ABSTRACT Recent US election cycles have revealed a startling bifurcation in the political allegiances of finance capitalism. The current configuration of alliances places one faction of finance capital, consisting of private equity, hedge funds and venture capital, firmly on the side of the Trumpian GOP, while the other faction, extending to mutual and index fund managers, has become increasingly dependent on the Democrats' policy agenda. By tracing the evolution of New Deal securities law in the 1980s and beyond, this article seeks to explain how this organisational and factional divide fell into place and what it tells us about the evolving commitments of Democrats and Republicans respectively.

This paper successfully replicates Kosowski, Naik and Teo (2007) and Jagannathan, Malakhov and Novikov (2010), two seminal studies of hedge fund performance persistence. The authors show that top funds continue to persist in a more recent sample, even when using novel “real-time” data that approximates an investor’s actual information set. The persistence available to investors has substantially weakened, however, and is only observed when using Kosowski et al.’s Bayesian alpha to predict performance. The authors identify the econometric source of the superiority of Kosowski et al.’s methodology and show that the decline in performance persistence is associated with decreasing returns to scale for superior funds.

ABSTRACT Combining hedge funds’ quarterly position information with their access records on the SEC's EDGAR server, we explore whether hedge funds proactively gather and analyze textual information in 10‐K filings related to their stock holdings, and how these behaviors affect their positions. We find that hedge funds adjust their positions according to the textual information in the annual reports they download. Meanwhile, analyzing these reports helps them to generate excess returns. Overall, our evidence suggests that the textual content of annual reports contains crucial company insights, prompting a subset of hedge funds that engage in bulk downloads from the SEC's website to trade based on diligent analysis of 10‐K filings.

Abstract We argue that the effective price of investing in hedge funds far exceeds the headline fee rate of “2-and-20.” In a large 22-year sample of hedge funds, we find that 60% of the gains on which incentive fees are paid are eventually offset by losses. As a result, the effective incentive fee rate is 50% vis-à-vis the nominal 20% rate. The tendency of investors and managers to disinvest capital after negative returns contributes to this phenomenon by causing the crystallization of underwater fees and net losses as well as by eroding the protection intended by the high-water mark provision. (JEL D24, G11, G23, J33)

The contemporary financial landscape is characterized by high-velocity data streams and extreme volatility, often overwhelming retail investors who lack the computational resources of institutional hedge funds. This paper proposes "FinAgent," an advanced Agentic AI-powered web application that synthesizes quantitative market data with qualitative sentiment analysis to provide professional-grade investment advisory. The system integrates real-time API hooks into the NSE and BSE exchanges to extract fundamental ratios and technical indicators. A Long Short-Term Memory (LSTM) neural network is deployed for predictive price modeling, while a Large Language Model (LLM)-based agent performs heuristic reasoning across financial reports and global news trends. Our methodology utilizes a weighted scoring model (40% fundamentals, 40% technicals, 20% sentiment) to ensure that the final Buy/Sell recommendation is a reasoned conclusion based on multi-source data. Experimental results demonstrate that this approach yields a 12.5% annualized return, outperforming the S&P 500.

This study investigates the causal impact of hedge fund activism (HFA) on market liquidity. The empirical results show that HFA leads to a deterioration in stock liquidity, with the effect being more pronounced in firms characterized by greater information asymmetry and financial constraints. The decline in liquidity is also more evident in cases of high-intensity campaigns, led by funds with weaker market reputation, and that engage more frequently in activist interventions. Additional analyses reveal that price efficiency, corporate information flow, and operating complexity contribute to liquidity decline. This evidence holds using several liquidity metrics and sensitivity tests, and we rule out any potential endogeneity concern using an exogenous setting in our Difference-in-Differences regression analysis. Overall, this study underscores the disruptive influence of HFA on corporate dynamics and its wider market repercussions.

When measured with daily return data, hedge fund factor exposures are more statistically significant than when measured with monthly data. Consequently, daily data can significantly improve investors’ hedge fund portfolios. Furthermore, daily data reveal that hedge funds have lower alphas and are exposed to more factors, and these exposures are more time-varying, than monthly data suggest. Analysis of hedge fund exposures with a comprehensive universe of daily variables reveals the importance of a new commodity put factor and of new liquidity timing effects for factors other than the market.

Correlations between financial assets are not stable constants amenable to simple historical estimation. They are regime-dependent, liquidity-sensitive, and structurally fragile quantities that reveal the internal coherence of financial markets with far greater accuracy than individual asset prices or volatility measures. This paper develops a comprehensive quantitative framework for detecting market fragility through the analysis of correlation breakdown patterns. Drawing on modern financial econometrics, network theory, and operational hedge fund risk management practice, we examine the statistical mechanics of correlation instability, including Dynamic Conditional Correlation models, minimum spanning tree topology, and the Absorption Ratio as a systemic fragility metric. We analyse how forced deleveraging, crowded positioning, and synchronized risk management systems transform correlation structures during market stress, and document how these transformations manifest across equities, fixed income, credit, and derivatives markets. We present the Verma Research Capital (VRC) Fragility Score: a proprietary composite metric that aggregates signals across factor correlations, cross-asset divergences, implied versus realised correlation spreads, and network connectivity measures. The framework is illustrated through three historical dislocations: the March 2020 COVID-19 selloff, the 2022 simultaneous equity and bond drawdown, and the February 2018 volatility spike. We conclude with a detailed discussion of how correlation regime signals inform position sizing, dynamic risk budgeting, hedging construction, and liquidity management at the portfolio level. The central argument is that market fragility is not a price event but a structural condition, and that systematic correlation analysis provides the most reliable early warning system available to the quantitative hedge fund practitioner.

Integrating insights from research on newcomer socialization and generalization of learning, we theorize that individuals’ prior mobility across organizations may offer learning opportunities that mitigate the barriers they face as newcomers transitioning into subsequent employment contexts. Building on evidence that individuals experience a temporary decline in performance when they switch employers, we argue that more mobile individuals will experience a smaller performance decline and shorter time to performance recovery following entry into a new employer as they are better equipped to adapt to the norms, values, and expectations of new organizational social contexts. We further theorize that the benefits associated with prior mobility are likely to be greatest when newcomers’ social integration into a new organization is more challenging or more central to their new role. We find overall support for our theory using data on moves made by 8,693 hedge fund managers between 2,129 firms in the U.S. hedge fund industry from 2004 to 2019. We contribute to research on the influence of individuals’ career experiences on their transitions into new organizations, and offer insights to ongoing conversations related to the implications of employees’ investments in firm-specific human capital.

The hedge fund industry has undergone significant consolidation, with capital increasingly concentrated among large multi-strategy platforms. Yet boutique managers, defined as firms with $200 million to $1 billion in assets under management, have exhibited notable resilience. This article examines how governance architecture, organizational scale, and strategy capacity interact to shape competitive outcomes in a consolidating industry. I analyze the structural advantages of large platforms, including regulatory infrastructure, distribution networks, and technological scale, alongside the distinctive governance features of boutiques, such as concentrated ownership, incentive alignment, and strategic specialization. The evidence suggests that performance dynamics are strategy dependent rather than purely size dependent: Boutiques tend to outperform in capacity-constrained strategies, whereas larger firms benefit from economies of scale in liquid markets and systematic strategies. I argue that optimal fund size exists along a strategy-specific continuum and that effective allocator decision-making requires evaluating governance structures, operational resilience, and capacity discipline rather than relying on asset size alone.

Abstract State‐administered pension plans report paying roughly $20 billion each year in fees to external asset managers, much of it for high‐cost, high‐risk “alternative” assets such as private equity and hedge funds. These outcomes involve trillions in pension investments that affect the retirement security of millions of public sector workers and the budgets of every U.S. state. Yet, a consistent finding in the finance literature is that these managers tend to underperform broad market indices after accounting for fees. Politically appointed members of pension boards may nonetheless rationally favor these strategies, given the short‐term political incentives of those who appoint them. Drawing on the “political embeddedness” framework, this study assesses whether and how the share of politically appointed members on pension boards influences asset allocation and fees. Using panel data for 66 state‐administered plans from 2019–2022, combined with fee data from annual reports, we estimate plan‐level fixed effects models to examine investments in alternative assets and fees paid by pension systems. We find that, among boards with statutory authority over asset allocation, adding one political appointee (≈10 percentage points) is associated with a 1.2 percentage point increase in alternative asset allocations and a 5–7 percent increase in total fees, even after adjusting for asset composition. These results align with the short‐term incentives of political appointees and underscore how board governance shapes investment outcomes. The prevalence of missing fee data—especially for alternative assets—further highlights the need for greater transparency in fee reporting.

Research methodology This case is solely based on published sources. These secondary sources include company websites and news releases of major artificial intelligence (AI) firms, AI industry reports such as Stanford Artificial Intelligence Index Report, news reports that have special coverage of tech news, such as the Wired, Hardware Corner, Venture Beat and Tech Radar, popular AI platforms at GitHub and Hugging Face and AI research paper repository in AI communities at Cornell arXiv. Case overview/synopsis DeepSeek was an AI startup founded in 2023 in Hangzhou, China. The company was fully funded by a hedge fund HighFlyer, whose co-founder and CEO, Wenfeng Liang, also served as DeepSeek’s CEO. The company aimed to develop large language models (LLMs) and ultimately to build Artificial General Intelligence (AGI). Due to US sanctions and embargo on advanced hardware exports to China, however, DeepSeek had to invent novel algorithms and model structures to develop stronger model capability with limited hardware resources. As such, the key to success for DeepSeek was to develop potentially game-changing architectural and algorithmic innovations. On December 26, 2024, DeepSeek launched a chat model, DeepSeek V3, with high performance at very low training costs. The initial benchmark tests indicated that DeepSeek V3 model outperformed Llama 3.1 and was comparable to GPT-4o and Claude 3.5 Sonnet. Yet, the company claimed to have trained its models in just two months at a total cost of merely $5.6m. That was the annual salary for one of those AI experts working at Meta. Initializing from the V3 model and sharing the V3 architecture, DeepSeek was planning to release its first chatbot application, the DeepSeek-R1 reasoning model, for iOS, Android, Web and application programming interface on January 20, 2025. Up to the release of the V3 model, DeepSeek’s algorithms, models and training details had been open-source, allowing its code to be used, viewed and modified by others. For the planned release of DeepSeek-R1, the company had to decide whether to continue the open-source policy or to choose closed-source, like most other firms were doing. Complexity academic level This case may be used in upper-level undergraduate and graduate classes in the fields of strategic management, innovation or business ethics. In strategic management classes, it can help students to analyze a firm’s external environment and internal strengths and weaknesses and then learn how to take advantage of the strengths to neutralize the weaknesses to cross industry entry barriers. In innovation classes, the case can help students understand how innovation takes place and how innovation can help to break industry entry barriers. In business ethics classes, students can learn how to balance firm performance and corporate social responsibility in decision-making.

A Hedge Fund in Your Garage: Automobile purchases under gasoline price uncertainty

Hedge fund strategies performance: The edge of Omega ratio over conventional metrics

• We propose a new measure for hedge funds’ style-selection skill. • We test the performance implications of style-selection skill of hedge funds. • Funds exhibiting greater style-selection skill enhance the probability of survival. • We find style-selection skill persists consistently over a time period of one year. • It opens a new perspective for managerial skills and supports investors. A distinctive feature of hedge funds is their dynamic style of trading; hedge funds may shift the investment style in their lifetime. Style shifting is a strategic decision for funds which is beyond the more traditional stock-picking and market-timing carried out at the operational level. This paper tests and validates the performance implications of style-selection skill of hedge funds. Based on the trading style identification through Probabilistic Principal Component Analysis and the measure of style-selection skill developed in this paper, we find that such skill has predictive power for future fund performance, persisting for up to one year. In addition, our findings reveal that funds exhibiting greater style-selection skill enhance the probability of survival. Furthermore, we show that smaller, solo-managed funds operated by managers with longer tenure and higher management fees tend to have greater style-selection skill. Our findings support investors’ decisions when selecting hedge funds. It also opens a new perspective for managerial skills in active money management, reflecting managers’ expertise in data processing about micro and macro information and shocks to achieve success, when considering the investment style.

Using an extensive matched hedge fund-prime broker panel dataset for the period 2001–2021, we document a strong positive relationship between hedge fund leverage and prime broker’s stock price crash risk after controlling for other crash risk drivers. Our results are not only statistically, but also economically significant, showing that a one-standard-deviation increase in hedge fund leverage is associated on average with an increase of around 5% of a standard deviation in the negative skewness or the down-to-up-volatility of bank stock returns. Moreover, they remain robust when accounting for endogeneity and conducting many robustness checks. We also document that some investment strategies, such as one focusing on fixed income, appear to decrease the slope of the risk metrics of prime brokers, and ultimately leading to lower stock price crash risk.

This study investigates the conventional wisdom that financial assets with higher risk levels should yield higher returns, a concept known as the risk-return trade-off. However, empirical research indicates that financially distressed assets tend to yield lower returns. Although several potential explanations have been proposed, there remains a lack of consensus in the literature regarding the underlying causes. Motivated by this puzzle, this study aims to ascertain whether distress risk is present in the hedge fund industry. To this end, this study empirically analyzes hedge funds monthly returns over a fourteen-year period from January 2000 to August 2016 and research is based upon a sample of 7151 hedge funds. The data were further segmented to capture both bull and bear market conditions and various hedge fund strategies. The results demonstrate that the distress risk puzzle is evident in the hedge fund industry. The findings suggest that hedge funds with a high probability of default do not yield higher returns, whereas those with a low probability of default yield higher returns. This study indicates that hedge funds with a high probability of default are riskier, and investors are not adequately compensated for investing in these funds.

Hedge Funds and Financial Stability: A Review of the Evidence 

Hedge Funds, Leveraged Finance and Safe Assets: A Look Through the Lenses of the Repocalypse

This paper examines the complexity of hedge fund strategies within the global economy and highlights the ongoing need to better understand their operations, particularly during periods of financial instability. Hedge funds are distinguished by their flexible investment mandates, use of advanced financial instruments, and active risk-taking approaches, which can significantly influence market dynamics. The paper discusses the classification of hedge funds based on factors such as investment style, geographic focus, and trading strategies, emphasizing the diverse nature of the industry. It also analyses key hedge fund strategies, including arbitrage, event-driven, distressed, and merger strategies, outlining their objectives and associated risks. The conclusion underscores the importance of increased transparency, stronger regulatory oversight, and improved disclosure to mitigate systemic risks. Furthermore, it calls for continued research to enhance understanding of hedge fund behaviour and their impact on global financial markets, supporting more informed investment and policy decisions.