Impact investing typically involves ranking and selecting assets based on a non-financial impact factor, such as the environmental, social, and governance (ESG) score and the prospect of developing a disease-curing drug. We develop a framework for constructing optimal impact portfolios and quantifying their financial performances. Under general bivariate distributions of the impact factor and residual returns from a multi-factor asset-pricing model, the construction and performance of optimal impact portfolios depend critically on the dependence structure (copula) between the two, which reduces to a correlation under normality assumptions. More generally, we explicitly derive the optimal portfolio weights under two widely-used copulas---the Gaussian copula and the Archimedean copula family, and find that the optimal weights depend on the tail characteristics of the copula. In addition, when the marginal distribution of residual returns is skewed or heavy-tailed, assets with the most extreme impact factors have lower weights than non-extreme assets due to their high risk. Our framework requires the estimation of only a constant number of parameters as the number of assets grow, an advantage over traditional Markowitz portfolios. Overall, these results provide a recipe for constructing and quantifying the performance of optimal impact portfolios with arbitrary dependence structures and return distributions.

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.

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.

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 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.

During the week of August 6, 2007, a number of quantitative long/short equity hedge funds experienced unprecedented losses. It has been hypothesized that a coordinated deleveraging of similarly constructed portfolios caused this temporary dislocation in the market. Using the simulated returns of long/short equity portfolios based on five specific valuation factors, we find evidence that the unwinding of these portfolios began in July 2007 and continued until the end of 2007. Using transactions data, we find that the simulated returns of a simple market-making strategy were significantly negative during the week of August 6, 2007, but positive before and after, suggesting that the Quant Meltdown of August 2007 was the combined effects of portfolio deleveraging throughout July and the first week of August, and a temporary withdrawal of market-making risk capital starting August 8th. Our simulations point to two unwinds—a mini-unwind on August 1st starting at 10:45am and ending at 11:30am, and a more sustained unwind starting at the open on August 6th and ending at 1:00pm—that began with stocks in the financial sector and long Book-to-Market and short Earnings Momentum. These conjectures have significant implications for the systemic risks posed by the hedge-fund industry.