Ever since the Great Recession, the global financial regulatory system has undergone significant changes. But have these changes been sufficient? Have they created a new problem of over-regulation? Is the system currently in a better position than in the pre-Recession years, or have we not adequately addressed the basic causes of the financial crisis and resulting Great Recession These were the questions and issues addressed in the seventeenth annual international banking conference held at the Federal Reserve Bank of Chicago in November 2014. In collaboration with the Bank of England, the theme of the conference was to examine the state of the new global financial system as it has evolved in response to significant market changes and regulatory reforms triggered by the global financial crisis. The papers from that conference are collected in this volume, with contributions from an international array of government officials, regulators, industry practitioners and academics.

Intelligence does not arise only in individual brains; it also arises in groups of individuals. This is collective intelligence: groups of individuals acting collectively in ways that seem intelligent. In recent years, a new kind of collective intelligence has emerged: interconnected groups of people and computers, collectively doing intelligent things. Today these groups are engaged in tasks that range from writing software to predicting the results of presidential elections. This volume reports on the latest research in the study of collective intelligence, laying out a shared set of research challenges from a variety of disciplinary and methodological perspectives. Taken together, these essays—by leading researchers from such fields as computer science, biology, economics, and psychology—lay the foundation for a new multidisciplinary field.

Pioneered by the Nobel Prize–winning economist Harry Markowitz over half a century ago, portfolio theory is one of the oldest branches of modern financial economics. It addresses the fundamental question faced by an investor: how should money best be allocated across a number of possible investment choices? That is, what collection or portfolio of financial assets should be chosen? In this article, we describe the fundamentals of portfolio theory and methods for its practical implementation. We focus on a fixed time horizon for investment, which we generally take to be a year, but the period may be as short as days or as long as several years. We summarize many important innovations over the past several decades, including techniques for better understanding how financial prices behave, robust methods for estimating input parameters, Bayesian methods, and resampling techniques.

We apply spectral techniques to analyze the volatility and correlation of U.S. common-stock returns across multiple time horizons at the aggregate-market and individual-firm level. Using the cross-periodogram to construct frequency bandlimited measures of variance, correlation and beta, we find that volatilities and correlations change not only in magnitude over time, but also in frequency. Factors that may be responsible for these trends are proposed and their implications for portfolio construction are explored.

As price-gouging practices by a handful of drug companies attract headlines, one troubling aspect of the story remains underplayed. Exorbitant increases in the prices of existing drugs, including generics, are motivated not just by crass profiteering but by a deep skepticism about the economic feasibility of developing new drugs. That skepticism is justified.

Traditional models for funding drug development are faltering. In the US and many other developed countries, the average cost of bringing a new drug to market has skyrocketed, even as patents on some of the industry’s most profitable drugs have expired. Venture capital has pulled back from early-stage life-sciences companies, and big pharmaceutical companies have seen fewer drugs reach the market per dollar spent on research and development...

In this opinion piece, MIT Sloan Professor Andrew Lo and Princeton Professor Simon Levin write, "We propose that the financial system has crossed a threshold of complexity where the system is evolving faster than regulators and regulations can keep pace. For example, the system is now truly globally connected, but coordination across sovereign jurisdictions is difficult to achieve. This new situation calls for a new perspective, one based on a different paradigm than the ones on which financial regulation is currently based, such as efficient markets, rational expectations, and models patterned after the physical sciences."

A document answering frequently asked questions about the megafund idea.

The agglomeration of rules and regulations over time has produced a body of legal code that no single individual can fully comprehend. This complexity produces inefficiencies, makes the processes of understanding and changing the law difficult,and frustrates the fundamental principle that the law should provide fair notice to the governed. In this Article, we take a quantitative, unbiased, and software-engineering approach to analyze the evolution of the United States Code from 1926 to today. Software engineers frequently face the challenge of understanding and managing large, structured collections of instructions, directives, and conditional statements, and we adapt and apply their techniques to the U.S. Code over time. Our work produces insights into the structure of the U.S. Code as a whole, its strengths and vulnerabilities, and new ways of thinking about individual laws. For example, we identify the first appearance and spread of important terms in the U.S. Code like “whistleblower” and “privacy.” We also analyze and visualize the network structure of certain substantial reforms,including the Patient Protection and Affordable Care Act and the Dodd-Frank Wall Street Reform and Consumer Protection Act, and show how the interconnections of references can increase complexity and create the potential for unintended consequences. Our work is a timely illustration of computational approaches to law as the legal profession embraces technology for scholarship in order to increase efficiency and to improve access to justice.

In Brennan and Lo (2010), a mean-variance efficient frontier is defined as “impossible” if every portfolio on that frontier has negative weights, which is incompatible with the Capital Asset Pricing Model (CAPM) requirement that the market portfolio is mean-variance efficient. We prove that as the number of assets n grows, the probability that a randomly chosen frontier is impossible tends to one at a geometric rate, implying that the set of parameters for which the CAPM holds is extremely rare. Levy and Roll (2014) argue that while such “possible”frontiers are rare, they are ubiquitous. In this reply, we show that this is not the case; possible frontiers are not only rare,but they occupy an isolated region of mean-variance parameter space that becomes increasingly remote as n increases. Ingersoll (2014) observes that parameter restrictions can rule out impossible frontiers, but in many cases these restrictions contradict empirical fact and must be questioned rather than blindly imposed.

A business development company (BDC) is a type of closed-end investment fund with certain relaxed requirements that allow it to raise money in the public equity and debt markets, and can be used to fund multiple early-stage biomedical ventures, using financial diversification to de-risk translational medicine. By electing to be a “Regulated Investment Company” for tax purposes, a BDC can avoid double taxation on income and net capital gains distributed to its shareholders. BDCs are ideally suited for long-term investors in biomedical innovation, including: (i) investors with biomedical expertise who understand the risks of the FDA approval process, (ii) “banking entities,” now prohibited from investing in hedge funds and private equity funds by the Volcker Rule, but who are permitted to invest in BDCs, subject to certain restrictions, and (iii) retail investors, who traditionally have had to invest in large pharmaceutical companies to gain exposure to similar assets. We describe the history of BDCs, summarize the requirements for creating and managing them, and conclude with a discussion of the advantages and disadvantages of the BDC structure for funding biomedical innovation.

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.

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.

The dynamic stochastic general equilibrium model (DSGE) marked a major milestone by capturing the dynamic change of economic variables over time. However, many DSGE models were exposed as having omitted critical structural linkages relevant to the financial crisis. To address these deficiencies, existing DSGE models should be enhanced to better incorporate the role of the financial sector and financial markets. In addition, these models should reexamine key micro-foundations of the model and consider behavioral components.

Translating scientific research into safe and effective drugs takes money—lots of money. Current estimates put the cost of developing a single successful drug at more than $2 billion by the time you include all the dead ends along the way; the out-of-pocket cost for just a single attempt is about $200 million. Drug development usually takes a decade or longer, and the probability of success is low (historically around 5 percent for oncology). As a result, investors are now shying away from the pharmaceutical industry, investing instead in less risky and more attractive opportunities like big data, social media and e-commerce. Financial engineering techniques can help change that, directing capital from those wishing to invest it to those who need it to develop new drugs.

The contemporary global community is increasingly interdependent and confronted with systemic risks posed by the actions and interactions of actors existing beneath the level of formal institutions, often operating outside effective governance structures. Frequently, these actors are human agents, such as rogue traders or aggressive financial innovators, terrorists, groups of dissidents, or unauthorized sources of sensitive or secret information about government or private sector activities. In other instances, influential “actors” take the form of climate change, communications technologies, or socioeconomic globalization. Although these individual forces may be small relative to state governments or international institutions, or may operate on long time scales, the changes they catalyze can pose significant challenges to the analysis and practice of international relations through the operation of complex feedbacks and interactions of individual agents and interconnected systems. We call these challenges “femtorisks,” and emphasize their importance for two reasons. First, in isolation, they may be inconsequential and semiautonomous; but when embedded in complex adaptive systems, characterized by individual agents able to change, learn from experience, and pursue their own agendas, the strategic interaction between actors can propel systems down paths of increasing, even global, instability. Second, because their influence stems from complex interactions at interfaces of multiple systems (e.g., social, financial, political, technological, ecological, etc.), femtorisks challenge standard approaches to risk assessment, as higher-order consequences cascade across the boundaries of socially constructed complex systems. We argue that new approaches to assessing and managing systemic risk in international relations are required, inspired by principles of evolutionary theory and development of resilient ecological systems.