We introduce a Human Capital Concern (HCC) index that captures public concern about financially material social risks. Analyzing U.S. mutual fund flows of retail investors, we document that, during periods of elevated human capital concern, flow sensitivity to fund social risk scores increases by 60%. Non-linear effects in the intensity and duration of the HCC index suggest that investors mainly respond to unexpected social news shocks. Funds with a stronger social score earn higher returns following high-HCC periods, with effects persisting for only two months. This pattern is consistent with investors updating their expectations about the future returns of social-risk assets.

We propose an approach to construct text-based time-series indices in an optimal way--typically, indices that maximize the contemporaneous relation or the predictive performance with respect to a target variable, such as inflation. We illustrate our methodology with a corpus of news articles from the Wall Street Journal by optimizing text-based indices focusing on tracking the VIX index and inflation expectations. Our results highlight the superior performance of our approach compared to existing indices.

We reassess Boehmer et al. (2021, BJZZ)'s seminal work on the predictive power of retail order imbalance (ROI) for future stock returns. First, we replicate their 2010-2015 analysis in the more recent 2016-2021 period. We find that the ROI's predictive power weakens significantly. Specifically, past ROI can no longer predict weekly returns on large-cap stocks, and the long-short strategy based on past ROI is no longer profitable. Second, we analyze the effect of using the alternative quote midpoint (QMP) method to identify and sign retail trades on their main conclusions. While the results based on the QMP method align with BJZZ's findings in 2010-2015, the two methods provide different conclusions in 2016-2021. Our study shows that BJZZ's original findings are sensitive to the sample period and the approach to identify ROIs.

Using intraday (hourly) and overnight changes in the number of Robinhood (RH) investors holding a stock, we examine their high-frequency trading behaviors in response to contemporaneous and lagged returns. RH investors do not react to contemporaneous returns. However, they respond to lagged intraday or overnight returns, exhibiting three high-frequency behaviors: (i) the number of RH investors increases more for stocks with extreme lagged returns than for those with moderate returns, suggesting attention-driven buying; (ii) this reaction is asymmetric, with larger increases in the number of RH users following extreme negative returns compared to extreme positive returns, suggesting that their contrarian buying is stronger than their momentum buying; (iii) this asymmetry is strongest immediately after extreme returns and dissipates over time. Compared to findings from daily data, our analysis shows that daily data underestimates this asymmetry. Further analyses reveal greater attention to overnight movements, exacerbated behaviors during COVID-19, and variation across firm sizes, with more contrarian buying for larger-cap firms.

We introduce a new framework for the mean-variance spanning (MVS) hypothesis testing. The procedure can be applied to any test-asset dimension and only requires stationary asset returns and the number of benchmark assets to be smaller than the number of time periods. It involves individually testing moment conditions using a robust Student-t statistic based on the batch-mean method and combining the p-values using the Cauchy combination test. Simulations demonstrate the superior performance of the test compared to state-of-the-art approaches. For the empirical application, we look at the problem of domestic versus international diversification in equities. We find that the advantages of diversification are influenced by economic conditions and exhibit cross-country variation. We also highlight that the rejection of the MVS hypothesis originates from the potential to reduce variance within the domestic global minimum-variance portfolio.

We study the relation between the promotion of a cryptocurrency on Twitter and its return dynamics around pump-and-dump events. By analyzing abnormal returns, trading volume, and tweet activity, we uncover that Twitter effectively garners attention for pump-and-dump schemes, leading to notable effects on abnormal returns before the event. Our results indicate that investors relying on Twitter information exhibit delayed selling behavior during the post-dump phase, resulting in significant losses compared to other participants. We also find that, while tweets directly promoting pump schemes align with anticipated market phases, a noteworthy portion of indirect, non-pump-aware tweets significantly influence market movements pre-event.

We propose a novel estimation procedure of bid-ask spreads from open, high, low, and close prices. Our estimator is asymptotically unbiased and optimally combines the full set of price data to minimize the estimation variance. When quote data are not available, our estimator generally delivers the most accurate estimates of effective bid-ask spreads numerically and empirically. The estimator is derived under permissive assumptions that allow for stylized facts typically observed in real market data, is easy to implement, and can be applied to liquid and illiquid market segments, both in low and high frequency.

We show that the two-stage minimum description length (MDL) criterion widely used to estimate linear change-point (CP) models corresponds to the marginal likelihood of a Bayesian model with a specific class of prior distributions. This allows results from the frequentist and Bayesian paradigms to be bridged together. Thanks to this link, one can rely on the consistency of the number and locations of the estimated CPs and the computational efficiency of frequentist methods, and obtain a probability of observing a CP at a given time, compute model posterior probabilities, and select or combine CP methods via Bayesian posteriors. Furthermore, we adapt several CP methods to take advantage of the MDL probabilistic representation. Based on simulated data, we show that the adapted CP methods can improve structural break detection compared to state-of-the-art approaches. Finally, we empirically illustrate the usefulness of combining CP detection methods when dealing with long time series and forecasting.

We develop a novel approach to separate alpha and beta under model misspecification. It comes with formal tests to identify less misspecified models and sharpen the return decomposition of individual funds. Our hedge fund analysis reveals that: (i) prominent models are as misspecified as the CAPM, (ii) several factors (time-series momentum, variance, carry) capture alternative strategies and lower performance in all investment categories, (iii) fund heterogeneity in alpha and beta is large—an important result for fund selection and models of active management, (iv) performance is increasingly similar to mutual funds, (v) fund valuation is sensitive to investor sophistication.

The decomposition of hedge fund returns is hampered by model misspecification. To address this issue, we develop a novel approach to compare models in a large population of funds. This comparison, which accounts for misspecification-driven estimation errors, sharpens the separation between alpha and beta. Our analysis reveals that: (i) prominent models are as misspecified as the CAPM, (ii) several factors—primarily time-series momentum, variance, carry—capture hedge fund strategies and lower performance, (iii) alpha and beta components correlate negatively and vary substantially across funds, consistent with equilibrium models featuring search costs, and (iv) fund valuation is sensitive to investor sophistication.

We examine the influence of Twitter promotion on cryptocurrency pump-and-dump events. By analyzing abnormal returns, trading volume, and tweet activity, we uncover that Twitter effectively garners attention for pump-and-dump schemes, leading to notable effects on abnormal returns before the event. Our results indicate that investors relying on Twitter information exhibit delayed selling behavior during the post-dump phase, resulting in significant losses compared to other participants. These findings shed light on the pivotal role of Twitter promotion in cryptocurrency manipulation, offering valuable insights into participant behavior and market dynamics.

We analyze Robinhood (RH) investors' trading reactions to intraday hourly and overnight price changes. Contrasting with recent studies focusing on daily behaviors, we find that RH users strongly favor big losers over big gainers. We also uncover that they react rapidly, typically within an hour, when acquiring stocks that exhibit extreme negative returns. Further analyses suggest greater (lower) attention to overnight (intraday) movements and exacerbated behaviors post-COVID-19 announcement. Moreover, trading attitudes significantly vary across firm size and industry, with a more contrarian strategy towards larger-cap firms and a heightened activity on energy and consumer discretionary stocks.

We empirically test the prediction of Pástor et al. (2021) that green firms outperform brown firms when concerns about climate change increase unexpectedly, using data for S&P 500 companies from January 2010 to June 2018. To capture unexpected increases in climate change concerns, we construct a daily Media Climate Change Concerns index using news about climate change published by major U.S. newspapers and newswires. We find that on days with an unexpected increase in climate change concerns, the green firms’ stock prices tend to increase, whereas brown firms’ prices decrease. Furthermore, using topic modeling, we conclude that this effect holds for concerns about both transition and physical climate change risk. Finally, we decompose returns into cash flow and discount rate news components and find that an unexpected increase in climate change concerns is associated with an increase (decrease) in the discount rate of brown (green) firms. This paper was accepted by George Serafeim, Special Section of Management Science on Business and Climate Change. Funding: This work was supported by the National Bank of Belgium, Research Foundation Flanders (FWO), Institut de Valorisation des Données (IVADO), the Natural Sciences and Engineering Research Council of Canada [Grant RGPIN-2022-03767], and Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung [Grants 179281, 191730]. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2022.4636 .

We study how the financial literature has evolved in scale, research team composition, and article topicality across 32 finance-focused academic journals from 1992 to 2021. We document that the field has vastly expanded regarding outlets and published articles. Teams have become larger, and the proportion of women participating in research has increased significantly. Using the Structural Topic Model, we identify 45 topics discussed in the literature. We investigate the topic coverage of individual journals and can identify highly specialized and generalist outlets, but our analyses reveal that most journals have covered more topics over time, thus becoming more generalist. Finally, we find that articles with at least one woman author focus more on topics related to social and governance aspects of corporate finance. We also find that teams with at least one top-tier institution scholar tend to focus more on theoretical aspects of finance.

Using the peer-exposure ratio, we explore the factor exposure heterogeneity in green and brown stocks. By looking at peer groups of S&P 500 index firms over 2014-2020 based on their greenhouse gas emission levels, we find that, on average, green stocks exhibit less factor exposure heterogeneity than brown stocks for most of the traditional equity factors but the value factor. Hence, investment managers shifting their investments from brown stocks to green stocks have less room to differentiate themselves regarding their factor exposures. Finally, we find that factor exposure heterogeneity has increased for green stocks compared to earlier periods.