Publications
Risk and Reward in the Orphan Drug Industry
2019Thanks 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.
Machine Learning with Statistical Imputation for Predicting Drug Approvals
2019We 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.
A Portfolio Approach to Accelerate Therapeutic Innovation in Ovarian Cancer
2019We 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.
Annals Issue in Honor of Jerry A. Hausman
2019It 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.
Acceleration of Rare Disease Therapeutic Development: A Case Study of AGIL-AADC
2019Rare-disease drug development is both scientifically and commercially challenging. This case study highlights Agilis Biotherapeutics (Agilis), a small private biotechnology company that has developed the most clinically advanced adeno-associated virus (AAV) gene therapy for the brain. In an international collaboration led by Agilis with National Taiwan University (NTU) Hospital and the Therapeutics for Rare and Neglected Diseases (TRND) program of the National Center for Advancing Translational Sciences (NCATS) at the National Institutes of Health, Agilis’ gene therapy for aromatic L-amino acid decarboxylase deficiency (AADC), AGIL-AADC, was granted biologics license application (BLA)-ready status by the FDA in 2018 only 18 months after being licensed from NTU by Agilis. Here, we highlight the factors that have enabled this remarkable pace of successful drug development for an ultra-rare disease.
Dynamic Alpha: A Spectral Decomposition of Investment Performance Across Time Horizons
2019The value added by an active investor is traditionally measured using alpha, tracking error, and the information ratio. However, these measures do not characterize the dynamic component of investor activity, nor do they consider the time horizons over which weights are changed. In this paper, we propose a technique to measure the value of active investment that captures both the static and dynamic contributions of an investment process. This dynamic alpha is based on the decomposition of a portfolio’s expected return into its frequency components using spectral analysis. The result is a static component that measures the portion of a portfolio’s expected return resulting from passive investments and security selection and a dynamic component that captures the manager’s timing ability across a range of time horizons. Our framework can be universally applied to any portfolio and is a useful method for comparing the forecast power of different investment processes. Several analytical and empirical examples are provided to illustrate the practical relevance of this decomposition.
Estimation of Clinical Trial Success Rates and Related Parameters
2019Previous estimates of drug development success rates rely on relatively small samples from databases curated by the pharmaceutical industry and are subject to potential selection biases. Using a sample of 406,038 entries of clinical trial data for over 21,143 compounds from January 1, 2000 to October 31, 2015, we estimate aggregate clinical trial success rates and durations. We also compute disaggregated estimates across several trial features including disease type, clinical phase, industry or academic sponsor, biomarker presence, lead indication status, and time. In several cases, our results differ significantly in detail from widely cited statistics. For example, oncology has a 3.4% success rate in our sample vs. 5.1% in prior studies. However, after declining to 1.7% in 2012, this rate has improved to 2.5% and 8.3% in 2014 and 2015, respectively. In addition, trials that use biomarkers in patient-selection have higher overall success probabilities than trials without biomarkers.
Is the FDA Too Conservative or Too Aggressive?: A Bayesian Decision Analysis of Clinical Trial Design
2019Implicit in the drug-approval process is a trade-off between Type I and Type II error. We propose using Bayesian decision analysis (BDA) to minimize the expected cost of drug approval, where relative costs are calibrated using U.S. Burden of Disease Study 2010 data. The results for conventional fixed-sample randomized clinical-trial designs suggest that for terminal illnesses with no existing therapies such as pancreatic cancer, the standard threshold of 2.5% is too conservative; the BDA-optimal threshold is 27.9%. However, for relatively less deadly conditions such as prostate cancer, 2.5% may be too risk-tolerant or aggressive; the BDA-optimal threshold is 1.2%. We compute BDA-optimal sizes for 25 of the most lethal diseases and show how a BDA-informed approval process can incorporate all stakeholders’ views in a systematic, transparent, internally consistent, and repeatable manner.
On Black’s Leverage Effect in Firms with No Leverage
2019One of the most enduring empirical regularities in equity markets is the inverse relationship between stock prices and volatility. Also known as the “leverage effect”, this relationship was first documented by Black (1976), who attributed it to the effects of financial or operating leverage. This paper documents that firms which had no debt (and thus no financial leverage) from January 1973 to December 2017 exhibit Black’s leverage effect. Moreover, it finds that the leverage effect of firms in this sample is not driven by operating leverage. On the contrary, in this sample the leverage effect is stronger for firms with low operating leverage as compared to those with high operating leverage. Interestingly, the firms with no debt from the lowest quintile of operating leverage exhibit the leverage effect that is on par with or stronger than that of debt-financed firms.
What Do Humans Perceive in Asset Returns?
2019In this article, the authors run experiments to test if and how human subjects can differentiate time series of actual asset returns from time series that are generated synthetically via various processes, including AR1. In contrast with previous anecdotal evidence, they find that subjects can distinguish between the two. These results show that temporal charts of asset prices convey to investors information that cannot be reproduced by summary statistics. They also provide a first refutation based on human perception of a strong form of the efficient-market hypothesis. Their experiments are implemented via an online video game (http://arora.ccs.neu.edu). The authors also link the subjects’ performance to statistical properties of the data and investigate whether subjects improve performance while playing.
Alzheimer’s Disease is About to Become a Crisis. Here’s How California Could Lead
2018Opinion article by Andrew W. Lo and Kenneth Kosik on the potential role of California in the space of Alzheimer's Disease research.
Biological Economics
2018This two-volume set brings together a unique collection of key publications at the intersection of biology and economics, two disciplines that share a common subject: Homo sapiens. Beginning with Thomas Malthus–whose dire predictions of mass starvation due to population growth influenced Charles Darwin–economists have routinely used biological arguments in their models and methods. This collection summarizes the most important of these developments, including articles in sociobiology, evolutionary psychology, behavioral ecology, behavioral economics and finance, neuroeconomics, and behavioral genomics. Together with an original introduction by the editors, this important research collection will appeal to economists, biologists, and practitioners looking to develop a deeper understanding of the limits of Homo Economicus.
The Visible Hand: A Review of The Guidance of an Enterprise Economy
2018It is a rare pleasure and honor for a former undergraduate student in Martin Shubik’s popular game theory classes at Yale University to be asked to write a review of his professor’s latest book, The Guidance of an Enterprise Economy, published by MIT Press in 2016. In contrast to the old saw in which “the student is now the master,” this volume confirms that the student is still the student and the master is—and always will be—the master.
Shubik, the world-renowned game theorist, and his co-author, Eric Smith, an impressive physicist cum biologist cum economist at the Santa Fe Institute, have undertaken an ambitious agenda to formulate a grand synthesis of the different levels of economic theory—financial, microeconomic, organizational, and macroeconomic—and reintroduce dynamics within the framework of general equilibrium (GE). They have written a fascinating, provocative, and occasionally frustrating volume that moves a much-neglected topic forward.
If Liberal Democracies Can Resist the Urge to Micromanage the Economy, Big Data Could Catalyze a New Capitalism
2018Capitalism is a powerful tool: By compressing enormous amounts of information regarding supply and demand into a single number—the market price—buyers and sellers are able to make remarkably intelligent decisions simply by engaging in self-interested behavior. But in a big-data world, where a supercomputer can fit into our pocket and a simple Internet search allows us to find every product under the Sun, do we still need it?
In Reinventing Capitalism in the Age of Big Data, Viktor Mayer-Schönberger and Thomas Ramge argue that big data will transform our economies on a fundamental level. Money will become obsolete, they argue, replaced by metadata. Instead of a single market price for each commodity, sophisticated matching algorithms will use a bundle of specifications and personal preferences to select just the right product for you. Artificial intelligence powered by machine-learning techniques will relentlessly negotiate the best possible transaction on your behalf. Capital will still be important, they concede, but increasingly just for its signaling content. “Venture informers” might even replace venture capitalists.
If Regulations Don’t Bend, They’ll Break
2018The tenth anniversary of the disastrous weekend that nearly brought down the global financial system is fast approaching. But in many of the jurisdictions that were central to the crisis, financial regulations introduced in the aftermath, aimed at preventing a repeat, are now being rolled back. The pendulum of regulation is now swinging back towards fewer and looser restrictions – and if the past is any guide, a ramp-up in systemic risk exposures will be the result.