In this case study, we profile the I-SPY 2 TRIAL (Investigation of Serial studies to Predict Your Therapeutic Response with Imaging And molecular anaLysis 2), a unique breast cancer clinical trial led by researchers at 20 leading cancer centers across the US, and examine its potential to serve as a model of drug development for other disease areas. This multicenter collaboration launched in 2010 to reengineer the drug development process to be more efficient and patient-centered. We observe that I-SPY 2 possesses several novel features that could be used as a template for more efficient and cost effective drug development, namely its adaptive trial design; precompetitive network of stakeholders; and flexible infrastructure to accommodate innovation.

Stop-loss strategies are commonly used by investors to reduce their holdings in risky assets if prices or total wealth breach certain pre- specified thresholds. We derive closed-form expressions for the impact of stop-loss strategies on asset returns that are serially correlated, regime switching, and subject to transaction costs. When applied to a large sample of individual U.S. stocks, we show that tight stop-loss strategies tend to under-perform the buy-and-hold policy in a mean-variance frame work due to excessive trading costs. Outperformance is possible for stocks with sufficiently high serial correlation in returns. Certain strategies succeed at reducing downside risk, but not substantially.

We propose an evolutionary framework for optimal portfolio growth theory in which investors subject to environmental pressures allocate their wealth between two assets. By considering both absolute wealth and relative wealth between investors, we show that different investor behaviors survive in different environments. When investors maximize their relative wealth, the Kelly criterion is optimal only under certain conditions, which are identified. The initial relative wealth plays a critical role in determining the deviation of optimal behavior from the Kelly criterion regardless of whether the investor is myopic across a single time period or maximizing wealth over an infinite horizon. We relate these results to population genetics, and discuss testable consequences of these findings using experimental evolution.

Translating academic medical research into new therapies is an important challenge for the biopharmaceutical industry and investment communities, which have historically favored later-stage assets with lower risk and clearer commercial value. The Stanford SPARK program is an innovative model for addressing this challenge. The program was created in 2006 to educate students and faculty about bringing academic research from bench to bedside. Every year, the program provides mentorship and funding for approximately a dozen SPARK ‘scholars,’ with a focus on impacting patient lives, regardless of economic factors. By reviewing the detailed structure, function and operation of SPARK we hope to provide a template for other universities and institutions interested in de-risking and facilitating the translation of biomedical research.

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.

The Food and Drug Administration (FDA) is a remarkable agency, one of the crown jewels of the US government. Its staff and structure are dedicated to safeguarding American public health, and although we sometimes complain about its role as gatekeeper, we all sleep better knowing that our foods and drugs have passed the FDA’s careful scrutiny. Its regulatory scope and process reflect the technical demands of its responsibilities, and the FDA is one of the very few federal agencies that have taken a lead in defining and developing the new field of regulatory science.

Breakthroughs in computing hardware, software, telecommunications, and data analytics have transformed the financial industry, enabling a host of new products and services such as automated trading algorithms, crypto-currencies, mobile banking, crowdfunding, and robo-advisors. However, the unintended consequences of technology-leveraged finance include firesales, flash crashes, botched initial public offerings, cybersecurity breaches, catastrophic algorithmic trading errors, and a technological arms race that has created new winners, losers, and systemic risk in the financial ecosystem. These challenges are an unavoidable aspect of the growing importance of finance in an increasingly digital society. Rather than fighting this trend or forswearing technology, the ultimate solution is to develop more robust technology capable of adapting to the foibles in human behavior so users can employ these tools safely, effectively, and effortlessly. Examples of such technology are provided.

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.

Andrew Lo discusses the tension between statistical significance (p-values) and patient values in medical research. Based on his research, he proposes a Bayesian decision analysis framework for making regulatory decisions that reflects differences in both the impact of diseases and stakeholder perspectives in a systematic, objective, transparent and repeatable manner.

During a half-hour interval on May 6, 2010, stock prices for some of the largest companies in the world dropped precipitously, some to just pennies a share. Then, just as suddenly and inexplicably, shares recovered to their pre-crash prices. This unprecedented event, burned into the memories of investors and regulators alike, is now known as the Flash Crash. Since that day, financial markets have seen flash crashes in US Treasury securities, foreign currencies, and exchange-traded funds (ETFs). Other puzzling, system-wide glitches are becoming more frequent as well. Without a doubt, our financial systems are complex and often unpredictable, and when they swing out of control they remind us how much we still have to learn about how they work and how inadequate our traditional methods of controlling them are.

In response to the senators January 21, 2016 request for comment on their Sovaldi report, February 27, 2016. On behalf of all patients and their family members and friends, thank you for conducting the study on the pricing strategy of Gilead Sciences and shining a spotlight on the issue of drug pricing. When access to life-saving therapies is limited by affordability, important moral and ethical issues must be considered in addition to economic and political ones. For too long, we in the United States have ignored these issues for fear of “death panels” and difficult end-of-life decisions. But the growing number of breakthrough therapies and the rising cost of healthcare will soon force us to confront these issues directly. Your report and is an important step in helping us to develop a rational, ethical approach to dealing with this looming challenge.

The authors introduce a new approach to identifying and monitoring systemic risk that combines network analysis and tail risk contribution (TRC). Network analysis provides great flexibility in representing and exploring linkages between institutions, but it can be overly general in describing the risk exposures of one entity to another. TRC provides a more focused view of key systemic risks and richer financial intuition, but it may miss important linkages between financial institutions. Integrating these two methods can provide information on key relationships between institutions that may become relevant during periods of systemic stress. The authors demonstrate this approach using the exposures of money market funds to major financial institutions during July 2011. The results for their example suggest that TRC networks can highlight both institutions and funds that may become distressed during a financial crisis.

Americans are increasingly apprehensive about our future, so it is inspiring when Congress produces legislation intended to both enhance our health and expand our economy. The 21st Century Cures Act, recently passed by the House with an impressive bipartisan majority vote of 344 to 77, intends to accelerate the many-step process of drug discovery and development, from basic scientific research to clinical development to delivery, distribution, and ongoing monitoring. Among other things, the legislation boosts National Institute of Health funding, dramatically speeds up the US Food and Drug Administration (FDA) approval process, and aims to make use of new information technology to better monitor the performance of medical products after they reach the market. This landmark bill now awaits a comparable piece of legislation being developed by the Senate Health Education, Labor, and Pensions Committee. Together, they will transform the biomedical ecosystem and provide the foundation for the next several decades of innovative life-saving and health-enhancing solutions for our nation and the world.

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.