Stock Valuation and Learning about Profitability

We develop a simple approach to valuing stocks in the presence of learning about average profitability. The market-to-book ratio (M/B) increases with uncertainty about average profitability, especially for firms that pay no dividends. M/B is predicted to decline over a firm's lifetime due to learning, with steeper decline when the firm is young. These predictions are confirmed empirically. Data also support the predictions that younger stocks and stocks that pay no dividends have more volatile returns. Firm profitability has become more volatile recently, helping explain the puzzling increase in average idiosyncratic return volatility observed over the past few decades.

Technological Revolutions and Stock Prices

We develop a general equilibrium model in which stock prices of innovative firms exhibit "bubbles" during technological revolutions. In the model, the average productivity of a new technology is uncertain and subject to learning. During technological revolutions, the nature of this uncertainty changes from idiosyncratic to systematic. The resulting "bubbles" in stock prices are observable ex post but unpredictable ex ante, and they are most pronounced for technologies characterized by high uncertainty and fast adoption. We find empirical support for the model’s predictions in 1830-1861 and 1992-2005 when the railroad and Internet technologies spread in the United States.

Was There a Nasdaq Bubble in the Late 1990s?

Not necessarily. The fundamental value of a firm increases with uncertainty about average future profitability, and this uncertainty was unusually high in the late 1990s. We calibrate a stock valuation model that includes this uncertainty, and show that the uncertainty needed to match the observed Nasdaq valuations at their peak is high but plausible. The high uncertainty might also explain the unusually high return volatility of Nasdaq stocks in the late 1990s. Uncertainty has the biggest effect on stock prices when the equity premium is low.

Political Uncertainty and Risk Premia

We develop a general equilibrium model of government policy choice in which stock prices respond to political news. The model implies that political uncertainty commands a risk premium whose magnitude is larger in weaker economic conditions. Political uncertainty reduces the value of the implicit put protection that the government provides to the market. It also makes stocks more volatile and more correlated, especially when the economy is weak. We find empirical evidence consistent with these predictions.

Stock Prices and IPO Waves

We develop a model of stock valuation and optimal IPO timing when investment opportunities are time-varying. IPO waves in our model are caused by declines in expected returns, increases in expected profitability, or increases in prior uncertainty about average profitability. The model predicts that IPO waves are preceded by high market returns, followed by low market returns, and accompanied by high stock prices. These as well as other predictions are supported empirically. Stock prices at the peak of the recent bubble', which was associated with an IPO wave, are consistent with plausible parameter values in our rational valuation model.

Evaluating and Investing in Equity Mutual Funds

Our framework for evaluating and investing in mutual funds combines observed returns on funds and passive assets with prior beliefs that distinguish pricing-model inaccuracy from managerial skill. A fund's alpha' is defined using passive benchmarks. We show that returns on non-benchmark passive assets help estimate that alpha more precisely for most funds. The resulting estimates generally vary less than standard estimates across alternative benchmark specifications. Optimal portfolios constructed from a large universe of equity funds can include actively managed funds even when managerial skill is precluded. The fund universe offers no close substitutes for the Fama-French and momentum benchmarks.

The Equity Premium and Structural Breaks

A long return history is useful in estimating the current equity premium even if the historical distribution has experienced structural breaks. The long series helps not only if the timing of breaks is uncertain but also if one believes that large shifts in the premium are unlikely or that the premium is associated, in part, with volatility. Our framework incorporates these features along with a belief that prices are likely to move opposite to contemporaneous shifts in the premium. The estimated premium since 1834 fluctuates between four and six percent and exhibits its sharpest drop in the last decade.

Comparing Asset Pricing Models: An Investment Perspective

We investigate the portfolio choices of mean-variance-optimizing investors who use sample evidence to update prior beliefs centered on either risk-based or characteristic-based pricing models. With dogmatic beliefs in such models and an unconstrained ratio of position size to capital, optimal portfolios can differ across models to economically significant degrees. The differences are substantially reduced by modest uncertainty about the models' pricing abilities. When the ratio of position size to capital is subject to realistic constraints, the differences in portfolios across models become even less important, nonexistent in some cases.

On the Size of the Active Management Industry

We argue that active management’s popularity is not puzzling despite the industry’s poor track record. Our explanation features decreasing returns to scale: As the industry’s size increases, every manager’s ability to outperform passive benchmarks declines. The poor track record occurred before the growth of indexing modestly reduced the share of active management to its current size. At this size, better performance is expected by investors who believe in decreasing returns to scale. Such beliefs persist because persistence in industry size causes learning about returns to scale to be slow. The industry should shrink only moderately if its underperformance continues.