As sustainability plays an increasingly important role in finance, understanding its influence on emerging assets such as cryptocurrencies is essential for portfolio management. This article analyzes the relation between sustainability and cryptocurrency returns. To address and reveal complexity in relationships, we use nonlinear machine learning methods. We find that sustainability variables, like energy consumption and environmental attention, are important return determinants. The greenness of a cryptocurrency measured by the consensus mechanism is a group-specific differentiation variable for the most sustainable cryptocurrencies with a positive impact on their returns. The economic relevance of their green consensus mechanism materializes primarily in the lower tail of the return distribution by providing downside protection. We detect a clear upward trend in complexity, 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 investors as well as policymakers.
This article examines the diversification and sustainability implications of incorporating digital assets into global equity portfolios. Using multiyear daily and monthly data for four major equity indexes (S&P 500, CAC 40, DAX, and FTSE 100) and four digital assets (Bitcoin, Ethereum, Tether, and USDC), our study applies the Markowitz mean-variance framework to construct efficient portfolios and evaluate regime-dependent risk-return trade-offs. Unlike in earlier studies that focus primarily on speculative cryptocurrencies, we incorporate stablecoins as digital cash equivalents, enabling a broader assessment of hybrid equity-crypto portfolios across different volatility environments. The results show that cryptocurrencies exhibit persistently weak correlations with global equities and can improve portfolio efficiency when optimally weighted. Stablecoins-although generating minimal returns-serve as low-variance anchors that stabilize digital asset allocations. Overall, ourfindings suggest that modest allocations to digital assets can enhance portfolio diversification, though their long-term institutional suitability depends on credible regulatory oversight, transparent governance, and sustainable technological architectures.
This study examines the drivers of decentralized finance (DeFi) token returns, focusing on protocol metrics and broader market trends. While previous research has primarily analyzed 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 March 2025 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 Total Value Locked (TVL). These results suggest that DeFi token returns are shaped both by marketwide 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 track Ether's price movements. Our results establish that protocol-specific factors must be accounted for in any comprehensive valuation model of DeFi assets.
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 R-2 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.
Private equity performance metrics recognize credit risk only when losses materialize, ignoring embedded default risk from the moment leverage is deployed. We develop a framework integrating Expected Credit Loss (ECL) methodology with J-curve analysis to quantify this forward-looking risk dimension. The framework complements rather than replaces traditional metrics, revealing probability-weighted downside exposure that peaks during re-commitment decisions for successor funds. Illustrative calibrations suggest embedded credit risk varies meaningfully with vintage year, sector concentration, asset composition, and GP quality - though external validation requires proprietary portfolio company default data that remains unavailable. We discuss potential applications for portfolio construction and performance evaluation.
This article discusses the complexities of calculating periodic performance-based fees (PBFs) in open-ended funds. While the basic calculation of applying a fixed percentage to gross performance appears simple, practical challenges arise from diverse fee structures, high-water marks (HWMs), 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 article uses detailed examples to illustrate these challenges and explores methods that funds employ to ensure fair treatment of investors. It also highlights the trade-offs of each approach, emphasizing the importance of clear fund documentation.
We develop a reproducible three-step protocol to clean daily cryptocurrency data from CoinMarketCap, one of the most used data providers in academic research. The procedure targets three recurring anomalies that distort market-level indicators: (1) extreme market-cap spikes, (2) one-day and multiday dips in Bitcoin dominance, and (3) abnormal trading volumes. Using more than 28,000 cryptocurrencies from 2014 to 2024, we show that the method modifies only a small subset of data while improving the reliability of key market indicators. We do not adjust prices or returns, preserving actual trading conditions. As an application, we construct dynamic investable universes using cleaned data and realistic constraints based on market capitalization and volume. This exercise shows that cleaning and filtering jointly produce more reliable universes, reducing spurious extremes and making them suitable for empirical asset pricing research and portfolio construction.
This research aims to explore the risk profile of the value-oriented environmental, social, and governance (ESG) portfolio in the United States stock market using explainable artificial intelligence (XAI) approaches. To achieve this, we use a range of key market, risk, and uncertainty indicators. The results indicate that the value ESG portfolio is significantly influenced not only by the stock market but also by other factors-particularly cash flow-related indicators such as default risk and bond-market conditions. We observe an approximately linear relationship between the value ESG portfolio and the most significant factors. Specifically, higher (lower) values in the stock and bond markets are associated with higher (lower) values of the value ESG portfolio, whereas the opposite is true for the default spread. The interpretability provided by the XAI-based approach offers valuable insights into developing effective portfolio investment strategies.
This article examines the structure, regulatory framework, financial performance, and yield determinants of Infrastructure Investment Trusts (INVITs) in India, as well as their role as alternative assets in investment portfolios. It also explores the benefits accruing to both infrastructure sponsor companies and retail investors. The analysis of listed INVITs with public infrastructure as underlying assets reveals that they have delivered attractive and stable dividends, moderate capital appreciation, and superior risk-adjusted returns compared to fixed-income securities over the past seven years, including the COVID-19 pandemic period. The low correlation between INVITs and equity markets positions them as an effective instrument for portfolio diversification, facilitating risk reduction and return enhancement. INVITs backed by stable infrastructure asset portfolios have demonstrated stronger performance. Multiple regression analysis indicates that leverage, market capitalization, and return on investment (ROI) have a significant influence on dividend payouts, which represent financial returns. Specifically, dividends show a significant negative association with leverage and a positive relationship with both short-and long-term interest rates. The structural framework of INVITs mirrors the economic characteristics of the underlying infrastructure asset market. Overall, INVITs have emerged as a viable alternative funding mechanism for public infrastructure development in India.
This article examines MicroStrategy's unconventional approach to corporate finance-issuing equity and zero-coupon convertible debt and issuing shares to accumulate Bitcoin. Using historical data and expanded Monte Carlo simulations, the authors estimate the probability of Bitcoin's price declining below critical levels over a typical five-to seven-year debt-maturity window. Results show that the strategy remains solvent except under extreme structural breaks in Bitcoin's long-term growth. Dilution, rather than insolvency, emerges as the primary equity holder risk. Comparative analysis versus crypto-fund benchmarks demonstrates that MicroStrategy's stock exhibits the risk-adjusted return profile of a moderately leveraged digital-asset fund. Broader adoption of this model could reshape corporate treasury management, influence monetary policy, and accelerate the integration of digital assets into conventional finance.
We examine the determinants, such as institutional, innovation, economic, stock market, digital, and entrepreneurial environment, that affect venture capital financing (VCF) in India. We adopt an integrated NCA-fsQCA method, as NCA determines the conditions necessary to have VCF and fsQCA reveals the configurations that are sufficient to generate high or low VCF. We identified six and eight combinations that lead to an increase and a decrease in Indian VCF, respectively. This highlights the interdependent and configurational nature of the factors influencing VCF, rather than any single condition being universally necessary and sufficient. Collectively, NCA-fsQCA methods offer a sound insight into the threshold conditions as well as the nonlinear causation that forms venture capital activity in India. The findings have significant implications for stakeholders, allowing them to make informed changes to drivers that will successfully guide VCF in India.
This article analyzes the role of insurance platform integration in shaping the business models, financial profiles, and market valuations of prominent alternative asset managers (AAMs). Employing panel data spanning 2014-2024 from large publicly listed North American and European AAMs, we document how insurance partnerships are associated with changes in funding architectures, product offerings, revenue compositions, and valuation multiples. Notably, insurance-backed AAMs exhibit accelerated asset-under-management growth, particularly in credit strategies, coinciding with consistent capital inflows from annuity liabilities. These firms also display more diversified income streams with a greater share of spread income and lower earnings volatility, on average, while trading at lower valuation multiples than asset-light peers. Spread-based income appears more stable but involves regulatory and capital complexity trade-offs. Overall, our evidence is consistent with insurance integration representing an important structural shift in the industry-one associated with greater scalability and resilience, but also with heightened organizational complexity and, on average, lower market valuation multiples.
Given gaming's remarkable growth and its crucial role in the digital economy, this article examines volatility spillovers among this target sector, key industries (technology, vices, consumer discretionary, consumer staples, utilities), and traditional financial assets (global equities, Treasury bonds, real estate, gold). The analysis is carried out using the R2 decomposed connectedness approach and spans the period from January 1, 2015, to October 31, 2025. Results show strong interlinkages among markets, with e-commerce & digital payments and global equities being the main drivers of volatility transmission. We further evaluate the efficacy of portfolios integrated with gaming stocks for their hedging and diversification properties under varying market conditions. Our findings indicate that hedging is not an effective strategy during stable market conditions; however, other assets perform as significant diversifiers for gaming-focused portfolios. Nevertheless, during turbulent periods, gaming stocks transform into a potent hedge and safe haven for related industries, though not for conventional assets. This research provides critical insights for portfolio managers and institutional investors, advocating for an updated approach to risk management that accounts for the shifting correlation dynamics.
We study the performance and behavior of retail hedge fund investors by using a novel fund-level classification that is based on client composition data from eVestment. Retail hedge funds significantly outperform retail mutual funds and have a similar performance to institutional hedge funds. In our sample of retail hedge funds, 14.3% generate positive alpha, and performance is partly predictable: low systematic risk funds outperform, and poor performers persist. Retail investors respond aggressively to past returns and lack selection ability; however, they do not systematically make mistakes. These results suggest that retail hedge funds provide sophisticated investors with a valuable alternative to the underperforming retail mutual fund market.
Financial speculation on agricultural commodities has been the subject of many academic studies. However, results remain contradictory because of theoretical uncertainties and practical considerations related to the measure of speculation. In this article, we describe and use various approaches to investigate a large set of seven direct speculation measures on a unified sample of agricultural commodity futures (grains and softs). Using data from 2006 to 2022, we find that speculation, often associated with hedge fund financial institutional behavior, tends to reduce subsequent volatility on many commodities when using speculators' gross exposure proxies-that is, the Working's T, speculative open interest, and speculative intensity-to measure speculation. Other measures are inconclusive. Measurement uncertainty compounds the problem of identifying the impact of speculation on agricultural markets. The negative link between some speculation proxies and subsequent agricultural commodity returns' volatility indicates that regulators should be cautious when attempting to measure speculative behavior and consider rules on speculation and hedge fund limits.
The growth of cryptocurrencies has made them a crucial part of the modern financial sector. Cryptocurrencies are supported by technological advancements, and their interconnectedness has the potential to become a source of systemic risk, raising concerns about spillover effects. This study examines how investor attention influences spillover dynamics among major cryptocurrencies. Using a time-varying parameter vector autoregression model and ordinary least squares regression, we analyze the spillover structure of cryptocurrencies from 2020 to 2024. The results reveal strong interconnectedness, with Bitcoin, Ethereum, and Binance Coin acting as net transmitters of shocks, whereas Dogecoin, Solana, Ripple, and Cardano are net receivers. Investor attention generally reduces spillovers but intensifies them at extreme levels. Of interest, investor attention impacts are heterogeneous effects across assets. This study contributes to behavioral finance by uncovering how investor sentiment affects cryptocurrency interconnectedness and provides practical insights for investors and regulators, particularly regarding portfolio diversification and risk management during periods of heightened public interest.
