This paper examines the impact of investment banks' prime brokerage connections to hedge funds on the choice of an advisor and the deal outcome in M&As. Acquirers are more likely to choose advisors connected to hedge funds that hold equity in the target before the deal announcement. Such connections increase the likelihood of deal completion and increase acquirer abnormal returns when target firms are characterised by a high degree of information asymmetry. This suggests an 'indirect toehold' mechanism of information transmission.

We develop a novel measure of effort and revisit the fundamental questions of asset management: how do incentives relate to effort; and how does effort affect performance? Using unique observations of daily work activity, we define mutual fund manager effort as the ratio of weekend work to weekday work. We find that investment advisors with stronger competitive incentives exert more effort on weekends. Focusing on within-advisor variation, we find that more effort follows poor performance, outflows and higher volatility. Regarding future performance, we show that more effort is associated with higher future returns, especially for mutual funds with strong competitive incentives, higher active share, and lower turnover. Finally, we demonstrate a causal link between effort and performance using exogenous variation in effort due to weather conditions.

We find that Exchange-Traded Funds (ETFs) are more expensive to borrow than stocks, and we provide an explanation for this difference. This phenomenon is due to features specific to the ETF lending market rather than due to the stocks the ETFs hold, as ETF loan fees tend to be higher than the average of their constituent stocks. We find that for most indices, one ETF tends to capture the majority of the short interest. This "short favorite" ETF tends to have low loan fees, while the "non-favorite" ETFs tend to be much more expensive to short and are less liquid. Demonstrating the magnitude of ETF loan fee differences within each index, we examine the returns to a within-index, cross-ETF arbitrage strategy which is profitable due to persistent loan fee differences across ETFs tracking the same index. Cross-ETF arbitrage returns are quite high and stable. Even when we partially constrain an investor’s ability to fully lend out their long position, we still find that the cross-ETF arbitrage strategy is profitable for about 2/3 of the indices in our sample. The results shed light on limits to arbitrage in the market for exchange-traded funds and provide an explanation for the high ETF loan fees that we observe.

ETFs add a new layer of market‐making to the corporate bond market that improves the market quality of the underlying bonds. Dealers use the flexibility of representative sampling in the primary corporate bond ETF market as a warehouse to manage inventory. We show that bonds with higher selling pressure are included in creation baskets increasing ETF ownership. ETF ownership increases predict credit downgrades and earnings surprises and misses in investment grade bonds. The face value of ETF holdings in investment grade bonds is 9.8% greater on the downgrade date than thirty days prior. By allowing dealers to offset inventory risk in periods of uncertainty, this new layer of market‐making leads to a negative relation between ETF ownership and idiosyncratic volatility, especially for the illiquid bonds.

Financial bubbles are often described as the result of behavioral biases and market frictions. The theoretical model presented in this paper shows how a rational expectation equilibrium (REE), where asset prices can deviate from their fundamental value, can still exist in a world with agents rationally and unbiasedly evaluating an asset's future return. Price deviation is the result of risk-averse fund managers endowed with different information sets who compete to outperform each other. A higher degree of competition leads to larger price deviations and worse risk-adjusted returns. However, competition enhances investors' ability to separate different types of managers, thus creating a trade-off between hampering excessive asset price growth and fostering informed trade identification.

I propose a new benchmark to evaluate hedge fund performance: the returns to shorting CBOE Volatility Index (VIX) futures. The informativeness of this benchmark leads to a new methodology that is able to predict hedge fund performance. Specifically, it separates hedge funds, ex-ante, into one group that delivers higher sharpe ratios and positive skewness (SR of 0.52 and Skew of 4.30) while the other group has lower sharpe ratios and negative skewness (SR of 0.15 and Skew of -0.83), out-of-sample (OOS). I refer to the former group as those hedge funds with edge, in contrast to the latter group as those hedge funds that are without edge. This approach cannot be explained or replicated by previously known methods. Lastly, I show that my empirical findings can be explained by a model that features traders with extrapolative expectations.

This paper hypothesizes that investors in long-short multi-factor strategies focus on variances in their short-term evaluation of portfolio risk, while they neglect correlations, which they perceive as close to zero - in line with the findings of various studies supporting this narrative. Using a set of 14 factor return time series over multiple asset classes, factor styles, and a long historical data period, this paper sheds light on the matter and finds empirical evidence in support of the hypothesis. By decomposing the variance of an equally-weighted multi-factor benchmark into two components, average variance (AV) across and average correlation (AC) between factors, the paper shows that the product of both variables reasonably spans contemporaneous multi-factor variance in-sample, indicating that both components are needed to explain current benchmark risk. However, the subsequent forecasting exercise reveals that only AV predicts future multi-factor risk and return over short horizons (one month up to two years). In sum, the findings support the idea of short-term persistence in risk and the typically assumed variance-in-mean relationship, suggesting that higher AV (in contrast to AC) leads to significantly higher future risk and returns for multi-factor investors, even after controlling for other macroeconomic and market candidate predictors, aswell as alternative risk approximations.

We study how mutual funds respond to ESG scandals of portfolio companies. We find that, after experiencing an ESG scandal in their portfolio, active mutual fund managers (but not passive ones) are more likely to vote in favor of ESG proposals compared to other funds voting on the same proposal, and are more likely to reduce their stakes (and hence their voting power) in high-ESG risk stocks compared to other funds holding the same stock at the same time. Both results are pronounced (a) when the stake in the scandal stock is large, (b) when the scandal is less expected, and (c) when the scandal is accompanied by more negative stock returns. Our results suggest that scandal-shocked funds manage ESG risks in their portfolios, but do not try to maximize impact. As a result, divestments may undermine engagement efforts precisely for those firms that presumably have the biggest need for reform.

We measure hedge funds’ portfolio exposures to green sentiment as measured by the returns on green-minus-brown (GMB) portfolios that track the performance of green stocks compared to brown stocks. While the aggregate hedge fund GMB beta is negative (i.e., an overall brown portfolio tilt), we find heterogeneity in green sentiment exposures that strongly predicts fund returns. A portfolio of funds with GMB betas in the top decile (“green funds”) outperform those with GMB betas in the bottom decile (“brown funds”) by 5.89% per year. Data on portfolio holdings show that the superior performance of green funds can be attributed to stock selectivity skill. A copycat portfolio of green stocks held by hedge funds outperforms a benchmark portfolio of green stocks, and holdings of put options reliably anticipate declines in green stock market valuations.

Can retail investors on social media platforms effectively target hedge fund short positions? We show that the disclosure of hedge fund short positions drives social media activity on WallStreetBets, which in turn precipitates price increases for heavily shorted stocks. The resultant short squeezes hurt hedge funds, which respond by shorting less aggressively, leading to prolonged overpricing in the stock market. In line with a causal interpretation, we find that the impact of social media on stock returns manifests around the publication dates for short sales, but not around the settlement dates, and attenuates during the trading restrictions imposed by Robinhood.

This paper demonstrates that economic narratives significantly price the cross-section of stocks. Using a vast dataset of more than 150k digital media sources since 2013, roughly 350 narratives are quantified, and corresponding narrative-mimicking, long-short portfolios are constructed using stock return narrative betas. Narrative-mimicking portfolios of recently trending narratives outperform those of descending attention by about 7% annually, controlling for standard risk factors. The cross-sectional narrative-beta-pricing is independent of past return and is neither significantly impacted by narrative coverage at the stock level nor earnings announcements. The results suggest that while investors respond to short-run narrative shocks as measured by narrative betas, they under-react to long-run narrative trends, manifesting narrative momentum returns.

I study how hedge funds strategically disclose their private information during short-selling campaigns. Using data on hedge funds' voluntary announcements and daily short positions in the EU market, I document the existence of two groups of funds: Announcers and Followers. Announcers, typically small and young, (1) establish short positions, (2) publish research reports about short targets, and (3) realise profits from the falling price within a short time frame. Followers, usually large, enter at the release of the report and increase their short positions even after announcers exit. To understand the strategic interaction among short sellers, I provide a model to explain how size affects a short seller’s incentive and behaviour. Small funds benefit more from disclosing when facing binding leverage constraints. In contrast, large funds profit from others’ private information by offering capital to price discovery. I characterize the effect of such short-selling campaigns on market efficiency and confirm the model prediction that stocks with lower borrowing costs and larger mispricing are more likely to be announced by hedge funds.

Using new data on mutual funds’ equity lending positions, we find that short sellers borrow shares of different stocks from a different but small set of repeated lenders. Through survey and empirical evidence, we argue that this fragmented, persistent lender structure is driven by myriads of lending-side institutional frictions and contributes to limits-to-arbitrage at the lender-stock level. When existing lenders sell their shares, short sellers struggle to find replacement lenders and get partially squeezed, even when conventional measures suggest lending supply is slack. Consequently, lending fees spike, and stocks become more likely to be overpriced. Ex ante, risks implied by lender structure are priced in equity prices. Overall, our findings suggest that lending-side frictions, a class of frictions unconsidered by prior literature, significantly hamper market efficiency.

We show that after the introduction of leverage ratio constraints on bank-affiliated dealers, bond mutual funds have engaged in more liquidity provision in investment-grade corporate bonds and that the performance of funds with liquidity-supplying strategies has benefited. Not only have regulations transferred profits associated with liquidity provision in the corporate bond market to mutual funds, but the liquidity and returns of investment-grade corporate bonds have become more exposed to redemptions from the bond mutual fund industry, suggesting that the regulations may have made investment-grade corporate bonds more volatile. Accordingly, we observe that investment-grade corporate bonds more exposed to leverage ratio constraints experienced a more severe deterioration in liquidity and returns at the onset of the COVID-19 pandemic.

We propose a novel method to impute daily mutual fund trades in individual stocks from daily stock prices and returns and quarterly fund holdings, monthly total net assets and daily fund returns -- so that the method can be applied to standard CRSP mutual fund data. We set up an (underidentified) system of linear equations and solve the underidentification issue with an iterative method that applies random and adaptive constraints on trade incidence. The method produces daily, stock-level trade estimates with associated confidence levels. Validation and simulation analyses using proprietary daily fund trading data show good accuracy, especially for larger trades.

This paper provides empirical evidence that the low volatility anomaly in stock prices, characterized by high alpha and low idiosyncratic volatility, is an externality that results from mutual funds' performance evaluation against benchmark indexes. Using a unique dataset of mutual funds benchmarked exclusively to the S&P 500 index, I show that mutual funds that tend to keep up with performances of other same-benchmark peers have the tendency to deviate the least possible from past peer trades. This study examines mutual fund holdings of non-benchmark stocks. Mutual fund managers invest heavily in certain stocks and create a clustering effect, causing these stocks to experience higher trade volumes compared to other stocks. High trade volumes rapidly reveals all private information about a stock to the market, causing the stock price to quickly converge to its full information equilibrium, and reduces stock volatility. Stocks with high trade volumes also rapidly adjust to marketwide information which increases stock alpha. Capital flows analyses show that the low volatility anomaly is an outcome due to the behaviour of same-benchmark mutual funds. It lasts for three quarters during which the anomaly remains unexploited.

Existing studies, focused primarily on the U.S., show the improved value factor performance when adjusting book values for intangible assets. However, there is little evidence whether this is a U.S. specific, or wider international phenomenon. My findings expand the existing evidence to multiple international regions and suggest that the intangible-adjusted book-to-market ratio better measures the value factor globally. Especially in more recent decades, where the size of intangible assets increased dramatically, the relative outperformance of the intangible-adjusted value factor over the traditional value factor has become stronger. Economically, the adjusted value factor bears additional risk related to liquidity, financial distress and funding constraints.

Whilst the predictability of fund flows based on prior-period returns is empirically well-established in the literature, we extend its insights to tradeable macro ecosystems, where there is anecdotal evidence of distribution divergences between flows and prior-period returns. We study the relative entropy of flows to capture an exogenous price signal, which we hypothesise to represent the aggregate directional positioning of informed investors. We construct and assess factor portfolios and a spectra of unconstrained systematic long-only portfolios to test our hypothesis, in pursuit of demonstrating the informativeness and additionality of the factor in augmenting traditional weighting schemes to produce superior systematic portfolios, and in adding to the discretionary investor’s toolbox a method for identifying the positioning of supposed “smart money” in the aggregate. Our findings indicate that the relative entropy signal demonstrates relevance in predicting future returns and identifying relative value within our designated tradeable macro ecosystem and that, within certain portfolio configurations, it can contribute positively to portfolio construction outcomes based on historical evidence.

Many institutional investors publicly commit to some form of responsible investment. This raises concerns about the credibility of such claims. We use participation in collaborative engagements to identify ``Leaders’’, i.e., institutional investors that are truly committed to improving firms' sustainability outcomes. Despite owning only 2.2% of the average firm, Leaders alone explain the positive relationship between institutional ownership and firms’ environmental and social performance. In line with committed owners facilitating corporate change, engagement campaigns are successful only when targeted firms are substantially owned by Leaders. We also find that these firms are less at risk of experiencing negative incidents.

We propose a new performance attribution framework that decomposes a constrained portfolio's holdings, expected utility, expected returns, variance, and realized returns into components attributable to: (1) the unconstrained mean-variance optimal portfolio; (2) individual static constraints; and (3) information, if any, arising from those constraints. A key contribution of our framework is the recognition that constraints may contain information that is correlated with returns, in which case imposing such constraints can affect performance. The excess return from information is positive (negative) when this correlation is positive (negative) and the constraint is binding. The excess variance of a portfolio is negative when the holdings of a shrinkage portfolio and the holdings attributable to constraints are positively correlated, and the degree to which variance is reduced depends on the squared correlation between returns and constraints. We provide simulated and empirical examples involving constraints on ESG portfolios. Contrary to conventional wisdom, constraints may improve portfolio performance under certain scenarios.

We develop a novel measure of disagreement in voice between active and passive mutual funds using their proxy votes that captures shareholder conflicts in public firms. We show that the disagreement in voice between passive and active mutual funds destroys firm value and suggest that the firm value loss is due to conflicting incentives between the two groups of mutual funds. Using Federal Open Market Committee announcements with press conferences as events that create scope for investors to make informed votes and interpret news differently for individual firms, we show that such value-destroying effect of disagreement is likely causal.