There is strong empirical evidence demonstrating that momentum (both cross-sectional and time-series) provides information on the cross-section of returns of many risk assets and has generated alpha relative to existing asset pricing models.
Higher volatility is associated with faster, initially stronger reversals, while lower turnover is associated with more persistent, ultimately stronger reversals
To date, the best metric we have for forecasting future equity returns and the ERP is current valuations. An interesting question is whether more complicated methods using newly developed machine learning models can provide superior forecasts.
The following factor performance modules have been updated on our Index website.
Momentum continues to receive much attention from researchers because of the strong empirical evidence.
Can AI models improve on the failures in predicting returns strictly from a practical point of view? In this paper, the possibilities are tested with a battery of AI models including linear regression, dimensional reduction methods, regression trees and neural networks. These machine learning models may be better equipped to address the multidimensional nature of stock returns when compared to traditional sorting and cross-sectional regressions used in factor research. The authors hope to overcome the drawbacks and confirm the results of traditional quant methods. As it turns out, those hopes are only weakly fulfilled by the MLM framework.
The justification for neutralizing sectors in factor strategies is a work in progress. To date, academic researchers haven't had an empirical model to mimic the impact of removing sector "effects" on the measurement and performance of factor strategies. The authors develop and test a two-component model to address the question of, "Is Sector Neutrality in Factor Investing a Mistake?"
While the empirical research on cross-sectional (long-short) momentum has shown that returns have been high, investors have also experienced huge drawdowns—momentum exhibits both high kurtosis and negative skewness. Since 1926 there have been several momentum crashes that featured short, but persistent, periods of highly negative returns. For example, from June to August 1932, the momentum portfolio lost about 91%, followed by a second drawdown from April to July 1933.
The following factor performance modules have been updated on our Index website.[ref]free access for financial professionals[/ref] Factor Performance Factor Premiums Factor Data Downloads
Studies have found that there is a correlation between stock market downturns and an increase in hospital admissions for mental illness, an increase in domestic violence, deteriorating mental health among retirees, and increased depression rates.
Convexity can provide explosive payoffs from unlikely events. It’s a powerful weapon to wield, but like most weapons, it could be inefficient or even dangerous in the hands of the untrained.
Strong empirical evidence demonstrates that momentum (both cross-sectional and time-series) provides information on the cross-section of returns of many risk assets and has generated alpha relative to existing asset pricing models.
In their two papers, Goulding, Harvey, and Mazzoleni showed that observed market corrections and rebounds carry predictive information about subsequent returns and showed how that information could be utilized to enhance the performance of trend-following strategies by dynamically blending slow and fast momentum strategies based on four-state cycle-conditional information.
Short squeezes are often associated with a large positive jump in the price of a stock. Filippou, Garcia-Ares, and Zapatero demonstrated that skewness-seeking investors try to identify securities that could experience a short squeeze in the near future and are willing to pay a premium for them. That results in an overvaluation of the options and, on average, negative returns. Investors are best served to avoid investments with lottery-like distributions. One way to do that is to turn a blind eye to social media sites like Robinhood and Reddit so you don’t get caught up in the hype and excitement. That’s another example of why retail investors are called “dumb money.” Forewarned is forearmed.
Be careful before acting on what is considered to be conventional wisdom. Make sure it’s supported by empirical evidence. In this case, the evidence makes clear that “cut your losses and let your profits run” should not be conventional wisdom.
Making a bet on biotech/pharma firms that have not yet achieved significant revenue is the equivalent of buying a lottery ticket—with the same poor risk/return relationship.
While analysts underwrite high growth for companies that have grown quickly and slow growth for companies that have grown slowly in the past, a large body of evidence demonstrates that reversion to the mean of both positive and negative abnormal earnings growth is the norm.
Advisors and investors should be aware that fund families that invest systematically have found ways to incorporate the research findings on the limits to arbitrage and the evolving changes we have discussed to improve returns over those of a pure index replication strategy. It seems likely this will become increasingly important, as the markets have become less liquid, increasing the limits to arbitrage and allowing for more overpricing.
The following factor performance modules have been updated on our Index website.
For investors that use trend-following strategies, Avramov, Kaplanski, and Subrhmanyam provided new evidence supporting momentum strategies and showed that the distance between short- and longer-term momentum signals provides additional explanatory power in the cross-section of equity returns.
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