We develop an approach to jointly study four dimensions of hedge fund value creation—its drivers, split, dynamics, and optimality. This approach captures the large fund heterogeneity and controls for hedge fund complexities. We find that most funds add value via their unique skills but face strong scalability constraints—a feature that prevents them from systematically dominating mutual funds. Hedge fund investors slowly improve their fund capital allocation over time, which suggests an impactful but noisy learning process. Despite these efforts, they extract a modest fraction of the total value. These findings fit reasonably well with an equilibrium model featuring funds with heterogeneous skill and scalability and investors with limited bargaining power.

I study analyst reports for worldwide equity mutual funds to examine two key features of active management models: investor learning and decreasing returns to scale. Flows respond to report tone over and above analyst ratings, especially for direct-sold and young funds, indicating that some investors use qualitative research when allocating capital. Report tone also predicts future abnormal returns. Using dictionary and machine learning methods, I show that analysts emphasize fund size and capacity when assets deviate from model-implied optimal levels, providing signals about under- or overcapitalization and thus positive-NPV opportunities. A composite of tone and capacity language strongly predicts returns.

This paper examines the benefits and costs of greenwashing in mutual funds. We identify greenwashing funds by analyzing their ESG-related disclosures using large language models (LLMs) alongside green investments. Greenwashing funds charge higher fees while attracting greater flows, with investors exhibiting tolerance for poor performance. However, they face higher regulatory and reputational costs. ESG-related comment letters issued by the SEC trigger outflows from greenwashing funds, spilling over to non-greenwashing funds within the same family. SEC's scrutiny reduces future green disclosures, but its effectiveness weakens when SEC faces human capital constraints. Finally, institutional and retail investors respond differently to greenwashing behavior.

This paper analyzes the impact of disclosures of sustainable investment targets under the EU Sustainable Finance Disclosure Regulation (SFDR) on mutual fund flows. Using a staggered difference-in-differences setup and focusing on retail-oriented index funds, we find that sustainable investment targets have a temporarily positive impact on fund flows in comparison to funds without sustainable investment targets. Furthermore, we find a negative linear relationship between sustainable investment targets and fund flows. While lower targets attract higher fund inflows, higher targets result in significantly lower or even no inflows. Our results suggest that up to a target level of 20% in sustainable investments, index funds can attract more inflows. This suggests a trade-off between sustainability commitments and performance considerations.

The assets under management of option-trading exchange-traded funds (ETFs) have grown more than 120-fold since 2018. This paper examines how flow-induced demand pressure and exogenous rollover trade demand pressure from option-trading ETFs affect the implied volatility surface. I show that demand pressure from these ETFs significantly affects implied volatility surface, with the magnitude of the effect varying with option characteristics—particularly moneyness and days to expiration—due to differences in option vega. In addition, liquidity frictions also explain the magnitude of impact. These findings suggest that flow-induced demand pressure plays an important role in shaping both the term structure and moneyness curve of implied volatility.

We investigate prices around timestamped informed trades using approximately 500,000 13D transactions from activist investors matched to TAQ trades. Activists are more price sensitive than non-activists, and are more likely to attempt to hide their trading by strategically choosing when and how to trade. Activists have lower execution quality, higher price impact, and lower realized spreads, suggesting that activists, on average, fail to hide among the uninformed. Activists with less information (as measured by lower returns) are better at hiding (that is, they have better execution quality). These results are reversed for hedge funds: hedge funds with better execution quality generate higher returns.

We develop a characteristic-based asset demand model in which cross-asset risk-return trade-offs vary with asset characteristics. The model relaxes the uniform substitution structure of the multinomial logit (MNL), accommodates large price elasticities, and enables recovery of investor-specific primitives, including alphas and factor loadings, from structural demand estimates. Applied to U.S. institutional equity holdings from 2000 to 2022, the model reveals meaningful deviations from MNL substitution patterns, particularly along the market equity dimension. The estimated average own-price elasticity is 77 percent higher than under the MNL, driven largely by investors whose portfolios imply cross-asset complementarity. Nonetheless, both elasticity estimates are substantially lower than those implied by CAPM calibrations. The model also uncovers heterogeneity in investor alphas: hedge funds earn near-zero alphas, while brokers earn up to five basis points annually.

This paper studies how securities lending affects mutual funds’ monitoring of portfolio firms. Contrary to the view that lending weakens governance by separating voting rights from ownership, I show that securities lending can enhance monitoring by revealing information embedded in short-selling demand. Using mutual fund voting data, I show that funds engaged in securities lending exhibit higher voting performance (“vote alpha”) around shareholder meetings, aligned with ex-post value-enhancing proposals. Lending funds vote more frequently and are less likely to support management in firms whose shares they lend, particularly on contentious agenda items, where management and the proxy advisor disagree, and on contested proposals, where outcomes are uncertain and votes may be pivotal. These effects are significantly stronger for funds that employ affiliated lending agents, consistent with an information transmission channel rather than selection. Overall, the evidence indicates that securities lending can strengthen mutual funds' role in corporate governance.

In this paper, we investigate the drivers of a recent shift in asset allocation away from Europe and toward the United States as documented in the factbook of the European Fund and Asset Management Association. We show that this shift is concentrated among funds that receive an ESG rating—specifically, Morningstar’s Globe Rating. This pattern highlights the potential role of ESG information in shaping cross-regional capital flows. Our hypothesis is that the supply of high ESG rated funds with an EU investment focus might be limited due to coverage issue of European issuers by ESG rating agencies and/or due to the financial materiality of the ratings (vs double materiality proned in Europe). This interpretation is consistent with our results showing that, after the introduction of SFDR 1.0, funds’ geographical allocations become even more sensitive to ESG issuer coverage, especially in Europe.

Using LinkedIn profile data, we measure AI adoption by mutual fund advisers and show that high-AI funds outperform low-AI funds. This outperformance is concentrated among discretionary funds and among funds managed by more experienced managers, consistent with AI complementing human judgment rather than replacing it. Higher AI adoption is associated with stronger time-varying managerial skill---improved stock picking in normal times and superior market timing during periods of elevated risk. The stock-picking ability of high-AI funds further improves with access to large, unstructured data, such as satellite imagery. Finally, we show that local AI labor supply predicts cross-sectional variation in AI adoption, and our results are robust to an instrumental-variable strategy based on geographic variation in AI skill availability.

This paper examines AI adoption in asset management and its investment implications. We document that AI-driven investing is concentrated among hedge funds, particularly those employing macro strategies. AI funds exhibit greater alpha comovement and are launched by investment advisers facing stronger performance incentives. These funds significantly outperformed non-AI hedge funds on a risk-adjusted basis, but their outperformance declined over time and disappeared after 2018, consistent with decreasing returns to scale. Nevertheless, AI funds continued to outperform sibling funds managed by the same advisers. Our findings highlight both the alpha-generating potential and the limitations of AI as a source of investment performance.

This paper derives the implications of a time gap between the publication of the disaggregated ESG information and the final ESG scores. We use early ESG raw data to reconstruct the scores of MSCI and build a portfolio long in the stocks which ESG score will be upgraded and short in the stocks which ESG score will be downgraded We show that because ESG information is material for financial performance, asset managers can trade on this disaggregated ESG information, before it is known to every player on the market and gain from it. Consistent with the idea that ESG scores incorporates fundamentals which are predictive of performance, we find that timing the announcement of ESG scores yields a significant 0.22 % monthly alpha. Additionally, we identify a subsample corresponding to 13.8 % of the active equity funds which use this strategy and confirm that these funds have a tendency to trade stocks prior to changes in ESG scores.

In this paper, we examine barriers of entry to conduct trading. Our survey-based evidence shows that industrial firms see large profitable trading opportunities in the product markets as well as in their own shares. Yet, according to the same survey, those opportunities are rarely exploited. Similarly non-bank financial institutions, apart from the hedge funds, see trading opportunities that they choose not to utilize. What prevents most non-financial firms and non-bank financial firms from entering the lucrative markets of trading, where hedge funds and merchant traders operate. We examine this topic both from an agency theoretical perspective as well as based on the evidence from an executive survey.