Relative Sentiment and Machine Learning for Tactical Asset Allocation
The application of machine learning models to Sentix relative sentiment data appears to extract more predictive information than our original, simplistic approach was capable of.
Factors Investing in Cryptocurrency
We find that three factors—cryptocurrency market, size, and momentum—capture the cross-sectional expected cryptocurrency returns. We consider a comprehensive list of price- and market-related return predictors in the stock market and construct their cryptocurrency counterparts. Ten cryptocurrency characteristics form successful long-short strategies that generate sizable and statistically significant excess returns, and we show that all of these strategies are accounted for by the cryptocurrency three-factor model. Lastly, we examine potential underlying mechanisms of the cryptocurrency size and momentum effects.
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.
Global Factor Performance: June 2022
The following factor performance modules have been updated on our Index website.[ref]free access for financial professionals[/ref]
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.
S&P 500 Valuations with and without Technology
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.
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.
Value Investing: Headwinds, Tailwinds, and Variables
Investing is no different. A question we regularly get in the current environment is "How does inflation affect value stocks?" Well...it depends. I could show you some data on how value stocks did in the 70's (period of high inflation) versus how they did in the 90's (low inflation). But if WW3 broke out tomorrow, wouldn't that variable quickly top all other variables? Probably. So let's table that variable.
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).
Form 3 and Form 4 Alpha: Focus on What Insiders Don’t Trade
Some individuals, e.g., those holding multiple directorships, are insiders at multiple firms. When they execute an insider trade at one firm, they may reveal information about the value of all—both the traded insider position and not-traded insider position(s)—the securities held in their “insider portfolio.” We find that insider “not-sold” stocks outperform “not-bought” stocks. Implementable trading strategies that buy not-sold stocks following the disclosure of a sale earn alphas up to 4.8% per year after trading costs. The results suggest that even insider sales that are motivated by liquidity and diversification needs can provide value-relevant information about insider holdings.
Momentum Investing: What happens if we boot stocks over 10x P/S?
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.
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.
Global Factor Performance: May 2022
The following factor performance modules have been updated on our Index website.[ref]free access for financial professionals[/ref]
Using Momentum to Find Value
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 Future of Factor Investing
In this article, the author discusses current structural research and investment trends that are shaping the future of factor investing. Specifically, the author focuses on three emerging trends: the ongoing evolution of traditional factor models and strategies, recent innovation in data sources and modeling techniques, and the potential disruption from integrating factor strategies into the asset allocation process.
Betting Against Beta: New Insights
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.
Is Sector-neutrality in Factor Investing a Mistake?
Long-only factor performance is more likely to degrade from sector neutralizing—keeping the sector component produced better long-only factors in 78 percent of the trials. The largest negative from sector neutralizing occurred for the value-weighted long-only factors that trade large stocks, arguably the most investable portfolio.
Global Factor Performance: April 2022
The following factor performance modules have been updated on our Index website.
2022 Democratize Quant Conference Recap and Materials
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.
Can Investment Flows Affect Prices? Yep.
Traditional finance theory suggests that stocks prices always reflect their fair market values based on publicly available information. Or in academic parlance, the "semi-strong" form efficient markets hypothesis serves as the null. What are the implications of this hypothesis? Well, the hypothesis suggests that the only reason a stock price will move is due to a shift in fundamentals (either through a change in expected cash flows or via the discount rate). But what about supply and demand shifts?