Publications
Differentiated dollars
2022Disease-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.
Financing Alzheimer’s Disease Drug Development
2022Alzheimer’s disease (AD) is one of the biggest challenges to modern medicine. However, before February 2021, the last AD drug approval occurred in 2003, implying a 100% failure rate of AD therapeutic programs over the 17 years to that point; the lowest probability of success among all diseases. One of the key challenges is funding, which we explore in more depth in this chapter by first reviewing the current funding landscape for AD, and then considering the strengths and weaknesses of various commercialization strategies. Despite the discouraging track record of the biopharma industry in addressing AD, there is reason to be hopeful due to substantial scientific progress in developing a deeper understanding of the biology of the disease as well as increased federal funding for AD research. However, we also we need the private sector to translate these scientific breakthroughs into new medicines, which takes additional funding and new business models so as to reduce risk and improve returns for investors. If we can change the narrative of AD therapeutics to give investors new hope, the private sector can serve as a powerful partner to the biomedical community.
Measuring the Economic and Academic Impact of Philanthropic Funding: The Breast Cancer Research Foundation
2022Using survey data gathered from grantees of the nonprofit Breast Cancer Research Foundation (BCRF), we investigated the commercial and non-commercial impacts of their research funding. We found significant impact in both domains. Commercially, 19.5% of BCRF grantees filed patents, 35.9% had a project that has reached clinical development, and 12 companies have or will be spun off from existing projects, thus creating 127 new jobs. Non-commercially, 441 graduate students have been trained by 116 grantees, 767 postdoctoral fellows have been trained by 137 grantees, 66%of grantees have used funding for faculty salaries, 93% have achieved collaboration with other researchers, and 42.7% have enacted process improvements in research methodology. Econometric analysis identifies BCRF funding and associated process improvements as key factors associated with the likelihood to file patents. However, we also found that the involvement of more than one institution in a collaborative project had a negative impact on subsequent development. This may point to frictions introduced by multi-university interactions.
Explainable Machine Learning Models of Consumer Credit Risk (Working Paper)
2022In this paper, we create machine learning (ML) models to forecast home equity credit risk for individuals using a real-world dataset and demonstrate methods to explain the output of these ML models to make them more accessible to the end-user. We analyze the explainability of these models for various stakeholders: loan companies, regulators, loan applicants, and data scientists, incorporating their different requirements with respect to explanations. For loan companies, we generate explanations for every model prediction of creditworthiness. For regulators, we perform a stress test for extreme scenarios. For loan applicants, we generate diverse counterfactuals to guide them with steps to reverse the model's classification. Finally, for data scientists, we generate simple rules that accurately explain 70-72% of the dataset. Our work is intended to accelerate the adoption of ML techniques in domains that would benefit from explanations of their predictions.
Financing Vaccines for Global Health Security
2022Recent outbreaks of infectious pathogens such as Zika, Ebola, and COVID-19 have under-scored the need for the dependable availability of vaccines against emerging infectious diseases (EIDs). Prior to the COVID-19 pandemic, the cost and risk of R&D programs and uniquely unpredictable demand for EID vaccines discouraged many potential vaccine developers, and government and nonprofit agencies have struggled to provide timely or sufficient incentives for their development and sustained supply. However, the economic climate has changed significantly post-pandemic. To explore this contrast, we analyze the pre-pandemic economic returns of a portfolio of EID vaccine assets, and find that, under realistic financing assumptions, the expected returns are significantly negative, implying that the private sector is unlikely to address this need without public-sector intervention. However, in a post-pandemic policy landscape, the financing deficit for this portfolio can be closed, and we analyze several potential solutions, including enhanced public–private partnerships and subscription models in which governments would pay annual fees to obtain access to a portfolio of stockpiled vaccines in the event of an outbreak.
Quantifying the Impact of Impact Investing (Working Paper)
2021We propose a quantitative framework for assessing the financial impact of any form of impact investing, including socially responsible investing (SRI), environmental, social, and governance (ESG) objectives, and other non-financial 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 propose a quantitative measure for the financial reward, or cost, of impact investing compared to passive index benchmarks. We illustrate our approach with applications to biotech venture philanthropy, divesting from “sin” stocks, investing in ESG, and “meme” stock rallies such as GameStop in 2021.
The Risk, Reward, and Asset Allocation of Nonprofit Endowment Funds (Working Paper)
2021We 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.
Spectral factor models
2021We represent risk factors as sums of orthogonal components capturing fluctuations with cycles of different length. The representation leads to novel spectral factor models in which systematic risk is allowed—without being forced—to vary across frequencies. Frequency- specific systematic risk is captured by a notion of spectral beta. We show that traditional factor models restrict the spectral betas to be constant across frequencies. The restriction can hide horizon-specific pricing effects that spectral factor models are designed to re- veal. We illustrate how the methods may lead to economically meaningful dimensionality reduction in the factor space.
To maximize or randomize? An experimental study of probability matching in financial decision making
2021Probability matching, also known as the “matching law” or Herrnstein’s Law, has long puzzled economists and psychologists because of its apparent inconsistency with basic self-interest. We conduct an experiment with real monetary payoffs in which each participant plays a computer game to guess the outcome of a binary lottery. In addition to finding strong evidence for probability matching, we document different tendencies towards randomization in different payoff environments—as predicted by models of the evolutionary origin of probability matching—after controlling for a wide range of demographic and socioeconomic variables. We also find several individual differences in the tendency to maximize or randomize, correlated with wealth and other socioeconomic factors. In particular, subjects who have taken probability and statistics classes and those who self-reported finding a pattern in the game are found to have randomized more, contrary to the common wisdom that those with better understanding of probabilistic reasoning are more likely to be rational economic maximizers. Our results provide experimental evidence that individuals—even those with experience in probability and investing—engage in randomized behavior and probability matching, underscoring the role of the environment as a driver of behavioral anomalies.
The evolutionary origin of Bayesian heuristics and finite memory
2021Bayes' rule is a fundamental principle that has been applied across multiple disciplines. However, few studies have addressed its origin as a cognitive strategy or the underlying basis for generalization from a small sample. Using a simple binary choice model subject to natural selection, we derive Bayesian inference as an adaptive behavior under certain stochastic environments. Such behavior emerges purely through the forces of evolution, despite the fact that our population consists of mindless individuals without any ability to reason, act strategically, or accurately encode or infer environmental states probabilistically. In addition, three specific environments favor the emergence of finite memory—those that are Markov, nonstationary, and environments where sampling contains too little or too much information about local conditions. These results provide an explanation for several known phenomena in human cognition, including deviations from the optimal Bayesian strategy and finite memory beyond resource constraints.
Can Financial Economics Cure Cancer?
2021Funding for early-stage biomedical innovation has become more difficult to secure at the same time that medical breakthroughs seem to be occurring at ever increasing rates. One explanation for this counterintuitive trend is that increasing scientific knowledge can actually lead to greater economic risk for investors in the life sciences. While the Human Genome Project, high-throughput screening, genetic biomarkers, immunotherapies, and gene therapies have made a tremendously positive impact on biomedical research and, consequently, patient lives, they have also increased the cost and complexity of the drug development process, causing many investors to shift their assets to more attractive investment opportunities. This suggests that new business models and financing strategies can be used to reduce the risk and increase the attractiveness of biomedical innovation so as to bring new and better therapies to patients faster.
Introduction to PNAS special issue on evolutionary models of financial markets
2021One of the longest debates in economics involves the existence of a rare Hominid “species” known as Homo economicus, the economic human. H. economicus is able to determine the optimal use of its resources to maximize its well-being as defined by the assumptions of neoclassical economics, leading to behavior that has come to be known as economic rationality. When interacting with other members of this species in market settings, such behavior leads to a magical outcome. The participants’ self-interested efforts to exploit their disparate pieces of information aggregates, distills, and compresses their information into a single number: the price. And because no piece of information is left unused or uninterpreted in the process of price discovery, this market is deemed “efficient.” Prices fully reflect all available information, as Eugene Fama concluded in his first articulation of the efficient markets hypothesis (1).
The origin of cooperation
2021We construct an evolutionary model of a population consisting of two types of interacting individuals that reproduce under random environmental conditions. We show that not only does the evolutionarily dominant behavior maximize the number of offspring of each type, it also minimizes the correlation between the number of offspring of each type, driving it toward −1. We provide several examples that illustrate how correlation can be used to explain the evolution of cooperation.
Patterns of Multimorbidity
2021With multimorbidity becoming the norm rather than the exception, the management of multiple chronic diseases is a major challenge facing healthcare systems worldwide. Using a large, nationally representative database of electronic medical records from the United Kingdom spanning the years 2005 to 2016 and consisting over 4.5 million patients, we apply statistical methods and network analysis to identify comorbid pairs and triads of diseases and identify clusters of chronic conditions across different demographic groups. Unlike many previous studies, which generally adopt cross-sectional designs, we examine temporal changes in the patterns of multimorbidity. In addition, we perform survival analysis to examine the impact of multimorbidity on mortality.
Accelerating glioblastoma therapeutics via venture philanthropy
2021Development of curative treatments for glioblastoma (GBM) has been stagnant in recent decades largely because of significant financial risks. A portfolio-based strategy for the parallel discovery of breakthrough therapies can effectively reduce the financial risks of potentially transformative clinical trials for GBM. Using estimates from domain experts at the National Brain Tumor Society (NBTS), we analyze the performance of a portfolio of 20 assets being developed for GBM, diversified across different development phases and therapeutic mechanisms. We find that the portfolio generates a 14.9% expected annualized rate of return. By incorporating the adaptive trial platform GBM AGILE in our simulations, we show that at least one drug candidate in the portfolio will receive US Food and Drug Administration (FDA) approval with a probability of 79.0% in the next decade.