Knowledge that Transforms

Changes in stock prices have substantial explanatory power for U.S. investment, especially for long-term samples, and even in the presence of cash flow variables. The stock market dramatically outperforms a standard q-variable because the market-equity component of this variable is only a rough proxy for stock market value. Although the stock market did not predict accurately after the crash of October 1987, the errors were not statistically significant. Parallel relationships for Canada raise the puzzle that Canadian investment appears to react more to the U.S. stock market than to the Canadian market.

Stambaugh, and especially to Robert Merton for comments on a previous draft. This paper is part of NBER's research program in Financial Markets and Monetary Economics. Any opinions expressed are those of the author not those

This article analyzes the behavior of stock return volatility using daily data from 1885 through 1988. The October 1987 stock market crash was unusual in many ways. October 19 was the largest percentage change in market value in over 29,000 days. Stock volatility jumped dramatically during and after the crash. Nevertheless, it returned to lower, more normal levels more quickly than past experience predicted. I use data on implied volatilities from call option prices and estimates of volatility from futures contracts on stock indexes to confirm this result.

We investigate product-market competition when managers maximize shareholder value rather than their expected discounted value of profits. If shareholders are imperfectly informed about future profitability, shareholder-value maximization can lead to either more or less aggressive product-market strategies. Lower rivals’ profits lead investors to believe that the firm’s costs are low relative to those of its rivals and that the industry’s prospects are poor. If the former (latter) inference dominates, each firm tries to lower (raise) its rivals’ profits to increase its own stock price. We also consider implications for corporate financial structure.

This article generalizes the Cox, Ross, and Rubinstein (1979) binomial option-pricing model, and establishes a convergence from discrete-time multivariate multinomial models to continuous-time multidimensional diffusion models for contingent claims prices. The key to the approach is to approximate the N-dimensional diffusion price process by a sequence of N-variate, (N+1)-nomial processes. It is shown that contingent claims prices and dynamic replicating portfolio strategies derived from the discrete time models converge to their corresponding continuous-time limits.

We find that conditional means and variances of consumption growth vary through time, and this variation appears to be associated with the business cycle. A pricing model with fluctuating means and variances of consumption growth provides implications about conditional moments of returns for both short and long investment horizons, and these implications are explored empirically. The U-shaped pattern of first-order autocorrelations of returns, as well as business cycle patterns in the price of risk, appears to be consistent with the model, but our exploration suggests that other implications about conditional return moments are at odds with the data.