Revisions in successive Greenbook forecasts of quarterly real GDP growth proxy for news of current and expected future economic growth. In the sample 1975 through 2015, news of future growth is slightly negatively related to contemporaneous changes in Treasury bond yields, while news of current growth is strongly positively related to changes in these yields. Both results are difficult to reconcile with a representative agent's bondholding first‐order condition. A continuous‐time dynamic model of output attributes almost all of the covariation with yields to martingale innovations in log output and a minimal amount to innovations in the conditional drift of log output.

This paper uses surveys to document CFO perspectives on corporate planning, investment, capital structure, payout, and shareholder versus stakeholder focus. Comparing policy decisions today to those 20 years ago, I find that companies employ decision rules that are conservative, sticky, and geared to time the market; rely on internal forecasts that are miscalibrated and considered reliable only two years ahead; and emphasize corporate objectives that focus increasingly on stakeholders and revenues. These practice of corporate finance themes can discipline academic models toward better explaining outcomes. Models of satisficing decision‐making or costly managerial biases align with many of the themes.

Shocks to nominal bond yields consist of news about expected future inflation, expected future real short rates, and expected excess returns—all over the bond's life. I estimate the magnitude of the first component for short‐ and long‐maturity Treasury bonds. At a quarterly frequency, variances of news about expected inflation account for between 10% to 20% of variances of yield shocks. Standard dynamic models with long‐run risk imply variance ratios close to 1. Habit formation models fare somewhat better. The magnitudes of shocks to real rates and expected excess returns cannot be determined reliably.

I compare the fees, expenses, and trading costs society pays to invest in the U.S. stock market with an estimate of what would be paid if everyone invested passively. Averaging over 1980–2006, I find investors spend 0.67% of the aggregate value of the market each year searching for superior returns. Society's capitalized cost of price discovery is at least 10% of the current market cap. Under reasonable assumptions, the typical investor would increase his average annual return by 67 basis points over the 1980–2006 period if he switched to a passive market portfolio.

The conditional covariance between aggregate stock returns and aggregate consumption growth varies substantially over time. When stock market wealth is high relative to consumption, both the conditional covariance and correlation are high. This pattern is consistent with the “composition effect,” where agents' consumption growth is more closely tied to stock returns when stock wealth is a larger share of total wealth. This variation can be used to test asset‐pricing models in which the price of consumption risk varies. After accounting for variations in this price, the relation between expected excess stock returns and the conditional covariance is negative.

The standard class of affine models produces poor forecasts of future Treasury yields. Better forecasts are generated by assuming that yields follow random walks. The failure of these models is driven by one of their key features: Compensation for risk is a multiple of the variance of the risk. Thus risk compensation cannot vary independently of interest rate volatility. I also describe a broader class of models. These aessentially affine‐ models retain the tractability of standard models, but allow compensation for interest rate risk to vary independently of interest rate volatility. This additional flexibility proves useful in forecasting future yields.

The sources of trading friction are studied, and simple, robust empirical measures of friction are provided. Seven distinct measures of trading friction are computed from transactions data for 1,706 NYSE/AMSE stocks and 2,184 Nasdaq stocks. The measures provide insights into the magnitude of trading costs, the importance of informational versus real frictions, and the role of market structure. The degree to which the various measures are associated with each other and with trading characteristics of stocks is examined.

A model is developed which implies that if an analyst has high reputation or low ability, or if there is strong public information that is inconsistent with the analyst's private information, she is likely to herd. Herding is also common when informative private signals are positively correlated across analysts. The model is tested using data from analysts who publish investment newsletters. Consistent with the model's implications, the empirical results indicate that a newsletter analyst is likely to herd on Value Line's recommendation if her reputation is high, if her ability is low, or if signal correlation is high.

A model is developed which implies that if an analyst has high reputation or low ability, or if there is strong public information that is inconsistent with the analyst's private information, she is likely to herd. Herding is also common when informative private signals are positively correlated across analysts. The model is tested using data from analysts who publish investment newsletters. Consistent with the model's implications, the empirical results indicate that a newsletter analyst is likely to herd on Value Line's recommendation if her reputation is high, if her ability is low, or if signal correlation is high.

In this paper, I set up scenarios where the mean‐variance capital asset pricing model is true and where it is false. Then I investigate whether the coefficients from regressions of population expected excess returns on population betas, and expected excess returns on betas and size, allow us to distinguish between the scenarios. I show that the coefficients from either ordinary least squares or generalized least squares regressions do not allow us to tell whether the model is true or false.