The Relationship Between the Demand for Leverage and Management Fees
This article discusses what academic research says about the relationship between the demand for leverage and management fees.
High-Frequency Consumer Spending Data and the Cross-Section of Stock Returns
Consumer demand drives the cash flows of consumer-oriented companies. Thus, they should serve as a reliable source of information to predict future fundamentals above and beyond the information contained in financial statements and readily available market data.
The Short-Duration Equity Premium
We examine the short-duration premium using pre-scheduled economic, monetary policy, and earnings announcements. We provide high-frequency evidence that duration premia associated with revisions of economic growth and interest rate expectations are consistent with asset pricing models but cannot explain the short-duration premium. Instead, we show that the trading activity of sentiment-driven investors raises prices of long-duration stocks, which lowers their expected returns, and results in the short-duration premium. Long-duration stocks have the lowest institutional ownership, exhibit the largest forecast errors at earnings announcements, and show the highest mispricing scores.
Brand Values and Long-Term Stock Returns
An equal-weighted portfolio of Best Brands (BBs) in the U.S. earns an excess return of 25 to 35 bps per month during the period 2000-2020. This result is remarkably robust across various factor models and therefore is not driven by exposure to common (risk) factors. The excess returns of the BB portfolio are not due to firm characteristics, industry composition, or small-cap stocks. We provide evidence suggesting that expensing investments in brands (instead of capitalizing them) and the tendency to underestimate the effect of brand name on generating future earnings are two mechanisms contributing to the excess returns.
How You Sort Matters in Sorting Factor Portfolios
Non-standard errors capture uncertainty due to differences in research design choices. We establish substantial variation in the design choices made by researchers when constructing asset pricing factors. By purposely data mining over two thousand different versions of each factor, we find that Sharpe ratios exhibit substantial variation within a factor due to different construction choices, which results in sizable non-standard errors and allows for p-hacking. We provide simple suggestions that reduce the average non-standard error by 70%. Our study has important implications for model selection exercises.
Financial Markets Responding to Climate Risks
This paper provides new evidence showing that carbon transition risk is becoming increasingly material and is priced both in equity and debt markets. We find that there is a widespread price-earnings discount linked to corporate carbon emissions. This discount varies, however, by sector and trends differently in Europe than in the US. We also find that a small discount emerges for corporate bonds, although it is statistically significant only for small caps. Finally, we find evidence that the pricing discount also emerges, albeit to a smaller extent, for other greenhouse gas emissions.
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Litigation Finance as Alternative Investment
Litigation finance is a rapidly growing niche asset class focused on debt and equity investments in litigation claims and law firms. We find that in-sample returns in the space have been in excess of 20% annually with limited correlation to other investment areas. This apparent excess return may be due to information asymmetry and barriers to entry in the space. Our findings highlight the opportunities and risks for investors in this nascent asset classes and suggest such excess returns are due in part to limits to the speed with which efficient markets take hold.
Avoiding Momentum Crashes
Across markets, momentum is one of the most prominent anomalies and leads to high risk-adjusted returns. On the downside, momentum exhibits huge tail risk as there are short but persistent periods of highly negative returns. Crashes occur in rebounding bear markets, when momentum displays negative betas and momentum volatility is high. Based on ex-ante calculations of these risk measures we construct a crash indicator that effectively isolates momentum crashes from momentum bull markets. An implementable trading strategy that combines both systematic and momentum-specific risk more than doubles the Sharpe ratio of original momentum and outperforms existing risk management strategies over the 1928–2020 period, in 5 and 10-year sub-samples, and an international momentum portfolio.
The Expected Returns to ESG-Excluded Stocks
What are the consequences of widespread ESG-based portfolio exclusions on the expected returns of firms subject to exclusion? We consider two possible theoretical explanations. 1) Short-term price pressure around the exclusions leading to correction of mispricing going forward. 2) Long term changes in required returns. We use the exclusions of Norwegian Government Pension Fund Global (GPFG -`The Oil Fund') to investigate. GPFG is the world's largest SWF, and its ESG decisions are used as a model for many institutional investors. We construct various portfolios representing the GPFG exclusions. We find that these portfolios have significant superior performance (alpha) relative to a Fama-French five factor model. The sheer magnitude of these excess returns (5\% in annual terms) leads us to conclude that short-term price pressure can not be the only explanation for our results, the excluded firms expected returns must be higher in the longer term.
Short Sellers Are Informed Investors
Using multiple short sale measures, we examine the predictive power of short sales for future stock returns in 38 countries from July 2006 to December 2014. We find that the days-to-cover ratio and the utilization ratio measures have the most robust predictive power for future stock returns in the global capital market. Our results display significant cross-country and cross-firm differences in the predictive power of alternative short sale measures. The predictive power of shorts is stronger in countries with non-prohibitive short sale regulations and for stocks with relatively low liquidity, high shorting fees, and low price efficiency.
Momentum Everywhere, Including in Factors
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.
Combining Factors in Multifactor Portfolios
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.
Can Machine Learning Identify Future Outperforming Active Equity Funds?
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.
Arbitrage and the Trading Costs of ETFs
This article examines ETF creations and redemptions around price deviations and finds that the expected arbitrage trades are relatively rare in a broad sample of equity index ETFs. In the absence of these trades, price deviations persist much longer. Creation and redemption activity appears to be constrained when exchange conditions would lead to a costlier arbitrage trade, and the size of the price deviations mainly impact the likelihood rather than the amount of trading. The authors also find some evidence that creations and redemptions are less likely to trade on price deviations when they would be required to trade the underlying stocks against broad market movements. Their results suggest that several factors may discourage the built-in ETF arbitrage mechanism and that investors may receive poorer trade execution in these conditions as a result.
The Unintended Consequences of Single Factor Strategies
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.
Short-term Momentum
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.
Strategies to Mitigate Tail Risk
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.
Trend Following: Timing Fast and Slow Trends
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).
Institutions Trading Against Anomalies: Are Their Trades Informed?
An interesting question is do the trades of the more sophisticated institutional investors against anomalies provide information on returns? To answer that question, Yangru Wu and Weike Xu, authors of the study “Changes in Ownership Breadth and Capital Market Anomalies,” published in the February 2022 issue of The Journal of Portfolio Management, examined whether the entries and exits of informed institutional investors (or ownership breadth changes) interact with the aforementioned 11 anomaly signals studied by Stambaugh and Yuan can be used to improve the performance of anomaly-based strategies. They explained that they emphasized institutions’ new entries and exits because they could be triggered by private information and correlated with future earnings news, thereby capturing useful information regarding future stock returns. To determine if the trades of the institutional investors were informed, they sorted all stocks into 10 decile portfolios based on quarterly changes in ownership breadth. Their data sample covered all NYSE/AMEX/Nasdaq common stocks from May 1981 to May 2018.