Want to Get on a Corporate Board? Who You Know Matters
Here's what the research says about how to get on a board of directors.
Does Momentum work in Option Markets?
This paper explores the question of option momentum by examining what the research says about the performance of option investments across different stocks.
Is there a theoretical foundation behind industry and factor momentum
Industry and factor momentum should be viewed as recent developments in the wider momentum story, although these aggregated measures of momentum lack any theoretical foundation.
The Cross Section of Stock Returns Pre-CRSP data: Value and Momentum are confirmed as robust anomalies
We study the cross-section of stock returns using a novel constructed database of U.S. stocks covering 61 years of independent data.
Momentum Factor Investing: 30 years of Out of Sample Data
In this article, the author examines the research published over the last 30 years on momentum and its theoretical credibility. One of the original momentum articles was published by Jegadeesh and Titman in 1993, and is considered the seminal work on the topic. The research review contained in this publication begins with the 1993 work and confines itself to only the highest quality journals among the plethora of work that has been published on momentum.
Are ESG strategies more effective for societal impact or performance?
In this article, we examine the research addressing the question of to what extent, if any, ESG strategies improve investment performance on a risk-adjusted basis, or if they are more effectively used for the societal impact they potentially have.
Is Democracy better for the stock market?
We examine the question of whether or not democracy leads to better possible outcomes for the stock market.
Pulling the curtain back: what do millionaires invest in?
In this article, we examine the academic research about what millionaires invest in.
Does Gender Affect Reactions to Market Sentiment Shifts?
Does gender matter in institutional investing?
Gender Pay Gap Transparency
In this article, we examine what the research says about gender pay gap transparency. We look at the research questions and academic insights with an eye toward why it matters.
Should Levered and Inverse ETFs Even Exist?
In this article, we explore Levered and Inverse ETPs (exchange-traded products); their purpose, the circumstances in which they tend to succeed and fail, and the research questions associated with them.
Is Momentum a Separate Factor?
We find that factor momentum concentrates in factors that explain more of the cross section of returns and that it is not incidental to individual stock momentum: momentum-neutral factors display more momentum.
ESG Ratings how do they Compare Across Data Providers?
Investments aligned with environmental, social, and governance (ESG) principles are rapidly growing globally. In the exchange traded fund (ETF) industry, this gives rise to the power of ESG rating firms that have the influence to direct capital flows into ETFs tracking the indexes. This article examines the issues of substantial ESG rating divergence across rating firms, the impact on investors’ choices, and the influence on the ETF industry. The divergence appears to be the greatest in social and governance components, and is often qualitative in nature. The author found that certain economic sectors are more prone to ESG rating divergence than others. She presents a case study about two ESG ETFs that are viewed quite differently under various rating lenses, and offers suggestions to investors, advisors, and analysts on how to research ESG ETFs, given the major rating divergence. The article concludes with ways the ETF industry could improve its practices collectively to better serve investors with clarity and to sustain the growth of ESG impact investments.
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.
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.
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.
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.
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.
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.
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.