Half of all Americans have money in the stock market, yet economists can't agree on whether investors and markets are rational and efficient, as modern financial theory assumes, or irrational and inefficient, as behavioral economists believe—and as financial bubbles, crashes, and crises suggest. This is one of the biggest debates in economics and the value or futility of investment management and financial regulation hang on the outcome. In this groundbreaking book, Andrew Lo cuts through this debate with a new framework, the Adaptive Markets Hypothesis, in which rationality and irrationality coexist.

Drawing on psychology, evolutionary biology, neuroscience, artificial intelligence, and other fields, Adaptive Markets shows that the theory of market efficiency isn't wrong but merely incomplete. When markets are unstable, investors react instinctively, creating inefficiencies for others to exploit. Lo's new paradigm explains how evolution shapes behavior and markets at the speed of thought—a fact revealed by swings between stability and crisis, profit and loss, and innovation and regulation.

A fascinating intellectual journey filled with compelling stories, Adaptive Markets starts with the origins of market efficiency and its failures, turns to the foundations of investor behavior, and concludes with practical implications—including how hedge funds have become the Galápagos Islands of finance, what really happened in the 2008 meltdown, and how we might avoid future crises.

Ever since I was a graduate student in economics, I’ve been struggling with the uncomfortable observation that economic theories often don’t seem to work in practice. That goes for that most influential economic theory, the Efficient Markets Hypothesis, which holds that investors are rational decision makers and market prices fully reflect all available information, that is, the “wisdom of crowds.”

Recent cases of aggressive pricing behavior in the biopharmaceutical industry have raised serious concerns among payers and policymakers about industry ethics. However, these cases should not be confused with price increases motivated by challenging business conditions that ultimately lead to greater investment in R&D and improved patient access to therapeutics. We study the example of Questcor Pharmaceuticals, which was forced to choose between increasing the price of an effective drug in 2007 and ceasing production and shutting down. We consider Questcor’s journey from inception to its acquisition in 2014, analyze the factors leading up to the price hike of its main revenue generator, Acthar Gel, and discuss its resulting impact on patients after 2007. A counterfactual financial simulation of the company’s prospects in the case where prices were not increased shows that Questcor would have become insolvent between 2008 and 2010.

Uncertainty surrounding the risk and reward of investments in biopharmaceutical companies poses a challenge to those interested in funding such enterprises. Using data on publicly traded stocks, we track the performance of 1,066 biopharmaceutical companies from 1930 to 2015—the most comprehensive financial analysis of this sector to date. Our systematic exploration of methods for distinguishing biotech and pharmaceutical companies yields a dynamic, more accurate classification method. We find that the performance of the biotech sector is highly sensitive to the presence of a few outlier companies, and confirm that nearly all biotech companies are loss-making enterprises, exhibiting high stock volatility. In contrast, since 2000, pharmaceutical companies have become increasingly profitable, with risk-adjusted returns consistently outperforming the market. The performance of all biopharmaceutical companies is subject not only to factors arising from their drug pipelines (idiosyncratic risk), but also from general economic conditions (systematic risk). The risk associated with returns has profound implications both for patterns of investment and for funding innovation in biomedical R&D.

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.

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.

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.

There is general agreement in the biomedical community that the development of therapies for certain diseases should take priority. This ethic has motivated legislative initiatives, such as the Orphan Drug Act of 1983, and underpins several important innovations in regulatory approval processes, such as the US Food and Drug Administration’s (FDA) fast-track, breakthrough-therapy, accelerated-approval, and priority-review designations. However, none of these innovations directly address the critical issue of how to incorporate the patient’s perspective in deciding whether a drug candidate should be approved or not. The current approach in clinical trial design is to minimize the chance of ineffective treatment caused by a type 1 error, that is, a false-positive result. However, the arbitrary nature of the threshold for the probability of type 1 error, alpha, raises an ethical question about its justification. A 2.5% threshold may not be appropriate for terminal illnesses that have no effective therapies; such patients may prefer to take a bigger chance on a false-positive result, even if the likelihood of an effective therapy is small. To quote the noted biostatistician Donald Berry, “We should also focus on patient values, not just P values.”

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.

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.