Short-term alpha signals are generally dismissed in traditional asset pricing models, primarily due to market friction concerns. However, this paper demonstrates that investors can obtain a significant net alpha by combining signals applied on a liquid global universe with simple buy/sell trading rules. The composite model consists of short-term reversal, short-term momentum, short-term analyst revisions, short-term risk, and monthly seasonality signals. The resulting alpha is present across regions, translates into long-only applications, is robust to incorporating implementation lags of several days, and is uncorrelated to traditional Fama-French factors.
I am grateful for this book because I am less confused about sustainable investing, and I am inspired to learn more about the topic. I commend Larry and Sam’s work for being technically accurate and complete, while accessible to a reader who isn’t an expert on the subject and is looking to learn more.
Using a unique dataset of individual transactions-level data for a universe of U.S. consumer facing stocks, we examine the information content of consumer credit and debit card spending in explaining future stock returns. Our analysis shows that consumer spending data positively predict various measures of a company’s future earnings surprises up to three quarters in the future. This predictive power remains strong in both large- and small-cap universes of consumer discretionary firms in our sample and is robust to the type of transactions data considered (credit card, debit card, or both), although the relationship is stronger in the small-cap universe where informational asymmetries are more pronounced. Based on this empirical observation we build a simple long-short strategy that takes long/short positions in the top/bottom tercile of stocks ranked on our real-time sales signal. The strategy generates statistically and economically significant returns of 16% per annum net of transaction costs and after controlling for the common sources of systematic factor returns. A simple optimization exercise to form (tangency) mean-variance efficient portfolios of factors leads to an optimal factor allocation that assigns almost 50% weight to our long-short portfolio. Our results suggest that consumer transaction level data can serve as a more accurate and persistent signal of a firm’s growth potential and future returns.
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.
If one had to invest in buy and hold treasury bonds or trend-followed treasury bonds, it is likely that most investors would prefer the trend-followed bond investment. However, in a broader portfolio context, the analysis suggests that how one 'eats' their bond exposure is largely irrelevant and the portfolio's long-term outcome will be driven by equity market dynamics. Bonds systematically lower an equity-centric portfolio's returns, but they also lower the risk profile of the overall portfolio.
How information affects asset prices is of fundamental importance. Public information flows through news, while private information flows through trading. We study how stock prices respond to these two information flows in the context of two major asset pricing anomalies— short-term reversal and momentum. Firms release news primarily during non-trading hours, which is reflected in overnight returns. While investors trade primarily intraday, which is reflected in intraday returns. Using a novel dataset that spans almost a century, we find that portfolios formed on past intraday returns display strong reversal and momentum. In contrast, portfolios formed on past overnight returns display no reversal or momentum. These results are consistent with underreaction theories of momentum, where investors underreact to the information conveyed by the trades of other investors.
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 analysis above highlights that we are in a rare regime when commodities are the only long asset with a positive trend. The last time this happened we entered a long period of high inflation and poor real returns. Will this happen again? Who knows. But we do know that post-1973 we entered a world where, for several decades (at least up to around 2007), both bonds and commodities were an important component of a diversified portfolio. The recent past has arguably made investors complacent in their reliance on a stock/bond portfolio as an end-all-be-all solution. When history tells us that incorporating commodities into a portfolio probably makes sense from a diversification standpoint.