The high cost of capital for firms conducting medical research and development (R&D) has been partly attributed to the government risk facing investors in medical innovation. This risk slows down medical innovation because investors must be compensated for it. We propose new and simple financial instruments, Food and Drug Administration (FDA) hedges, to allow medical R&D investors to better share the pipeline risk associated with FDA approval with broader capital markets. Using historical FDA approval data, we discuss the pricing of FDA hedges and mechanisms under which they can be traded and estimate issuer returns from offering them. Using various unique data sources, we find that FDA approval risk has a low correlation across drug classes as well as with other assets and the overall market. We argue that this zero-beta property of scientific FDA risk could be a main source of gains from trade between issuers of FDA hedges looking for diversified investments and developers looking to offload the FDA approval risk. We offer proof of concept of the feasibility of trading this type of pipeline risk by examining related securities issued around mergers and acquisitions activity in the drug industry. Overall, our argument is that, by allowing better risk sharing between those investing in medical innovation and capital markets more generally, FDA hedges could ultimately spur medical innovation and improve the health of patients.

What is the interaction between competition, R&D investments, and the financing choices of R&D-intensive firms? Motivated by existing theories, we hypothesize that as competition increases, R&D-intensive firms will: (1) increase R&D investment relative to assets-in-place that support existing products; (2) carry more cash; and (3) maintain less net debt. We provide causal evidence supporting these hypotheses by exploiting differences between the biopharma industry and other industries, as well as heterogeneity within the biopharma industry, in response to an exogenous change in competition. We also explore how these changes affect innovative output, and provide novel evidence that in response to greater competition, companies increasingly “focus” their efforts—there is a relative decline in the total number of innovations, but an increase in the economic value of these innovations.

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.

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.

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.

Economic shocks can have diverse effects on financial market dynamics at different time horizons, yet traditional portfolio management tools do not distinguish between short- and long-term components in alpha, beta, and covariance estimators. In this paper, we apply spectral analysis techniques to quantify stock-return dynamics across multiple time horizons.Using the Fourier transform, we decompose asset-return variances, correlations, alphas, and betas into distinct frequency components. These decompositions allow us to identify the relative importance of specific time horizons in determining each of these quantities, as well as to construct mean-variance-frequency optimal portfolios. Our approach can be applied to any portfolio, and is particularly useful for comparing the forecast power of multiple investment strategies. We provide several numerical and empirical examples to illustrate the practical relevance of these techniques.

We construct a computer simulation of a repeated double-auction market, designed to match those in experimental-market settings with human subjects, to model complex interactions among artificially-intelligent traders endowed with varying degrees of learning capabilities. In the course of six different experimental designs, we investigate a number of features of our agent-based model: the price efficiency of the market, the speed at which prices converge to the rational expectations equilibrium price, the dynamics of the distribution of wealth among the different types of AI-agents, trading volume, bid/ask spreads, and other aspects of market dynamics. We are able to replicate several findings of human-based experimental markets, however, we also find intriguing differences between agent-based and human-based experiments.