The interaction between product market competition, R&D investment, and the financing choices of R&D-intensive firms on the development of innovative products is only partially understood. We hypothesize that as competition increases, R&D-intensive firms will: i) increase R&D investment relative to existing assets in place; ii) carry more cash; and iii) maintain less net debt. Using the Hatch–Waxman Act as an exogenous shock to competition, we provide causal evidence supporting these hypotheses through a differences-in-differences analysis that exploits differences between the biopharma industry and other industries, and heterogeneity within the biopharma industry. We also explore how these changes affect innovative output.

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 across frequencies. The restriction can hide horizon-specific pricing effects that spectral factor models are designed to re- veal. We illustrate how the methods may lead to economically meaningful dimensionality reduction in the factor space.

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.

The 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.

One 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.

We examine the relation between changes in hedge fund equity holdings and measures of informational efficiency of stock prices derived from intraday transactions as well as daily data. On average, hedge fund ownership of stocks leads to greater improvements in price efficiency than mutual fund or bank ownership, especially for stocks held by hedge funds with high portfolio turnover and superior security selection skills. However, stocks held by hedge funds experienced large declines in price efficiency in the last quarter of 2008, particularly if the funds were connected to Lehman Brothers as a prime broker and used leverage in combination with lenient redemption terms.

We use a new dataset of hedge fund returns from a separate account platform to examine (1) how much of hedge fund return smoothing is due to main-fund specific factors, such as managerial reporting discretion (2) the costs of removing hedge fund share restrictions. These accounts trade pari passu with matching hedge funds but feature third-party reporting and permissive share restrictions. We use these properties to estimate that 33% of reported smoothing is due to managerial reporting methods. The platform's fund-level liquidity is associated with costs of 1.7% annually. Investor flows chase monthly past performance on the platform but not in the associated funds.

Technological advances in telecommunications, securities exchanges, and algorithmic trading have facilitated a host of new investment products that resemble theme-based passive indexes but which depart from traditional market-cap-weighted portfolios. I propose broadening the definition of an index using a functional perspective—any portfolio strategy that satisfies three properties should be considered an index: (1) it is completely transparent; (2) it is investable; and (3) it is systematic, i.e., it is entirely rules-based and contains no judgment or unique investment skill. Portfolios satisfying these properties that are not market-cap-weighted are given a new name: “dynamic indexes.” This functional definition widens the universe of possibilities and, most importantly, decouples risk management from alpha generation. Passive strategies can and should be actively risk managed, and I provide a simple example of how this can be achieved. Dynamic indexes also create new challenges of which the most significant is backtest bias, and I conclude with a proposal for managing this risk.

The hedge-fund industry has grown rapidly over the past two decades, offering investors unique investment opportunities that often reflect more complex risk exposures than those of traditional investments. In this article, we present a selective review of the recent academic literature on hedge funds as well as updated empirical results for this industry. Our review is written from several distinct perspectives: the investor’s, the portfolio manager’s, the regulator’s, and the academic’s. Each of these perspectives offers a different set of insights into the financial system, and the combination provides surprisingly rich implications for the Efficient Markets Hypothesis, investment management, systemic risk, financial regulation, and other aspects of financial theory and practice.

During the past few years, hedge fund beta replication strategies have become more common. At the same time, questions about the relevance, performance, and applicability of these strategies have been raised in response to the rapidly shifting landscape in the hedge fund industry. We present a review of the growing beta replication industry with particular emphasis on the ASG Global Alternatives Fund. We discuss the motivation for its existence and the logic of its absolute and relative performance over time and across different market environments. We also explain why these strategies are complements to, and not substitutes for, direct investments in hedge funds, and provide examples of their value-added in investors’ portfolios.

In the aftermath of the recent financial crisis, the federal government has pursued significant regulatory reforms, including proposals to measure and monitor systemic risk. However, there is much debate about how this might be accomplished quantitatively and objectively—or whether this is even possible. A key issue is determining the appropriate trade-offs between risk and reward from a policy and social welfare perspective given the potential negative impact of crises.

One of the first books to address the challenges of measuring statistical risk from a system-wide perspective, Quantifying Systemic Risk looks at the means of measuring systemic risk and explores alternative approaches. Among the topics discussed are the challenges of tying regulations to specific quantitative measures, the effects of learning and adaptation on the evolution of the market, and the distinction between the shocks that start a crisis and the mechanisms that enable it to grow.

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.

With the financial crisis continuing after five years, people are questioning why economics failed either to send an adequate early warning ahead of the crisis or to resolve it quickly. The gap between important real-world problems and the workhorse mathematical model-based economics being taught to students has become a chasm. Students continue to be taught as if not much has changed since the crisis, as there is no consensus about how to change the curriculum. Meanwhile, employer discontent with the knowledge and skills of their graduate economist recruits has been growing. This book examines what economists need to bring to their jobs, and the way in which education in universities could be improved to fit graduates better for the real world. It is based on an international conference in February 2012, sponsored by the UK Government Economic Service and the Bank of England, which brought employers and academics together. Three themes emerged: the narrow range of skills and knowledge demonstrated by graduates; the need for reform of the content of the courses they are taught; and the barriers to curriculum reform. While some issues remain unresolved, there was strong agreement on such key issues as the strengthening of economic history, the teaching of inductive as well as deductive reasoning, critical evaluation and communication skills, and a better alignment of lecturers' incentives with the needs of their students.

Some economic events are so major and unsettling that they “change everything.” Such is the case with the financial crisis that started in the summer of 2007 and is still a drag on the world economy. Yet enough time has now elapsed for economists to consider questions that run deeper than the usual focus on the immediate causes and consequences of the crisis. How have these stunning events changed our thinking about the role of the financial system in the economy, about the costs and benefits of financial innovation, about the efficiency of financial markets, and about the role the government should play in regulating finance? In Rethinking the Financial Crisis, some of the nation’s most renowned economists share their assessments of particular aspects of the crisis and reconsider the way we think about the financial system and its role in the economy.

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.