The portfolio optimization problem has attracted researchers from many disciplines to resolve the issue of poor out-of-sample performance due to estimation errors in the expected returns. A practical method for portfolio construction is to use assets’ ordering information, expressed in the form of preferences over the stocks, instead of the exact expected returns. Due to the fact that the ranking itself is often described with uncertainty, we introduce a generic robust ranking model and apply it to portfolio optimization. In this problem, there are n objects whose ranking is in a discrete uncertainty set. We want to find a weight vector that maximizes some generic objective function for the worst realization of the ranking. This robust ranking problem is a mixed integer minimax problem and is very difficult to solve in general. To solve this robust ranking problem, we apply the constraint generation method, where constraints are efficiently generated by solving a network flow problem. For empirical tests, we use post-earnings-announcement drifts to obtain ranking uncertainty sets for the stocks in the DJIA index. We demonstrate that our robust portfolios produce smaller risk compared to their non-robust counterparts.

Billions of dollars are spent annually on cancer drug development, yet effective treatments for many types of cancer remain as elusive as ever. Recently, the MIT Laboratory for Financial Engineering announced the launch of Project ALPHA (Analytics for Life-sciences Professionals and Healthcare Advocates), a large-scale estimation of clinical trial probabilities of success (PoS) for a variety of drug development programs, where a single program is defined as the set of all clinical trials corresponding to a unique drug-indication pair. In that study, we found that only 3.4 percent of all cancer drug development programs from 2000 to 2015 moved from phase 1 to regulatory approval, despite the fact that oncology accounted for 42 percent of all drug development programs in that dataset.

Recent 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.

In the midst of epidemics such as COVID-19, therapeutic candidates are unlikely to be able to complete the usual multi-year clinical trial and regulatory approval process within the course of an outbreak. We apply a Bayesian adaptive patient-centered model—which minimizes the expected harm of false positives and false negatives—to optimize the clinical trial development path during such outbreaks. When the epidemic is more infectious and fatal, the Bayesian-optimal sample size in the clinical trial is lower and the optimal statistical significance level is higher. For COVID-19 (assuming a static Ro = 2 and initial infection percentage of 0.1%), the optimal significance level is 7.1% for a clinical trial of a non-vaccine anti-infective therapeutic clinical trial and 13.6% for that of a vaccine. For a dynamic Ro ranging from 2 to 4, the corresponding values are 14.4% and 26.4%, respectively. Our results illustrate the importance of adapting the clinical trial design and the regulatory approval process to the specific parameters and stage of the epidemic.

A key driver in biopharmaceutical investment decisions is the probability of success of a drug development program. We estimate the probabilities of success (PoS) of clinical trials for vaccines and other anti-infective therapeutics using 43,414 unique triplets of clinical trial, drug, and disease between January 1, 2000, and January 7, 2020, yielding 2,544 vaccine programs and 6,829 non-vaccine programs targeting infectious diseases. The overall estimated PoS for an industry-sponsored vaccine program is 39.6%, and 16.3% for an industry-sponsored anti-infective therapeutic. Among industry-sponsored vaccines programs, only 12 out of 27 disease categories have seen at least one approval, with the most successful being against monkeypox (100%), rotavirus (78.7%), and Japanese encephalitis (67.6%). The three infectious diseases with the highest PoS for industry-sponsored nonvaccine therapeutics are smallpox (100%), CMV (31.8%), and onychomycosis (29.8%). Nonindustry- sponsored vaccine and non-vaccine development programs have lower overall PoSs: 6.8% and 8.2%, respectively. Viruses involved in recent outbreaks—MERS, SARS, Ebola, Zika—have had a combined total of only 45 non-vaccine development programs initiated over the past two decades, and no approved therapy to date (Note: our data was obtained just before the COVID-19 outbreak and do not contain information about the programs targeting this disease.) These estimates offer guidance both to biopharma investors as well as to policymakers seeking to identify areas most likely to be undeserved by private-sector engagement and in need of public-sector support.

The healthcare industry in the United States (U.S.) is a complex ecosystem with many different stakeholders. Unlike the universal single-payer healthcare systems of many European countries,the accessibility of prescription drugs in the U.S. is largely determined by contract negotiations between health plans and drug manufacturers about formulary placement. These negotiations can sometimes result in higher out-of-pocket costs for the patient, since the current structure of the U.S. healthcare system creates a perverse incentive for many health plans to elicit higher rebates from drug manufacturers in exchange for formulary placement of brand-name drugs, thereby increasing patients’ out-of-pocket costs.

Thanks to a combination of scientific advances and economic incentives, the development of therapeutics to treat rare or orphan diseases has grown dramatically in recent years. With the advent of Food and Drug Administration–approved gene therapies and the promise of gene editing, many experts believe we are at an inflection point in dealing with these afflictions. In this article, the authors propose to document this inflection point by measuring the risk and reward of investing in the orphan drug industry. They construct a stock market index of 39 publicly traded companies that specialize in developing drugs for orphan diseases and compare the financial performance of this index, which they call ORF, to the broader biopharmaceutical industry and the overall stock market from 2000 to 2015. Although the authors report that ORF underperformed other biopharma companies and the overall stock market in the early 2000s, its performance has improved over time: from 2010 to 2015, ORF returned 608%, far exceeding the 317%, 320%, and 305% returns of the S&P, NASDAQ, and NYSE ARCA Biotech indexes, respectively, and the 83% of the S&P 500. ORF does have higher volatility than the other indexes but still outperforms even on a risk-adjusted basis, with a Sharpe ratio of 1.24 versus Sharpe ratios of 1.17, 1.14, and 1.05, respectively, for the other three biotech indexes and 0.71 for the S&P 500. However, ORF has a market beta of 1.16, which suggests significant correlation to the aggregate stock market and less diversification benefits than traditional pharmaceutical investments.

We consider a portfolio-based approach to financing ovarian cancer therapeutics in which multiple candidates are funded within a single structure. Twenty-five potential early-stage drug development projects were identified for inclusion in a hypothetical portfolio through interviews with gynecological oncologists and leading experts, a review of ovarian cancer-related trials registered in the ClinicalTrials.gov database, and an extensive literature review. The annualized returns of this portfolio were simulated under a purely private sector structure both with and without partial funding from philanthropic grants, and a public–private partnership that included government guarantees. We find that public–private structures of this type can increase expected returns and reduce tail risk, allowing greater amounts of private sector capital to fund early-stage research and development.

It is a rare privilege for students and friends to celebrate a scholar’s  accomplishments even as he remains as active as ever. This volume marks just such an occasion, a permanent tribute to Jerry A. Hausman’s positive life-changing impact on so many over the years. The wide range of contributions of ‘‘Jerry’s kids’’ is a direct consequence of the extraordinary breadth and depth of Jerry’s interests, expertise, and generosity. No part of econometrics and economics more broadly was uninteresting to him, and every subfield Jerry touched was materially better for it. It is the hope and aspiration of every author in this volume that our scholarship measures up to the high standards that Jerry set with his remarkable example.

We apply machine-learning techniques to predict drug approvals using drug-development and clinical-trial data from 2003 to 2015 involving several thousand drug-indication pairs with over 140 features across 15 disease groups. To deal with missing data, we use imputation methods that allow us to fully exploit the entire dataset, the largest of its kind. We show that our approach outperforms complete-case analysis, which typically yields biased inferences. We achieve predictive measures of 0.78, and 0.81 AUC (“area under the receiver operating characteristic curve,” the estimated probability that a classifier will rank a positive outcome higher than a negative outcome) for predicting transitions from phase 2 to approval and phase 3 to approval, respectively. Using five-year rolling windows, we document an increasing trend in the predictive power of these models, a consequence of improving data quality and quantity. The most important features for predicting success are trial outcomes, trial status, trial accrual rates, duration, prior approval for another indication, and sponsor track records. We provide estimates of the probability of success for all drugs in the current pipeline.