In this work, the authors 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. They analyze the explainability for various stakeholders: loan companies, regulators, loan applicants, and data scientists, incorporating their different requirements with respect to explanations. For loan companies, they generate explanations for every model prediction of creditworthiness. For regulators, they perform a stress test for extreme scenarios. For loan applicants, they generate diverse counterfactuals to guide them with steps toward a favorable classification from the model. Finally, for data scientists, they generate simple rules that accurately explain 70%–72% of the dataset. Their study provides a synthesized ML explanation framework for all stakeholders and is intended to accelerate the adoption of ML techniques in domains that would benefit from explanations of their predictions.

We develop a theory of optimal financing for R&D-intensive firms. With only market financing, the firm relies exclusively on equity financing and carries excess cash, but underinvests in R&D. We use mechanism design to examine how intermediated financing can attentuate this underinvestment. The mechanism combines equity with put options such that investors insure firms against R&D failure and firms insure investors against high R&D payoffs not being realized.

We provide a review of macro-finance models featuring nonlinear dynamics. We survey the models developed recently in the literature, including models with amplification effects of financial constraints, models with households' leverage constraints, and models with financial networks. We also construct an illustrative model for those readers who are unfamiliar with the literature. Within this framework, we highlight several important limitations of local solution methods compared with global solution methods, including the fact that local-linearization approximations omit important nonlinear dynamics, yielding biased impulse-response analysis.

A tribute to John (Jack) C. Bogle, founder of The Vanguard Group, who passed away on January 16, 2019.

We propose a Markov chain Monte Carlo (MCMC) algorithm for estimating the parameters of algorithmic models of investor behavior. We show that this method can successfully infer the relative importance of each heuristic among a large cross-section of investors, even when the number of observations per investor is quite small. We also compare the accuracy of the MCMC approach to regression analysis in predicting the relative importance of heuristics at the individual and aggregate levels and conclude that MCMC predicts aggregate weights more accurately while regression outperforms in predicting individual weights.

Most people will experience a severe pandemic within their lifetime, and the world remains dangerously unprepared. In fact, scientists predict a nearly 50% chance—the same probability as flipping heads or tails on a coin—that we will endure another COVID-19-level pandemic within the next 25 years. Shifting America’s pandemic response capability from reactive to proactive is, therefore, urgent. Failure to do so risks the country’s welfare.

Getting ahead of the next pandemic is impossible without government financing. Vaccine production is costly, and these expenses will hinder industries from preemptively developing the tools needed to halt disease transmission. For example, the total expected revenues over a 20-year vaccine patent lifecycle would cover just half of the upfront research and development (R&D) costs.

However, research suggests that a portfolio-based approach to vaccine development—especially now with new, broadly applicable mRNA technology—dramatically increases the returns on investment while also guarding against an estimated 31 of the next 45 epidemic outbreaks. With lessons learned from Operation Warp Speed, Congress can deploy this approach by (i) authorizing and appropriating $10 billion to the Biomedical Advanced Research and Development Authority (BARDA) (ii) developing a vaccine portfolio for 10 emerging infectious diseases (EIDs), and (iii) a White House Office of Science and Technology Policy (OSTP)-led interagency effort focused on scaling up production of priority vaccines.

INTRODUCTION: Machine learning models are increasingly applied to predict the drug development outcomes based on intermediary clinical trial results. A key challenge to this task is to address various forms of bias in the historical drug approval data.

OBJECTIVE:We aimed to identify and mitigate the bias in drug approval predictions and quantify the impacts of debiasing in terms of financial value and drug safety.

METHODS: We instantiated the Debiasing Variational Autoencoder, the state-of-the-art model for automated debiasing. We trained and evaluated the model on the Citeline dataset provided by Informa Pharma Intelligence to predict the final drug development outcome from phase II trial results.

RESULTS: The debiased Debiasing Variational Autoencoder model achieved better performance (measured by the F₁ score 0.48) in predicting the drug development outcomes than its un-debiased baseline (measured by the F₁ score 0.25). It had a much higher true-positive rate than baseline (60% vs 15%), while its true-negative rate was slightly lower (88% vs 99%). The Debiasing Variational Autoencoder distinguished between drugs developed by large pharmaceutical firms and those by small biotech companies. The model prediction is strongly influenced by multiple factors such as prior approval of the drug for another indication, whether the trial meets the positive/negative endpoints, and the year when the trial is completed. We estimate that the debiased model generates financial value for the drug developer in six major therapeutic areas, with a range of US$763–1,365 million.

CONCLUSIONS: Our analysis shows that debiasing improves the financial efficiency of late-stage drug development. From the pharmacovigilance perspective, the debiased model is more likely to identify drugs that are both safe and effective. Meanwhile, it may predict a higher probability of success for drugs with potential adverse effects (because of its lower true-negative rate), thus it must be used with caution to predict the development outcomes of drug candidates currently in the pipeline.

BACKGROUND: The authors estimate the probability of successful development and duration of clinical trials for medications to treat neuropathic and nociceptive pain. The authors also consider the effect of the perceived abuse potential of the medication on these variables.

METHODS: This study uses the Citeline database to compute the probabilities of success, duration, and survivorship of pain medication development programs between January 1, 2000, and June 30, 2020, conditioned on the phase, type of pain (nociceptive vs. neuropathic), and the abuse potential of the medication.

RESULTS: The overall probability of successful development of all pain medications from phase 1 to approval is 10.4% (standard error, 1.5%).Medications to treat nociceptive and neuropathic pain have a probability of successful development of 13.3% (standard error, 2.3%) and 7.1% (standard error, 1.9%), respectively. The probability of successful development of medications with high abuse potential and low abuse potential are 27.8% (standard error, 4.6%) and 4.7% (standard error, 1.2%), respectively. The most common period for attrition is between phase 3 and approval.

CONCLUSIONS: The authors’ data suggest that the unique attributes of pain medications, such as their abuse potential and intended pathology, can influence the probability of successful development and duration of development.

In this article, I describe my approach to dealing with the challenges and opportunities of synchronous online teaching during the Fall semester of 2020 in the specific context of a 90-student graduate course in Healthcare Finance at the MIT Sloan School of Management.

Hamilton’s rule [W. D. Hamilton, Am. Nat. 97, 354–356 (1963); W. D. Hamilton, J. Theor. Biol. 7, 17–52 (1964)] quantifies the central evolutionary ideas of inclusive fitness and kin selection into a simple algebraic relationship. Evidence consistent with Hamilton’s rule is found in many animal species. A drawback of investigating Hamilton’s rule in these species is that one can estimate whether a given behavior is consistent with the rule, but a direct examination of the exact cutoff for altruistic behavior predicted by Hamilton is almost impossible. However, to the degree that economic resources confer survival benefits in modern society, Hamilton’s rule may be applicable to economic decision-making, in which case techniques from experimental economics offer a way to determine this cutoff. We employ these techniques to examine whether Hamilton’s rule holds in human decision-making, by measuring the dependence between an experimental subject’s maximal willingness to pay for a gift of $50 to be given to someone else and the genetic relatedness of the subject to the gift’s recipient. We find good agreement with the predictions of Hamilton’s rule. Moreover, regression analysis of the willingness to pay versus genetic relatedness, the number of years living in the same residence, age, and sex shows that almost all the variation is explained by genetic relatedness. Similar but weaker results are obtained from hypothetical questions regarding the maximal risk to her own life that the subject is willing to take in order to save the recipient’s life.

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.

Using a novel dataset of 653,455 individual brokerage accounts belonging to 298,556 households, we document the frequency, timing, and duration of panic sales, which we define as a decline of 90% of a household account’s equity assets over the course of one month, of which 50% or more is due to trades. We find that a disproportionate number of households make panic sales when there are sharp market downturns, a phenomenon we call ‘freaking out.’ We show that panic selling and freak-outs are predictable and fundamentally different from other well-known behavioral patterns such as overtrading or the disposition effect.

In this article, the authors develop a data-driven peer grouping system using artificial intelligence (AI) tools to capture market perception and, in turn, group companies into clusters at various levels of granularity. In addition, they develop a continuous measure of similarity between companies; they use this measure to group companies into clusters and construct hedged portfolios. In the peer groupings, companies grouped in the same clusters had strong homogeneous risk and return profiles, whereas different clusters of companies had diverse, varying risk exposures. The authors extensively evaluated the clusters and found that companies grouped by their method had higher out-of-sample return correlation but lower stability and interpretability than companies grouped by a standard industry classification system. The authors also develop an interactive visualization system for identifying AI-based clusters and similar companies.

Despite its success in financial markets and other domains, collective intelligence seems to fall short in many critical contexts, including infrequent but repeated financial crises, political polarization and deadlock, and various forms of bias and discrimination. We propose an evolutionary framework that provides fundamental insights into the role of heterogeneity and feedback loops in contributing to failures of collective intelligence. The framework is based on a binary choice model of behavior that affects fitness; hence, behavior is shaped by evolutionary dynamics and stochastic changes in environmental conditions. We derive collective intelligence as an emergent property of evolution in this framework, and also specify conditions under which it fails. We find that political polarization emerges in stochastic environments with reproductive risks that are correlated across individuals. Bias and discrimination emerge when individuals incorrectly attribute random adverse events to observable features that may have nothing to do with those events. In addition, path dependence and negative feedback in evolution may lead to even stronger biases and levels of discrimination, which are locally evolutionarily stable strategies. These results suggest potential policy interventions to prevent such failures by nudging the “madness of mobs” towards the “wisdom of crowds” through targeted shifts in the environment