We investigate, in a theoretical framework, the joint role played by investors' attention to news and learning uncertainty in determining asset prices. The model provides two main predictions. First, stock return variance and risk premia increase with both attention and uncertainty. Second, this increasing relationship is quadratic. We empirically test these two predictions, and we show that the data lend support to the increasing relationship. The evidence for a quadratic relationship is mixed. Overall, our study shows theoretically and empirically that both attention and uncertainty are key determinants of asset prices.

We propose a joint theory of time-series momentum and reversal based on a rational-expectations model. We show that a necessary condition for momentum to arise in this framework is that information flows at an increasing rate. We focus on word-of-mouth communication as a mechanism that enforces this condition and generates short-term momentum and long-term reversal. Investors with heterogeneous trading strategies—contrarian and momentum traders—coexist in the marketplace. Although a significant proportion of investors are momentum traders, momentum is not completely eliminated. Word-of-mouth communication spreads rumors and generates price run-ups and reversals. Our theoretical predictions are in line with empirical findings.

We provide a novel explanation for the empirical failure of the capital asset pricing model (CAPM) despite its widespread practical use. In a rational-expectations economy in which information is dispersed, variation in expected returns over time and across investors creates an informational gap between investors and the empiricist. The CAPM holds for investors, but the securities market line appears flat to the empiricist. Variation in expected returns across investors accounts for the larger part of this distortion, which is empirically substantial; it offers a new interpretation of why “betting against beta” (BAB) works: BAB really bets on true beta. The empiricist retrieves a stronger CAPM on days when public information reduces disagreement among investors.

We model how investor learning about monetary-policy transmission impacts asset prices. In an asset-pricing model, investors learn from realized inflation surprises how effectively monetary policy steers future inflation. Downward revisions in perceived effectiveness raise expected inflation persistence, increasing return volatility and risk premia. These effects intensify when policy deviates significantly from neutral or monetary-transmission uncertainty is high. We estimate the model using U.S. macro and policy data from 1954 to 2023. The resulting dynamics align with observed patterns in equity returns and volatility. Empirical tests support the model’s core prediction: investor learning turns central-bank credibility into a priced risk factor.

We investigate, in a theoretical framework, the joint role played by investors' attention to news and learning uncertainty in determining asset prices. The model provides two main predictions. First, stock return variance and risk premia increase with both attention and uncertainty. Second, this increasing relationship is quadratic. We empirically test these two predictions, and we show that the data lend support to the increasing relationship. The evidence for a quadratic relationship is mixed. Overall, our study shows theoretically and empirically that both attention and uncertainty are key determinants of asset prices.

We show that the relation between aggregate investment and Tobin’s q has become remarkably tight in recent years, contrasting with earlier times. We connect this change with the growing empirical dispersion in Tobin’s q, which we show both in the cross-section and the time series. To study the source of this dispersion, we augment a standard investment model with two distinct mechanisms related to firms’ research activities: innovations and learning. Both innovation jumps in cash flows and the frequent updating of beliefs about future cash flows endogenously amplify volatility in the firm’s value function. Perhaps counterintuitively, the investment-q regression works better for research-intensive industries, a growing segment of the economy, despite their greater stock of intangible assets. We confirm the model’s predictions in the data, and we disentangle the results from measurement error in q.

Persistence risk is an endogenous source of risk that arises when a rational agent learns about the length of business cycles. Persistence risk is positive during recessions and negative during expansions. This asymmetry, which solely results from learning about persistence, causes expected returns, return volatility, and the price of risk to rise during recessions. Persistence risk predicts future excess returns, particularly at 3- to 7-year horizons. Its predictability is strongest around business-cycle peaks and troughs. We confirm the model’s predictions in the data and provide evidence that persistence risk is priced in financial markets.Received October 13, 2017; editorial decision September 19, 2018 by Editor Stijn Van Nieuwerburgh.

This paper develops a multi-firm equilibrium model of information acquisition based on differences in firms’ characteristics. The model shows that heightened economic uncertainty amplifies stock price reactions to earnings announcements via increased investor attention, which varies by firm characteristics. Firms with higher systematic risk or more informative announcements attract more attention and exhibit stronger reactions to earnings announcements. Moreover, heightened investor attention caused by high economic uncertainty leads to a steeper CAPM relation and higher betas for announcing firms. Empirical analyses using firm-level attention measures and CAPM tests on high- versus low-attention days support the model’s predictions.