factor strategies need to be rebalanced in order to maintain their factor exposure. But different factors decay at different rates and this affects how they should be rebalanced. For example, momentum needs to be rebalanced more than value. This study digs into these questions.
On this week's episode, Isaiah is joined by expert Dr. Wesley Gray, CEO of Alpha Architects, to discuss the concepts of value ???? investing.
We believe owning deep-value stocks is potentially interesting at these valuation peaks. But as I said in the previous two times I wrote this, the spread can get more extreme. At some point, we'd like to stop talking about the valuation spread and its potential effect on forward expected returns...and see that spread COMPRESS!
Wide divergences between the valuations of cheap stocks relative to expensive stocks have preceded significant outperformance for value over the subsequent decade, as shown in this figure.
Given that valuations provide information on equity returns, it should not be surprising to learn that valuation spreads provide information on future factor premiums.
How do you separate the signal from the noise? To have confidence that a factor premium, or strategy, isn’t just the result of data mining - a lucky/random outcome - we recommended that you should require evidence that the premium has been not only persistent over long periods of time and across economic regimes, but also pervasive across sectors, countries, geographic regions and even asset classes; robust to various definitions (for example, there has been both a value and a momentum premium using many different metrics); survives transactions costs; and has intuitive risk- or behavioral-based explanations for the premium to persist.
Jules van Binsbergen, Liang Ma and Michael Schwert, authors of the September 2022 study “The Factor Multiverse: The Role of Interest Rates in Factor Discovery,” posed an interesting question: Are the findings of at least some of the reported anomalies the direct result of the 40-year secular decline in global interest rates and thus not really anomalies?
We study the cross-section of stock returns using a novel constructed database of U.S. stocks covering 61 years of independent data.
Managed portfolios that exploit positive first-order autocorrelation in monthly excess returns of equity factor portfolios produce large alphas and gains in Sharpe ratios. We document this finding for factor portfolios formed on the broad market, size, value, momentum, investment, prof- itability, and volatility. The value-added induced by factor management via short-term momentum is a robust empirical phenomenon that survives transaction costs and carries over to multi-factor portfolios. The novel strategy established in this work compares favorably to well-known timing strategies that employ e.g. factor volatility or factor valuation. For the majority of factors, our strategies appear successful especially in recessions and times of crisis.
The book-to-market ratio has been widely used to explain the cross-sectional variation in stock returns, but the explanatory power is weaker in recent decades than in the 1970s. I argue that the deterioration is related to the growth of intangible assets unrecorded on balance sheets. An intangible-adjusted ratio, capitalizing prior expenditures to develop intangible assets internally and excluding goodwill, outperforms the original ratio significantly. The average annual return on the intangible-adjusted highminus-low (iHML) portfolio is 5.9% from July 1976 to December 2017 and 6.2% from July 1997 to December 2017, vs. 3.9% and 3.6% for an equivalent HML portfolio
Reschenhofer’s findings demonstrate the important role that portfolio construction rules (such as creating efficient buy and hold ranges or imposing screens that exclude stocks with negative momentum) play in determining not only the risk and expected return of a portfolio but how efficiently the strategy can be implemented (considering the impact of turnover and trading costs)—wide (narrow) thresholds reduce (increase) portfolio turnover and transactions costs, thereby increasing after-cost returns and Sharpe ratios. His findings also provide support for multiple characteristics-based scorings to form long-only factor portfolios, encouraging the combination of slow-moving characteristics (such as value, investment and/or profitability) conditional on fast moving characteristics (such as momentum), to reduce portfolio turnover and transactions cost. Fund families such as AQR, Avantis, Bridgeway and Dimensional use such an approach, integrating multiple characteristics into their portfolios conditional on momentum signals.
We show, using machine learning, that fund characteristics can consistently differentiate high from low-performing mutual funds, as well as identify funds with net-of-fees abnormal returns. Fund momentum and fund flow are the most important predictors of future risk-adjusted fund performance, while characteristics of the stocks that funds hold are not predictive. Returns of predictive long-short portfolios are higher following a period of high sentiment or a good state of the macro-economy. Our estimation with neural networks enables us to uncover novel and substantial interaction effects between sentiment and both fund flow and fund momentum.
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.
A few quick charts for our readers. As we all know, technology-related sectors and names have been crushed. But is blood in the streets? Not really.
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