Fusion energy faces many hurdles. The history of the biotech industry offers lessons for how to build public trust and create a robust investment ecosystem to help fusion achieve its potential.

Within the contemporary context of environmental, social, and governance (ESG) investing principles, the authors explore the risk–reward characteristics of portfolios in the United States, Europe, and Japan constructed using the foundational tenets of Markowitz’s modern portfolio theory with data from six major ESG rating agencies. They document statistically significant excess returns in ESG portfolios from 2014 to 2020 in the United States and Japan. They propose several statistical and voting-based methods to aggregate individual ESG ratings, the latter based on the theory of social choice. They find that aggregating individual ESG ratings improves portfolio performance. In addition, the authors find that a portfolio based on Treynor–Black weights further improves the performance of ESG portfolios. Overall, these results suggest that significant signals in ESG rating scores can enhance portfolio construction despite their noisy nature.

We develop a mathematical framework for constructing optimal impact portfolios and quantifying their financial performance by characterizing the returns of impact-ranked assets using induced order statistics and copulas. The distribution of induced order statistics can be represented by a mixture of order statistics and uniformly distributed random variables, where the mixture function is determined by the dependence structure between residual returns and impact factors—characterized by copulas—and the marginal distribution of residual returns. This representation theorem allows us to explicitly and efficiently compute optimal portfolio weights under any copula. This framework provides a systematic approach for constructing and quantifying the performance of optimal impact portfolios with arbitrary dependence structures and return distributions.

The case for investing in fusion energy has never been greater, given increasing global energy demand, high annual carbon dioxide output, and technological limitations for wind and solar power. Nevertheless, financing for fusion companies through traditional means has proven challenging. While fusion startups have an unparalleled upside, their high upfront costs, lengthy delay in payoff, and high risk of commercial failure have historically restricted funding interest to a niche set of investors. Drawing on insights from investor interviews and case studies of public–private partnerships, we propose a megafund structure in which a large number of projects are securitized into a single holding company funded through various debt and equity tranches, with first loss capital guarantees from governments and philanthropic partners. The megafund exploits many of the core properties of the fusion industry: the diversity of approaches to engender fusion reactions, the ability to create revenue-generating divestitures in related fields, and the breadth of auxiliary technologies needed to support a functioning power plant. The model expands the pool of available capital by creating tranches with different risk–return tradeoffs and providing a diversified “fusion index” that can be viewed as a long hedge against fossil fuels. Simulations of a fusion megafund demonstrate positive returns on equity (ROE) and low default rates for the capital raised using debt.

We propose a quantitative framework for assessing the financial impact of any form of impact investing, including socially responsible investing; environmental, social, and governance (ESG) objectives; and other nonfinancial investment criteria. We derive conditions under which impact investing detracts from, improves on, or is neutral to the performance of traditional mean-variance optimal portfolios, which depends on whether the correlations between the impact factor and unobserved excess returns are negative, positive, or zero, respectively. Using Treynor–Black portfolios to maximize the risk- adjusted returns of impact portfolios, we derive an explicit and easily computable measure of the financial reward or cost of impact investing as compared with passive index bench-marks. We illustrate our approach with applications to biotech venture philanthropy, a semiconductor research and development consortium, divesting from “sin” stocks, ESG investments, and “meme” stock rallies such as GameStop in 2021.

We study the performance of green portfolios in both the US and Chinese markets, constructed using a broad range of climate-related environmental metrics, including carbon emissions, water consumption, waste disposal, land and water pollutants, air pollutants, and natural resource use. We compare several popular long-only and long–short green portfolio construction methodologies and find that a method based on Treynor–Black weights offers the most robust performance, thanks to its ability to quantify alphas for individual assets using only a small number of parameters. In the United States, green portfolios (e.g., low-carbon portfolios) have realized positive alphas in excess of Fama–French factors, a significant portion of which can be explained by an unexpected increase in climate concerns over the past decade, rather than positive expected returns. In contrast, Chinese investors have borne a cost for holding green assets instead of brown assets over the past seven years, implying a positive carbon premium, the opposite of US markets.

The long-run impact and implications of an endowment’s spending policy and asset allocation decisions are examined. Using a dynamic model, the authors explore how different endowment spending rules influence the dynamics of an endowment’s size and future spending. They find that different parameters within each spending rule have significant long-term impact on wealth accumulation and spending capacity. Using Merton's (1993) endowment model and compiled asset allocation data, they estimate the intertemporal preferences and risk aversion of several major endowments and find significant variation across endowments in their propensity to increase portfolio risk in response to increased spending needs.

Disease-focused foundations have used venture philanthropy (VP) for decades to develop interventions that have patient impact and generate revenue to support their mission. We articulate the distinguishing motives and features of VP funds and their distinct role in the life sciences innovation ecosystem. In particular, we focus on how entrepreneurs and VP funds can work together to help patients and generate economic value. We recommend that entrepreneurs seeking VP support understand a fund’s mission and objectives, and position themselves to fit the fund’s strategic and financial portfolio needs. Finally, we provide case studies of three specific initiatives — the JDRF T1D Fund, targeting type 1 (juvenile) diabetes; MPM Capital’s Oncology Impact Fund; and the American Heart Association’s Cardeation Capital — to showcase these efforts and benefits in practice.

We collect tax return data from all 311,222 public NPOs in the United States over the 2009-2017 period to study the asset allocation choices and investment returns of their endowment funds. One in nine public NPOs have endowment funds. The majority of funds allocate their assets conservatively to low-risk assets, and as a result, earn an average annual return of 5.3%. There is substantial heterogeneity in investment returns across funds. Large funds significantly outperform small funds across all return measures and nonprofit sectors. Endowments in NPO sectors devoted to public and societal benefit, the environment, and the arts are among the top performers. High returns among higher education endowments are explained by size, while hospital endowments significantly underperform. Higher investment returns are associated with better governance, more highly paid management, lower discretionary spending, and lower investment management fees. Lastly, when faced with volatile contributions, endowment funds hold more cash and invest more conservatively.