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 their actual green investments. We find that greenwashing funds charge higher fees while attracting greater investor flows. Moreover, investors appear more lenient and less sensitive to poor performance in greenwashing funds, incentivizing underperforming funds to adopt this practice. However, greenwashing funds face higher regulatory and reputational costs, as reflected in ESG-related SEC comment letters and subsequent investor outflows. 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.

Using unique data from LinkedIn profiles, we measure the adoption of AI technologies by mutual fund managers. Compared to low-AI funds, high-AI funds generate superior returns and incur lower expenses. AI outperformance is particularly strong among discretionary funds, which rely on human judgment, as opposed to quantitative funds. The greater the AI adoption, the more pronounced the time-varying skill of fund managers across different market conditions. The stock-picking abilities of high-AI funds improve with the availability of big data, such as satellite imagery of parking lots. The local availability of AI skills is a key determinant of cross-sectional variation in mutual fund AI investment. Our findings are robust to using geographic variation in AI supply as an instrument for AI utilization by mutual funds.

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.

Existing research argues that many target date funds feature excessive fees and suboptimal allocation choices, and that investors could achieve superior outcomes by self-directing their retirement accounts. This paper provides evidence that a first-order benefit of target date funds is their impact on investor behavior. Participants in a laboratory experiment are less likely to eliminate equity allocations following a stock market crash when they are invested in a target date fund. In a carefully calibrated simulation exercise, this behavioral benefit of target date funds provides significant increases in welfare even with economically meaningful fees and distorted glide paths.

Mutual funds face the risk of withdrawals if they perform poorly in the short term, which encourages manager myopia. We show that fund families can insulate managers from this funding pressure via compensation tied to long-term fund performance. Managers with long-horizon contracts are more likely to undertake long-term investments and outperform their constrained peers. Since long-horizon pay does not shut off the funding pressure but simply insulates the manager from it, not all families can offer these contracts. Long-horizon contracts are more prevalent in families that cater to patient investors and have more resources to buffer liquidity shocks.

Using confidential administrative data from the U.S. Census Bureau, we examine

whether race and gender affect compensation and career outcomes in the U.S. asset management industry. We document substantial compensation gaps: female portfolio managers earn 27% less than male peers, and minority managers earn 20% less than White peers. Female and minority managers also face significantly higher rates of forced turnover, and female managers are less likely to be rehired following job separations. These gaps can not be explained by differences in qualifications: minority managers are more likely to attend elite schools and hold advanced degrees. Nor do they reflect differences in performance: we find no systematic disparities in investment performance or in the ability to attract investor flows. Importantly, we show that greater diversity among asset management firm owners helps mitigate these disparities. Together, these findings challenge the notion that meritocratic industries are immune to discrimination and raise concerns about taste-based discrimination and potential talent misallocation in an industry central to capital markets.

This paper constructs the first comprehensive database of U.S. diverse-owned hedge fund managers by leveraging image recognition technology alongside extensive manual verification. Our analysis reveals that, as of the end of 2022, only 1.1% of U.S. hedge fund industry assets are managed by firms owned by women or historically under-represented racial/ethnic minorities. These firms appear to encounter significant barriers to raising capital, expanding their asset base, and winning mandates from large asset owners, challenges that persist after social movements such as Black Lives Matter and the MeToo campaign. At the employee level, hedge funds show under-representation of Blacks and Hispanics, and women in senior and investment roles relative to both the U.S. labor force and the financial services industry. Promotion rates for these groups also lag behind those of their White, male counterparts. Women are more often hired into non-investment positions like investor relations and compliance than into investment positions, but minority-owned firms tend to employ more minority professionals. Despite external pressures on, and stated internal efforts by, the asset management industry to increase diversity of ownership and investment teams, our findings suggest that there has been no material change.

Does political partisanship shape the investment performance of professional fund managers? We find that hedge funds that hold stocks that are strongly aligned with the incumbent president's economic policies underperform funds that hold stocks that are poorly aligned with the incumbent president's economic policies by 4.44% per year after adjusting for risk. In line with a partisanship bias story, our findings are driven by managers who are politically aligned with the incumbent president and stronger when (a) fund managers are highly partisan, (b) sentiment diverges between Democrats and Republicans, and (c) conflicts occur between the President and US Congress. Following exogenous shocks that heighten partisan bias such as mass shootings and political protests, politically aligned funds increase their exposures to politically aligned stocks, leading to greater underperformance. Our results extend to mutual funds and suggest that political partisanship can be detrimental for investment management.

Using dealer surveys and hedge fund regulatory filings, we study how prime brokers' credit supply affects hedge fund performance. Hedge funds with more access to credit from their prime brokers subsequently increase borrowing and generate higher returns and alphas. This effect is stronger for funds relying on fewer prime brokers and those using borrowing rather than derivatives for leverage. Credit supply is particularly impactful during market stress and when arbitrage opportunities are abundant. Prime brokers allocate credit based on hedge funds' profit potential. Our findings support models of leverage constraints where less constrained investors hold higher-alpha portfolios and use leverage to amplify returns. Our findings also explain return co-movement among hedge funds sharing a prime broker and the outperformance of large funds with diverse credit sources.

We study how changes in third-party relative performance evaluation (RPE) shapes mutual-fund managers’ incentives. Exploiting Morningstar’s 2002 shift from a single U.S. equity peer group to size–style categories and its 2016 introduction of ESG Globe ratings, we show that incomplete benchmarking disadvantaged certain funds and induced risk-taking. Pre-2002, growth funds received lower star ratings and held higher-beta, more volatile portfolios—especially when the value spread was large. Similarly, before the globe rating introduction, ESG funds held higher risk stocks with lower ESG ratings. These higher risk holding effects disappear after both the 2002 and 2016 Morningstar reclassification events.