Alpha Architect

Do Equity Markets Care About Income Inequality?

Do equity markets care about income inequality? We address this question by examining equity markets’ reaction and investors’ portfolio rebalancing in response to the first-time disclosure of the ratio of CEO to median worker pay by U.S. public companies in 2018. We find that firms’ disclosing higher pay ratios experience significantly lower abnormal announcement returns. Additional evidence suggests that equity markets “dislike” high pay dispersion rather than high CEO pay or low worker pay. Firms whose shareholders are more inequality-averse experience a more pronounced negative market response to high pay ratios compared to firms with less inequality-averse shareholders. Finally, we find that during 2018 more inequality-averse investors rebalance their portfolios away from high pay ratio stocks relative to other investors. Overall, our results suggest that equity markets are concerned about high within-firm pay dispersion, and investors’ attitude towards income inequality is a channel through which high pay ratios negatively affect firm value.

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.

Is Passive Ownership Bigger than Estimated?

We estimate that passive investors held at least 37.8% of the US stock market in 2020. This estimate is based on the closing volumes of index additions and deletions on reconstitution days. 37.8% is more than double the widely accepted previous value of 15%, which represents the combined holdings of all index funds. What’s more, 37.8% is a lower bound. The true passive-ownership share for the US stock market must be higher. This result suggests that index membership is the single most important consideration when modeling investors’ portfolio choice. In addition, existing models studying the rise of passive investing give no hint that prior estimates for the passive-ownership share were 50% too small. The size of this oversight restricts how useful these models can be for policymakers.

Book Review: Your Essential Guide to Sustainable Investing

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.

Mining Credit Card Data for Stock Returns

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 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.

What Drives Momentum and Reversal?

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.

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.

Do Stocks Efficiently Predict Recessions?

I find that returns are predictably negative for several months after the onset of recessions, becoming high only thereafter. I identify business cycle turning points by estimating a state-space model using macroeconomic data. Conditioning on the business cycle further reveals that returns exhibit momentum in recessions, whereas in expansions they display the mild reversals expected from discount rate changes. A strategy exploiting this pattern produces positive alphas. Using analyst forecast data, I show that my findings are consistent with investors' slow reaction to recessions. When expected returns are negative, analysts are too optimistic and their downward expectation revisions are exceptionally high.

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.

Measuring a Firms’ Environmental Impact

To manage climate risks, investors need reliable climate exposure metrics. This need is particularly acute for climate risks along the supply chain, where such risks are recognized as important, but difficult to measure. We propose an intuitive metric that quantifies the exposure a company has to customers, or suppliers, who may in turn be exposed to climate risks. We show that such risks are not captured by traditional climate data. For example, a company may seem green on a standalone basis, but may still have meaningful, and potentially material, climate risk exposure if it has customers, or suppliers, whose activities could be impaired by transition or physical climate risks. Our metric is related to scope 3 emissions and may help capture economic activities such as emissions offshoring. However, while scope 3 focuses on products sold to customers and supplies sourced from suppliers, our metric captures the strength of economic linkages and the overall climate exposure of a firm’s customers and suppliers. Importantly, the data necessary to compute our measure is broadly accessible and is arguably of a higher quality than the currently available scope 3 data. As such, our metric’s intuitive definition and transparency may be particularly appealing for investors.

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.

Can We Measure Inflation with Twitter

Drawing on Italian tweets, we employ textual data and machine learning techniques to build new real-time measures of consumers’ inflation expectations. First, we select keywords to identify tweets related to prices and expectations thereof. Second, we build a set of daily measures of inflation expectations around the selected tweets, combining the Latent Dirichlet Allocation (LDA) with a dictionary-based approach, using manually labeled bi-grams and tri-grams. Finally, we show that Twitter-based indicators are highly correlated with both monthly survey-based and daily market-based inflation expectations. Our new indicators anticipate consumers’ expectations, proving to be a good real-time proxy, and provide additional information beyond market-based expectations, professional forecasts, and realized inflation. The results suggest that Twitter can be a new timely source for eliciting beliefs.

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.

An Investor’s Guide to Crypto

We provide practical insights for investors seeking exposure to the growing cryptocurrency space. Today, crypto is much more than just bitcoin, which historically dominated the space but accounted for just a 21% share of total crypto trading volume in 2021. We discuss a wide variety of tokens, highlighting both their functionality and their investment properties. We critically compare popular valuation methods. We contrast buy-and-hold investing with more active styles. We only deem return data from 2017 representative, but the use of intraday data boosts statistical power. Underlying crypto performance has been notoriously volatile, but volatility-targeting methods are effective at controlling risk, and trend-following strategies have performed well. Crypto assets display a low correlation with traditional risky assets in normal times, but the correlation also rises in the left tail of these risky assets. Finally, we detail important custody and regulatory considerations for institutional investors.

Does Intangible-Adjusted Book-to-Market Work?

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

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.

Calculating Supply Chain Climate Exposure

To manage climate risks, investors need reliable climate exposure metrics. This need is particularly acute for climate risks along the supply chain, where such risks are recognized as important, but difficult to measure. We propose an intuitive metric that quantifies the exposure a company has to customers, or suppliers, who may in turn be exposed to climate risks. We show that such risks are not captured by traditional climate data. For example, a company may seem green on a standalone basis, but may still have meaningful, and potentially material, climate risk exposure if it has customers, or suppliers, whose activities could be impaired by transition or physical climate risks. Our metric is related to scope 3 emissions and may help capture economic activities such as emissions offshoring. However, while scope 3 focuses on products sold to customers and supplies sourced from suppliers, our metric captures the strength of economic linkages and the overall climate exposure of a firm’s customers and suppliers. Importantly, the data necessary to compute our measure is broadly accessible and is arguably of a higher quality than the currently available scope 3 data. As such, our metric’s intuitive definition and transparency may be particularly appealing for investors.

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.

Using Institutional Investor’s Trading Data in Factors

The authors investigate how the interaction between entries and exits of informed institutional investors and market anomaly signals affects strategy performance. The long legs of anomalies earn more positive alphas following entries, whereas the short legs earn more negative alphas following exits. The enhanced anomaly-based strategies of buying stocks in the long legs of anomalies with entries and shorting stocks in the short legs with exits outperform the original anomalies, with an increase of 19–54 bps per month in the Fama–French five-factor alpha. The entries and exits of institutional investors capture informed trading and earnings surprises, thereby enhancing the anomalies.

Are You Ready to Become a Better Investor?

Get Updates Now

Recent Posts

Important Disclosures

Other Insights

Are You Ready to Become a Better Investor?

Get Updates Now

Recent Posts

Important Disclosures