Do factor investing funds successfully capture the premiums associated with academic factors? We explore this question using the growing number of factor investing funds that seek to capture those premiums. While, on average, such funds do not outperform, we find that the factor investing funds with the portfolios that most closely match their academic factors—determined using our novel, holding-based ‘active characteristic share’ measure—significantly outperform those that less closely match. Furthermore, adjusting for stock size, we conclude that the answer to our question is “yes” for closely-matching factor investing funds, which net of costs duplicate the paper performance of the long side of academic factors.

We identify a parsimonious set of factors from a large set of candidates that can

potentially explain hedge fund returns, ranging from equity market factor, anomaly

factors, trend-following factors to macroeconomic factors. The resulting nine-factor model, including five anomaly factors, outperforms existing hedge fund models both in-sample and out-of-sample, with a significant reduction in alphas while maintaining substantial cross-sectional performance heterogeneity. Further analysis reveals evidence of strategy shifts by hedge funds over time, making necessary the addition of the anomaly factors. Our results suggest the importance of periodically updating factors for the hedge fund industry.

This study addresses the optimal asset allocation problem for investors managing a diversified portfolio of stocks, bonds, and hedge funds. Significant allocations to hedge funds may be justified due to their diversification benefits, even when hedge funds generate minimal or no alpha. For instance, an investor with constant relative risk aversion and concern for inter-temporal utility should allocate around 20% to hedge funds, even under the assumption of zero alpha. Historical correlations and specified alpha levels indicate that equity and event-driven hedge fund strategies offer the greatest diversification advantages, while global macro and managed futures strategies are less favorable. However, optimal hedge fund allocations are highly sensitive to alpha assumptions. If alphas fall below -1%, the allocation to hedge funds typically approaches zero, whereas an alpha above 2% can lead the investor to allocate nearly 100% to hedge funds. This sensitivity also applies to individual hedge fund strategies. Finally, given that investing in many different hedge funds can be cost-prohibitive, we assess the allocation impact of investing in a limited number of hedge funds instead of a broad, uninvestable index. We find that reducing the number of funds held—from 30 to 5—substantially increases the likelihood that hedge funds will diminish investor utility.

We study hedge fund liquidity management in the presence of liquidity risks on the asset and liability sides. We formulate a two-period model where a single fund has always access to a liquid asset and can invest in an illiquid asset which pays off only at the end of period two. Funding liquidity risk takes the form of a random outflow originating from clients in period one. The fund suffers from a random haircut on the illiquid asset’s secondary market to cover its outflow. We solve the allocation problem of the fund and find its optimal allocation between liquid and illiquid assets. We show that the liquidation probability and the portfolio composition of the fund are revealing about the market liquidity and funding liquidity, respectively. Gates, as a device that limits the outflows experienced by the fund, helps it reduce its liquidation risk and harvest liquidity premia.

We study the impact of hedge funds' bargaining power on banks' haircut policies in secured lending transactions. We observe that on the same day, for identical collateral, and under identical repo contracts, banks require significantly lower haircuts from hedge funds with greater bargaining power, even when controlling for their probability of default. This effect is further confirmed by plausible exogenous variation in hedge funds' bargaining power, stemming from Credit Suisse's withdrawal from the prime brokerage business. Furthermore, our findings reveal that higher bargaining power of hedge funds substantially elevates the risk of insufficient haircuts according to standard value-at-risk models, in particular for collateral eligible for monetary policy operations.

This paper investigates the impact of ESG short-selling activity in light of the recent ESG backlash and the rise of passive investment. We postulate that ESG short-selling activity varies depending on whether stocks are included in a blue-chip equity index, influenced by the growing prominence of passive investment strategies. Using short-selling data from IHS Markit and ESG ratings from Refinitiv for U.S. equities from January 2016 to December 2022, we find that higher-overpriced ESG stocks excluded from the MSCI USA Index are no longer immune to short sellers. Our results also reveal that the securities lending market

supply is not a constraint for short sellers when stocks are excluded from the index,

particularly in the context of ESG considerations. Finally, we find that borrowing costs for higher-ESG stocks are higher for those outside the index, while the opposite is true for stocks included in the index.

This research presents a comprehensive framework for optimizing Volume Weighted Average Price (VWAP) execution in cryptocurrency markets using deep learning approaches. Through three interconnected studies, we demonstrate how moving beyond traditional volume curve prediction can enhance VWAP execution performance. First, we show how deep learning's automatic differentiation capabilities can directly optimize VWAP execution by minimizing either absolute or quadratic deviations from market VWAP. Our initial static model, implemented using a Temporal Linear Network architecture, consistently outperforms traditional volume-prediction approaches across multiple cryptocurrencies. Building on these results, we develop a dynamic execution framework utilizing recurrent neural networks that can adapt to changing market conditions during order execution. This dynamic approach further improves performance by incorporating real-time market feedback into the execution strategy. Finally, we introduce a novel multi-asset learning approach using signature-enhanced transformers, which enables efficient model deployment across multiple assets while maintaining superior performance. This architecture, which combines attention mechanisms with path signatures, demonstrates consistent improvement over both asset-specific and globally-fitted models for both previously seen and unseen assets. Our research not only advances the theoretical understanding of VWAP execution but also provides practical implementations that significantly improve trading efficiency in cryptocurrency markets.

Higher corporate social responsibility ratings limit short selling. Among firms with high expected values of short interest, those with higher ESG scores and higher environmental scores have less shorting. We find evidence consistent with higher ESG scores creating additional costs and risks for short sellers through two channels: 1) some long-side investors are reluctant to sell high ESG stocks, even if valuations warrant it; and 2) short squeeze risk—high ESG stocks experience positive sentiment-driven price jumps when public attention to ESG spikes. The lack of shorting impacts asset prices. Stocks with high ESG scores are less responsive to negative earnings announcements. High ESG stocks that are avoided by short sellers have low future stock returns.

We propose a new measure of ESG-specific skill based on fund manager trades and ESG rating changes. We differentiate between proactive ESG managers, whose trades predict future changes in ESG ratings, reactive ESG managers, who change their portfolio allocation after a change in ESG ratings occurs, and non-ESG managers. The predictive ability of proactive managers is persistent in out-of-sample tests, consistent with manager skill. For identification, we rely on an exogenous methodology change of one ESG rating provider that redefined ESG ratings levels without releasing new information. Reactive managers significantly change their holdings in firms whose ESG ratings exogenously change, consistent with a lack of ESG skill. Proactive managers do not trade in the direction of the change, consistent with their trading no new ESG information. This ESG skill has economic implications: Investors in mutual funds with an explicit sustainability mandate reward proactive managers with 58bps higher average quarterly flows.

Smart money often trades actively during times of large corporate events. We document in the context of mergers and acquisitions (M&A) that, during the public bid negotiation period, institutional investors increase their holdings of acquirers in deals that generate positive value and decrease their holdings in those that generate negative value. The resulting trading profits create a significant gap between the return to the acquiring firm and the return to these investors, and this gap renders firm return a misleading measure of investors’ incentives in pursuing mergers. On average, institutional investors of acquiring firms earn 2.4% from M&A while the return to passive acquirer shareholders is only -0.9%. In deals that deliver volatile returns to acquiring firms, the gap widens to 6.3%. We further show how the trading motive impacts the ex ante holdings of institutional investors and how the trading decision and the resulting gap are impacted by deal characteristics such as merger size and stock liquidity as well as institutions’ characteristics such as initial holdings, portfolio weight, and trading skills. Institutions that earn a high return gap are associated with weak governance in preempting and correcting value-destroying mergers. Our study highlights that the group of investors who have influence over corporate actions do not necessarily bear the full consequences of such events, and therefore accounting for the dynamics of shareholder composition is critical in measuring investors’ incentives correctly.

We conduct the first analysis on the impact of democratized AI (ChatGPT) on the trading activities of investors by leveraging a dataset of long textual information spanning 19 years of earnings calls. We have three key findings. First, AI-sentiment generated by ChatGPT strongly predicts returns for up to 12 months, while traditional human-dictionary-based sentiment yields little predictability. Second, before the arrival of ChatGPT, short sellers traded in alignment with AI-sentiment within two weeks following earnings calls, while retail traders did not. Following the widespread deployment of ChatGPT, there was a significant 65-fold increase in retail-trader alignment with AI-sentiment, whereas the alignment of short-sellers with AI-sentiment may have weakened. Third, stock experiencing increased retail traders’ alignment with AI-sentiment also witnessed a significant decrease in bid-ask spreads. An exogenous variation in AI availability due to ChatGPT outages led to notable reduction in retail-AI alignment and reversal of narrowed bid-ask spreads, further supporting the causal role of AI. Overall, the study suggests that democratizing AI has the potential to level the playing field and bridge the information gap between privileged and ordinary investors.