Research

Despite many compelling applications in economics, sociobiology, and evolutionary psychology, group selection is still one of the most hotly contested ideas in evolutionary biology. Here we propose a simple evolutionary model of behavior and show that what appears to be group selection may, in fact, simply be the consequence of natural selection occurring in stochastic environments with reproductive risks that are correlated across individuals. Those individuals with highly correlated risks will appear to form “groups”, even if their actions are, in fact, totally autonomous, mindless, and, prior to selection, uniformly randomly distributed in the population. This framework implies that a separate theory of group selection is not strictly necessary to explain observed phenomena such as altruism and cooperation. At the same time, it shows that the notion of group selection does captures a unique aspect of evolution—selection with correlated reproductive risk–that may be sufficiently widespread to warrant a separate term for the phenomenon.

Risk aversion is one of the most basic assumptions of economic behavior, but few studies have addressed the question of where risk preferences come from and why they differ from one individual to the next. Here, we propose an evolutionary explanation for the origin of risk aversion. In the context of a simple binary-choice model, we show that risk aversion emerges by natural selection if reproductive

risk is systematic (i.e., correlated across individuals in a given generation). In contrast, risk neutrality emerges if reproductive risk is idiosyncratic (i.e., uncorrelated across each given generation). More generally, our framework implies that the degree of risk

aversion is determined by the stochastic nature of reproductive rates, and we show that different statistical properties lead to different utility functions. The simplicity and generality of ourmodel suggest that these implications are primitive and cut across species, physiology, and genetic origins.

Much of financial regulation and supervision is devoted to ensuring the safety and soundness of financial institutions. Such micro- and macroprudential policies are almost always formulated as capital requirements, leverage constraints, and other statutory restrictions designed to limit the probability of extreme financial loss to some small but acceptable threshold. However, if the risks of a financial institution's assets vary over time and across circumstances, then the efficacy of financial regulations necessarily varies in lockstep unless the regulations are adaptive. We illustrate this princilple with empirical examples drawn from the financial industry, and show how the interaction of certain regulations with dynamic loss probabilities can have the unintended consequence of amplifying financial losses. We propose an ambitious research agenda in which legal scholars and financial economists collaborate to develop optimally adaptive regulations that anticipate the endogeneity of risk-taking behavior.

The portfolio of the National Center for Advancing Translational Sciences (NCATS) rare diseases therapeutic development program comprises 28 research projects initiated at the preclinical stage. Historical data reveal substantially lower costs and higher success rates but longer preclinical timelines for the NCATS projects relative to the industry averages for early-stage translational medical research and development (R&D) typically cited in literature. Here, we evaluate the potential risks and rewards of investing in a portfolio of rare-disease therapeutics. Using a “megafund” financing structure, NCATS data, and valuation estimates from a panel of industry experts, we simulate a hypothetical megafund in which senior and junior debt yielded 5 and 8%, respectively. The simulated expected return to equity was 14.7%, corresponding to a modified internal rate of return of 21.6%. These returns and the likelihood of private-sector funding can be enhanced through third-party funding guarantees from philanthropies, patient advocacy groups, and government agencies.

We explore a new dimension of fund managers' timing ability by examining whether they can time market liquidity through adjusting their portfolios' market exposure as aggregate liquidity conditions change. Using a large sample of hedge funds, we find strong evidence of liquidity timing. A bootstrap analysis suggests that top-ranked liquidity timers cannot be attributed to pure luck. In out-of-sample tests, top liquidity timers outperform bottom timers by 4.0–5.5% annually on a risk-adjusted basis. We also find that it is important to distinguish liquidity timing from liquidity reaction, which primarily relies on public information. Our results are robust to alternative explanations, hedge fund data biases, and the use of alternative timing models, risk factors, and liquidity measures. The findings highlight the importance of understanding and incorporating market liquidity conditions in investment decision making.

To reduce risk, investors seek assets that have high expected return and are unlikely to move in tandem. Correlation measures are generally used to quantify the connections between equities. The 2008 nancial crisis, and its aftermath, demonstrated the need for a better way to quantify these connections. We present a machine learning-based method to build a connectedness matrix to address the shortcomings of correlation in capturing events such as large losses. Our method uses an unconstrained optimization to learn this matrix, while ensuring that the resulting matrix is positive semi-de nite. We show that this matrix can be used to build portfolios that not only beat the market," but also outperform optimal (i.e., minimum variance) portfolios.

The biotechnology and pharmaceutical industries are facing significant challenges to their existing business models because of expiring drug patents, declining risk tolerance of venture capitalists and other investors, and increasing complexity in translational medicine. In response to these challenges, new alternative investment companies have emerged to bridge the biopharma funding gap by purchasing economic interests in drug royalty streams. Such purchases allow universities and biopharma companies to monetize their intellectual property, creating greater financial flexibility for them while giving investors an opportunity to participate in the life sciences industry at lower risk. Royalty Pharma is the largest of these drug royalty investment companies, and in this case study, we profile its business model and show how its unique financing structure greatly enhances the impact it has had on the biopharma industry and biomedical innovation.

Recently proposed ‘megafund’ financing methods for funding translational medicine and drug development require billions of dollars in capital per megafund to de-risk the drug discovery process enough to issue long-term bonds. Here, we demonstrate that the same financing methods can be applied to orphan drug development but, because of the unique nature of orphan diseases and therapeutics (lower development costs, faster FDA approval times, lower failure rates and lower correlation of failures among disease targets) the amount of capital needed to de-risk such portfolios is much lower in this field. Numerical simulations suggest that an orphan disease megafund of only US$575 million can yield double-digit expected rates of return with only 10–20 projects in the portfolio. To download the open-source software, please click here.

There exist scientifically promising treatments not being tested further because of insufficient financial incentives. Many of these therapies involve off-label uses of drugs approved by the Food and Drug Administration that are readily available and often inexpensive. Pharmaceutical companies—largely responsible for clinical drug development—cannot justify investing in such clinical trials because they cannot recoup the costs of these studies. However, without prospective data demonstrating efficacy, such treatments will never be adopted as standard of care.

In an era of increasing health care costs and the need for effective therapies in many diseases, it is essential that society finds ways to adopt these “financial orphans.” We propose several potential solutions for the non-profit sector, pharmaceutical companies, health insurers, patient driven research, and others to accomplish this goal.

As the prevalence of Alzheimer’s disease (AD) grows, so do the costs it imposes on society. Scientific, clinical, and financial interests have focused current drug discovery efforts largely on the single biological pathway that leads to amyloid deposition. This effort has resulted in slow progress and disappointing outcomes. Here, we describe a “portfolio approach” in which multiple distinct drug development projects are undertaken simultaneously. Although a greater upfront investment is required, the probability of at least one success should be higher with “multiple shots on goal,” increasing the efficiency of this undertaking. However, our portfolio simulations show that the risk-adjusted return on investment of parallel discovery is insufficient to attract private-sector funding. Nevertheless, the future cost savings of an effective AD therapy to Medicare and Medicaid far exceed this investment, suggesting that government funding is both essential and financially beneficial.