The Journal of Alternative Investments

Exploring European Private Equity and Venture Capital Market Drivers

Eduard Bossauer — 2026-06-15

Despite its increasing significance within the global private equity industry, the European markets remain academically underdeveloped. To enhance understanding, we conducted an analysis of a Preqin dataset encompassing 433 private equity funds from 2000 to 2021, to identify performance factors relevant to European markets. The application of generalized least squares (GLS) regressions reveals that sector-based specialization effects exert a positive influence on the net internal rate of return (Net IRR) of private equity funds. Our findings indicate a weak negative impact of fund sequencing, which challenges the prevailing assumption regarding the importance of track records. This result highlights the need for further investigation into additional influential factors to cultivate a comprehensive understanding of effective management strategies within the private equity sector. In addition, the study emphasizes the critical need for enhanced data provision concerning illiquid investments beyond North American markets, thereby establishing a foundation for future research on the determinants of European private equity markets.

Three-item list of highlights

Buyout strategies predominantly influence European private equity markets. This analysis focuses on a strategy that is posited to have a positive impact on the Net Internal Rate of Return (IRR) during the observed period, underscoring the significance of comprehending market dynamics.
The frequently referenced importance of track records appears to be a subjective factor, as the regression results do not substantiate a positive correlation between fund series and performance metrics.
Furthermore, broader market indices, which are suggested to be affected by a distinct set of influential factors regarding performance, are attributable to a lack of connection to the private equity markets, as indicated by the regression outcomes.

Explaining DeFi Token Returns: Do Protocol Metrics and Broader Crypto Trends Matter?

Vera Larionova — 2026-06-15

This study examines the drivers of DeFi token returns, focusing on protocol metrics and broader market trends. While previous research has primarily analysed a limited set of factors, this study incorporates additional indicators, including such groups of metrics as protocol activity indicators, income statement metrics, treasury balances, market efficiency metrics, and valuation multiples. Using weekly data from January 2021 to February 2024 for three major DeFi tokens (COMP, AAVE, CRV), we apply linear regression to test the relationship between token returns and internal protocol metrics. Our findings show that COMP and AAVE exhibit strong associations, mainly with Ether price movements, whereas CRV is also linked to financial metrics such as net treasury and TVL. These results suggest that DeFi token returns are shaped both by market-wide conditions and by protocol-specific characteristics, with the relative importance of these factors varying across different token types. This study contributes to the decentralized finance literature in three key ways: (1) we conduct the first comprehensive analysis linking governance token performance to five distinct categories of protocol-specific metrics; (2) we demonstrate significant variation in valuation drivers between different protocol types (lending vs. DEX); and (3) we challenge the assumption that DeFi tokens simply track Ether's price movements. Our results establish that protocol-specific factors must be accounted for in any comprehensive valuation model of DeFi assets.

Hype Cycles in Venture Capital Investments, Funding Round Decisions and Top-tier Investors

Christian Hennings — 2026-06-15

Hype cycles in venture capital (VC) are characterized by surging expectations that attract substantial investor attention and capital inflows. This study examines how investment timing shifts during such hype waves for the overall VC market and for “top-tier investors”, firms managing more than USD 10 billion in assets, using comprehensive data from PitchBook. We distinguish between level-based hype, defined by high vertical popularity, and growth-driven hype, defined by sudden spikes in deal activity. The findings reveal that growth-driven hype leads the overall VC market to move into earlier financing stages, while level-based hype has only marginal effects. “Top-tier investors” exhibit a particularly strong adjustment, investing almost a full financing stage earlier than the market average in hype verticals. This shift marks a temporary departure from their typical late-stage orientation. The results indicate that accelerated entry is not a structural feature of investor experience but a context-dependent reaction to intensified competition and visibility pressures.

Drivers of Cryptocurrency Returns - An Analysis through the Lens of Sustainability

Alicia Billand — 2026-06-15

As sustainability plays an increasingly important role in finance, understanding its influence on emerging assets such as cryptocurrencies is essential for portfolio
management. This paper analyzes the relation between sustainability and cryptocurrency returns. To address and reveal complexity in relationships, we use non-linear machine learning methods. We find that sustainability variables, like the energy consumption and environmental attention, are important return determinants, whereas the distinction between green and dirty cryptocurrencies as a proxy for sustainability, is not a return determinant per se. In general, individual cryptocurrencies show a heterogeneous exposure towards energy consumption and environmental attention. We detect a clear upward trend in complexity over time, with maxima during Covid-19 and the change of Ethereum’s consensus mechanism from Proof-of-Work to the more environmentally friendly alternative Proof-of-Stake. These findings underline the importance of considering sustainability factors in cryptocurrency investment decisions, offering new insights for portfolio managers as well as regulators.

Equalization of Performance-Based Fees

Francois-Serge Lhabitant — 2026-06-15

This note discusses the complexities of calculating periodic performance-based fees (PBF) in open-ended funds. While the basic calculation, i.e. applying a fixed percentage to gross performance, appears simple, practical challenges arise from diverse fee structures, high-water marks (HWM), varying investor entry and exit points, and differing subscription periods. These factors can lead to inequitable fee allocations and discrepancies in reported performances for investors with different entry points. Academic literature often overlooks these nuances, leaving gaps in understanding for students and practitioners. This note uses detailed examples to illustrate these challenges and explores methods funds employ to ensure fair treatment of investors. It also highlights the trade-offs of each approach, emphasizing the importance of clear fund documentation.

Crypto-Equity Diversification in the MPT Framework

Pat Obi — 2026-06-15

This study examines the diversification and sustainability implications of incorporating digital assets into global equity portfolios, extending earlier work that focused primarily on cryptocurrency-specific risk or return-factor behavior. Using multi-year daily and monthly data from four major equity indices and four leading digital assets, Modern Portfolio Theory is applied to evaluate risk-return trade-offs and efficient frontier outcomes. In contrast to prior studies, this analysis considers both speculative cryptocurrencies and stablecoins, enabling a comprehensive assessment of mixed traditional–digital portfolios across different market regimes. The results show that cryptocurrencies exhibit weak correlations with equities and can enhance portfolio efficiency, whereas stablecoins serve as low-volatility anchors within digital asset allocations. Sustainability considerations are also examined, with attention to the reduced energy intensity associated with proof-of-stake validation. Overall, the findings support selective and strategically constrained digital-asset allocations, provided that regulatory conditions, governance standards, and environmental practices are robust.

GMAM (Generalized Multi-Asset Model)

Arnav Sheth — 2026-06-15

We introduce the Generalized Multi-Asset Model (GMAM), a novel hierarchical Bayesian state-space framework designed to estimate latent (de-smoothed) economic returns and factor exposures for illiquid alternative investments, including private equity, private credit, real estate, and hedge funds. GMAM addresses key challenges such as stale NAVs, return smoothing, and sparse data by incorporating a Bayesian moving-average process and stochastic search variable selection (SSVS). Using a sample of 57 funds on our platform, GMAM outperforms ordinary least squares (OLS) in matching predicted returns, achieving a mean out-of-sample R² of 21.96% versus 17.02% for OLS. In de-smoothing diagnostics, GMAM largely recovers the dynamics of an artificially smoothed S&P 500 series and reverses NAV-induced volatility compression across funds, with a median reported-to-latent volatility ratio of 0.56. GMAM contributes a scalable, probabilistic approach to modeling alternative assets, bridging the gap between academic theory and practitioner needs.