2013 Nobel Memorial Prize in Economic Sciences
Reason for Award
for their empirical analysis of asset prices
Laureates
United States of America
United States of America
United States of America
Explanation
The price of things like stocks and bonds, which you can think of as a grown-up version of trading cards, moves every day. Guessing tomorrow’s price is extremely hard, but scientists wondered whether the broad trend over five years might be somewhat predictable. Eugene Fama showed that prices jump almost instantly when news arrives, proving that markets absorb information very quickly. Lars Peter Hansen invented a new statistical tool to study complicated economic data and used it to test theories about risk and return. Robert Shiller discovered that over many years prices sometimes rise or fall too much, creating bubbles that later burst. Their work helps families, pension funds, and others understand investment risks. For example, we learned that buying the whole market in an index fund is often safer than trying to pick only a few lucky stocks. Thanks to them, people can manage their money more wisely for the future.
Related Keywords
Efficient Market Hypothesis
The Efficient Market Hypothesis (EMH) states that publicly available information is incorporated into asset prices virtually instantaneously. Documented by Fama’s 1960s studies of large price datasets, EMH explains why short-run returns are nearly unpredictable. It is divided into weak, semi-strong, and strong forms, each tested with autocorrelation measures or event studies. In a perfectly efficient market, arbitrage profits disappear over time because prices already reflect all known information. Nevertheless, bubbles and behavioral biases have prompted many partial modifications of the hypothesis. In practice EMH underpins index-fund investing even as debates about its limitations remain lively.
Generalized Method of Moments
The Generalized Method of Moments (GMM) estimates model parameters by minimizing the distance between theoretical and empirical moments implied by the data. Introduced by Hansen in 1982, it delivers asymptotically consistent estimates even when maximum-likelihood techniques are infeasible. GMM remains valid under serial correlation and conditional heteroskedasticity, features common in asset-pricing and macroeconomic datasets. Its over-identifying restrictions test allows researchers to judge statistically whether the proposed model fits the data. Extensions to continuous-time and panel settings have made GMM a universal empirical tool in economics. In finance, practitioners apply it to estimate risk premia and calibrate stress-test parameters.
Stock-return predictability
Stock-return predictability refers to the statistical ability of variables such as dividend yield or the CAPE ratio to forecast future average returns. Shiller’s 1980s long-horizon regressions provided early, influential evidence of such predictability. It may arise from time-varying risk premia, investor sentiment, or market frictions, making it a key testing ground for efficiency and behavioral theories. Academically it serves as central evidence in debates over the limits of market efficiency. Practitioners use predictive signals for strategic asset allocation and dynamic hedging, though model misspecification and out-of-sample stability remain concerns. Methodological refinements continue to improve robustness of return-forecasting models.
Asset price bubble
An asset price bubble occurs when prices soar far above intrinsic value and subsequently crash. Shiller’s work highlighted the roles of behavioral biases and social psychology in bubble formation, popularizing the term "irrational exuberance." Indicators for detecting bubbles include price-dividend gaps, accelerating price paths, and rising leverage. Bubble collapses often precipitate financial crises and real-economy slowdowns, making them a major risk factor for policymakers. Empirical studies show that extreme positive returns during bubbles followed by large negative corrections contribute substantially to long-run return predictability. Recent research employs machine learning and high-frequency data to identify bubble signals in real time.
Fama–French three-factor model
The Fama–French three-factor model adds size (SMB) and value (HML) factors to the market portfolio to explain the cross-section of stock returns. Proposed by Fama and French in the early 1990s, it organized the empirical finding that small-cap and value stocks earn higher average returns than CAPM predicts. Estimation typically employs portfolio sorting with Fama-MacBeth regressions or GMM. Subsequent work appended momentum, profitability, and investment factors, yielding five- and six-factor versions. In asset management the model underlies factor ETFs and smart-beta strategies. Debates continue about the persistence of factor returns, measurement error, and trading costs.
Consumption-based CAPM
The consumption-based CAPM (CCAPM) is a general-equilibrium model linking asset returns to fluctuations in a representative agent’s consumption. Risk premia depend on the covariance of returns with consumption growth through the stochastic discount factor m_{t+1}=β(C_{t+1}/C_t)^{-γ}. Hansen’s GMM made it possible to estimate CCAPM parameters and test over-identifying restrictions statistically. Empirically the standard CCAPM struggles to match the equity premium, prompting extensions such as habit formation and long-run risks. Nevertheless, CCAPM remains a foundational framework connecting finance to macroeconomic fundamentals. Similar preference structures are used in macro policy evaluation and welfare analysis.
Random Walk Hypothesis
The Random Walk Hypothesis posits that successive price changes are independently and identically distributed, offering the simplest representation of an efficient market. Fama employed autocorrelation statistics and variance-ratio tests to show that U.S. stock data broadly conform to this hypothesis. Under a random walk, no trading rule based solely on past prices can systematically earn excess returns. Accumulating evidence has revealed modest deviations, such as seasonality and short-term reversals, but these are typically small. The hypothesis underpins standard critiques of technical analysis. Contemporary research often models prices as near-random walks that allow weak dependence.
Case-Shiller Home Price Index
The Case-Shiller Home Price Index tracks U.S. residential real-estate prices using a repeat-sales methodology. Developed by Shiller and Case in the 1980s, it maintains quality consistency more effectively than hedonic adjustments. The index is widely employed to detect housing bubbles, value mortgage-backed securities, and underlie derivative products. During the 2000s U.S. housing boom and the subprime crisis, it served as a key indicator of price overheating. Shiller advocated futures contracts based on the index, envisioning markets that let households hedge real-estate risk. It is still published regularly as an official economic statistic and influences monetary policy and investment decisions.