A Cost/Benefit Analysis of Clinical Trial Designs for COVID-19 Vaccine Candidates2020
We compare and contrast the expected duration and number of infections and deaths averted among several designs for clinical trials of COVID-19 vaccine candidates, including traditional and adaptive randomized clinical trials and human challenge trials. Using epidemiological models calibrated to the current pandemic, we simulate the time course of each clinical trial design for 756 unique combinations of parameters, allowing us to determine which trial design is most effective for a given scenario. A human challenge trial provides maximal net benefits—averting an additional 1.1M infections and 8,000 deaths in the U.S. compared to the next best clinical trial design—if its set-up time is short or the pandemic spreads slowly. In most of the other cases, an adaptive trial provides greater net benefits.
Robert C. Merton: The First Financial Engineer2020
This is an edited version of a talk given at the Robert C. Merton 75th Birthday Celebration Conference held at MIT on August 5 and 6, 2019. A video of the talk is available at https://bit.ly/2nvITM6.
This article is one of a pair of articles published in this volume about Robert C. Merton's contributions to the science of financial economics. The other article in this pair is “Robert C. Merton and the Science of Finance” by Zvi Bodie.
Financially Adaptive Clinical Trials via Option Pricing Analysis2020
The regulatory approval process for new therapies involves costly clinical trials that can span multiple years. When valuing a candidate therapy from a financial perspective, industry sponsors may terminate a program early if clinical evidence suggests market prospects are not as favorable as originally forecasted. Intuition suggests that clinical trials that can be modified as new data are observed, i.e., adaptive trials, are more valuable than trials without this flexibility. To quantify this value, we propose modeling the accrual of information in a clinical trial as a sequence of real options, allowing us to systematically design early-stopping decision boundaries that maximize the economic value to the sponsor. In an empirical analysis of selected disease areas, we find that when a therapy is ineffective, our adaptive financing method can decrease the expected cost incurred by the sponsor in terms of total expenditures, number of patients, and trial length by up to 46%. Moreover, by amortizing the large fixed costs associated with a clinical trial over time, financing these projects becomes less risky, resulting in lower costs of capital and larger valuations when the therapy is effective.
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Estimating the Financial Impact of Gene Therapy2020
We assess the potential financial impact of future gene therapies by identifying the 109 late-stage gene therapy clinical trials currently underway, estimating the prevalence and incidence of their corresponding diseases, developing novel mathematical models of the increase in quality-adjusted life years for each approved gene therapy, and simulating the launch prices and the expected spending of these therapies over a 15-year time horizon. The results of our simulation suggest that an expected total of 1.09 million patients will be treated by gene therapy from January 2020 to December 2034. The expected peak annual spending on these therapies is $25.3 billion, and the total spending from January 2020 to December 2034 is $306 billion. We decompose their annual estimated spending by treated age group as a proxy for U.S. insurance type, and consider the tradeoffs of various methods of payment for these therapies to ensure patient access to their expected benefits.
A Brain Capital Grand Strategy: Toward Economic Reimagination2020
Current brain research, innovation, regulatory, and funding systems are artificially siloed, creating boundaries in our understanding of the brain based on constructs such as aging, mental health, and/or neurology, when these systems are all inextricably integral.
Grand strategy provides a broad framework that helps to guide all elements of a major, long-term project. There are converging global trends resulting from the COVID pandemic compelling a Brain Capital Grand Strategy: widespread appreciation of the rise in brain health issues (e.g., increase prevalence of mental illness and high rates of persons with age-related cognitive impairment contracting COVID), increased automation, job loss and underemployment, radical restructuring of health systems, rapid consumer adoption and acceptance of digital and remote solutions, and recognition of the need for economic reimagination. If we respond constructively to this crisis, the COVID pandemic could catalyze institutional change and a better social contract.
SCRAM: A Platform for Securely Measuring Cyber Risk2020
We develop a new cryptographic platform called SCRAM (Secure Cyber Risk Aggregation and Measurement) that allows multiple entities to compute aggregate cyber-risk measures without requiring any entity to disclose its own sensitive data on cyberattacks, penetrations, and losses. Using the SCRAM platform, we present results from two computations in a pilot study with six large private-sector companies: (1) benchmarks of the adoption rates of 171 critical security measures and (2) links between monetary losses from 49 security incidents and the specific sub-control failures implicated in each incident. These results provide insight into problematic cyber-risk-control areas that need additional scrutiny and/or investment, but in a completely anonymized and privacy-preserving way.
Measuring Risk Preferences and Asset-Allocation Decisions: A Global Survey Analysis2020
We use a global survey of over 22,400 individual investors, 4,892 financial advisors, and 2,060 institutional investors between 2015 and 2017 to elicit their asset allocation behavior and risk preferences. We find substantially different behaviors among these three groups of market participants. Most institutional investors exhibit highly contrarian reactions to past returns in their equity allocations. Financial advisors are also mostly contrarian; a few of them demonstrate passive behavior. However, individual investors tend to extrapolate past performance. We use a clustering algorithm to partition individuals into five distinct types: passive investors, risk avoiders, extrapolators, contrarians, and optimistic investors. Across demographic categories, older investors tend to be more passive and risk averse.
Macroeconomic Models for Monetary Policy: A Critical Review from a Finance Perspective2020
We provide a critical review of macroeconomic models used for monetary policy at central banks from a finance perspective. We review the history of monetary policy modeling, survey the core monetary models used by major central banks, and construct an illustrative model for those readers who are unfamiliar with the literature. Within this framework, we highlight several important limitations of current models and methods, including the fact that local-linearization approximations omit important nonlinear dynamics, yielding biased impulse-response analysis and parameter estimates. We also propose new features for the next generation of macrofinancial policy models, including: a substantial role for a financial sector, the government balance sheet and unconventional monetary policies; heterogeneity, reallocation, and redistribution effects; the macroeconomic impact of large nonlinear risk-premium dynamics; time-varying uncertainty; financial sector and systemic risks; imperfect product market and markups; and further advances in solution, estimation, and evaluation methods for dynamic quantitative structural models.
Macro-Finance Models with Nonlinear Dynamics2020
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.
Financing Correlated Drug Development Projects2020
Current business models have struggled to support early-stage drug development. In this paper, we study an alternative financing model, the megafund structure, to fund drug discovery. We extend the framework proposed in previous studies to account for correlation between phase transitions in drug development projects, thus making the model a more realistic representation of biopharma research and development. In addition, we update the parameters used in our simulation with more recent estimates of the probability of success (PoS). We find that the performance of the megafund becomes less attractive when correlation between projects is introduced. However, the risk of default and the expected returns of the vanilla megafund remain promising even under moderate levels of correlation. In addition, we find that a leveraged megafund outperforms an equity-only structure over a wide range of assumptions about correlation and PoS.
Spectral Factor Models2020
We 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 over frequencies. The restriction can hide horizon-specific pricing effects which spectral factor models are designed to reveal. We illustrate how the methods may lead to economically-meaningful dimensionality reduction in the factor space.
An Empirical Evaluation of Tax-Loss-Harvesting Alpha2020
Advances in financial technology have made tax-loss harvesting more feasible for retail investors than such strategies were in the past. We evaluated the magnitude of this “tax alpha” with the use of historical data from the CRSP monthly database for the 500 securities with the largest market capitalizations from 1926 to 2018. Given long-term and short-term capital gains tax rates of 15% and 35%, respectively, we found that a tax-loss-harvesting strategy yielded a before-transaction-cost tax alpha of 1.08% per year for our sample period. When the strategy was constrained by the “wash sale rule,” the tax alpha decreased from 1.08% per year to 0.82% per year.
The Challenging Economics of Vaccine Development in the Age of COVID-19, and What Can Be Done About It2020
Financing Vaccines for Global Health Security2020
Recent outbreaks of infectious pathogens such as Zika, Ebola, and COVID‐19 have underscored the need for the dependable availability of vaccines against emerging infectious diseases (EIDs). The cost and risk of R&D programs and uniquely unpredictable demand for EID vaccines have discouraged vaccine developers, and government and nonprofit agencies have been unable to provide timely or sufficient incentives for their development and sustained supply. We analyze the 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. We have sized the financing deficit for this portfolio and propose several potential solutions, including price increases, enhanced public‐private partnerships, and subscription models through which individuals would pay annual fees to obtain access to a portfolio of vaccines in the event of an outbreak.
Bayesian Adaptive Clinical Trials for Anti‐Infective Therapeutics During Epidemic Outbreaks2020
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.