The reports by Wim van Harten and colleagues and Sabine Vogler and colleagues in The Lancet Oncology on the costs of cancer drugs in European countries deserve special attention from all oncology and biopharmaceutical stakeholders. van Harten identified that, in 15 European countries, list prices can be up to 92% lower than the highest reported, with actual prices paid up to 58% lower. These findings are backed up by Vogler and colleagues' study in 16 European countries, Australia, and New Zealand, which documented that highest-minus-lowest list price differences ranged from 28% to 388% for cancer drugs. Such variability argues strongly for greater transparency in drug pricing and the circumstances leading to such differences. But most importantly, it underscores the need to establish the true value of cancer therapies, and those who have championed this cause have been handed unequivocal evidence confirming what they have long suspected: drug prices are typically driven by what the market will bear.

With the rise of social media, investors have a new tool for measuring sentiment in real time. However, the nature of these data sources raises serious questions about its quality. Because anyone on social media can participate in a conversation about markets—whether the individual is informed or not—these data may have very little information about future asset prices. In this article, the authors show that this is not the case. They analyze a recurring event that has a high impact on asset prices—Federal Open Market Committee (FOMC) meetings—and exploit a new dataset of tweets referencing the Federal Reserve. The authors show that the content of tweets can be used to predict future returns, even after controlling for common asset pricing factors. To gauge the economic magnitude of these predictions, the authors construct a simple hypothetical trading strategy based on this data. They find that a tweet-based asset allocation strategy outperforms several benchmarks—including a strategy that buys and holds a market index, as well as a comparable dynamic asset allocation strategy that does not use Twitter information.

Duchenne muscular dystrophy (DMD) is a rare genetic disorder affecting thousands of individuals, mainly young males, worldwide. Currently, the disease has no cure, and is fatal in all cases. Advances in our understanding of the disease and innovations in basic science have recently allowed biotechnology companies to pursue promising treatment candidates for the disease, but so far, only one drug with limited application has achieved FDA approval. In this case study, we profile the work of an early-stage life sciences company, Solid Biosciences, founded by a father of a young boy with DMD. In particular, we discuss Solid’s one-disease focus and its strategy to treat the disease with a diversified portfolio of approaches. The company is currently building a product pipeline consisting of genetic interventions, small molecules and biologics, and assistive devices, each aimed at addressing a different aspect of DMD. We highlight the potential for Solid’s business model and portfolio to achieve breakthrough treatments for the DMD patient community.

Moderator Martin Leibowitz asked a panel of industry experts—Andrew W. Lo, Robert C. Merton, Stephen A. Ross, and Jeremy Siegel—what they saw as the most important issues in finance, especially as those issues relate to practitioners. Drawing on their vast knowledge, these panelists addressed topics such as regulation, technology, and financing society’s challenges; opacity and trust; the social value of finance; and future expected returns.

WSJ Wealth Expert Andrew W. Lo of MIT says robo advisers are the rotary phones to today’s iPhone--technology that has great potential but it still immature.

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.

A crisis is building over the prices of new transformative therapies for cancer, hepatitis C virus infection, and rare diseases. The clinical imperative is to offer these therapies as broadly and rapidly as possible. We propose a practical way to increase drug affordability through health care loans (HCLs)—the equivalent of mortgages for large health care expenses. HCLs allow patients in both multipayer and single-payer markets to access a broader set of therapeutics, including expensive short-duration treatments that are curative. HCLs also link payment to clinical benefit and should help lower per-patient cost while incentivizing the development of transformative therapies rather than those that offer small incremental advances. Moreover, we propose the use of securitization—a well-known financial engineering method—to finance a large diversified pool of HCLs through both debt and equity. Numerical simulations suggest that securitization is viable for a wide range of economic environments and cost parameters, allowing a much broader patient population to access transformative therapies while also aligning the interests of patients, payers, and the pharmaceutical industry.

Opinion Editorial

"A new class of medications was recently approved that cures more than 95 percent of people with Hepatitis C in only six weeks at a cost of about $84,000 per person, and new therapies with price tags that are likely to exceed $1 million per person are now available or coming soon. How can patients possibly afford them?

"In an article published in the journal Science Translation Medicine, we outline a feasible market-based solution that could immediately expand access to transformative medications, including cures for Hepatitis C and cancer. The basic concept is to convert a large upfront medical expense into a series of more affordable payments, akin to getting a mortgage when buying a house. The challenge of curative medications that only require a short course of therapy is that the whole price is paid upfront — how many homeowners could buy their houses using only cash? Instead, most home buyers get a mortgage and make monthly payments for as long as they benefit from owning the house or until the full amount is paid. We propose the same solution to overcome the liquidity problem that prevents access to curative medications, which we call “health care loans,” or HCLs..."

We extend the megafund concept for funding drug discovery to enable dynamic leverage in which the portfolio of candidate therapeutic assets is predominantly financed initially by equity, and debt is introduced gradually as assets mature and begin generating cash flows. Leverage is adjusted so as to maintain an approximately constant level of default risk throughout the life of the fund. Numerical simulations show that applying dynamic leverage to a small portfolio of orphan drug candidates can boost the return on equity almost twofold compared with securitization with a static capital structure. Dynamic leverage can also add significant value to comparable all-equity-financed portfolios, enhancing the return on equity without jeopardizing debt performance or increasing risk to equity investors.

Using account level credit-card data from six major commercial banks from January 2009 to December 2013, we apply machine-learning techniques to combined consumer-tradeline, credit-bureau, and macroeconomic variables to predict delinquency. In addition to providing accurate measures of loss probabilities and credit risk, our models can also be used to analyze and compare risk management practices and the drivers of delinquency across the banks. We find substantial heterogeneity in risk factors, sensitivities, and predictability of delinquency across banks, implying that no single model applies to all six institutions. We measure the efficacy of a bank’s risk-management process by the percentage of delinquent accounts that a bank manages effectively, and find that efficacy also varies widely across institutions. These results suggest the need for a more customized approached to the supervision and regulation of financial institutions, in which capital ratios, loss reserves, and other parameters are specified individually for each institution according to its credit-risk model exposures and forecasts.

In this article, we suggest an alternative structure for undertaking the long-term, high-risk, highly capital-intensive R&D programs that typify science-based settings. We refer to this structure as a project-focused organization (PFO). PFOs are entities that are created with the sole purpose of conducting a specific R&D project. When the project is completed, the PFO is disbanded, residual returns (if there are any) are distributed to investors, and intellectual property and other assets are sold off. We think PFOs are an attractive alternative to both the traditional vertical integration model and the traditional venture capital/entrepreneurial startup model. We discuss how such PFOs could work in practice, using the example of biopharmaceutical R&D, although we argue that the structure has much broader applicability.

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.

Culture is a potent force in shaping individual and group behavior, yet it has received scant attention in the context of financial risk management and the recent financial crisis. I present a brief overview of the role of culture according to psychologists, sociologists, and economists, and then present a specific framework for analyzing culture in the context of financial practices and institutions in which three questions are answered: (1) What is culture?; (2) Does it matter?; and (3) Can it be changed? I illustrate the utility of this framework by applying it to five concrete situations—Long Term Capital Management; AIG Financial Products; Lehman Brothers and Repo 105; Société Générale’s rogue trader; and the SEC and the Madoff Ponzi scheme—and conclude with a proposal to change culture via “behavioral risk management.”

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.