Knowledge that Transforms

Derivatives valuation and risk management involve heavy use of quantitative models. To develop a quantitative assessment of model risk as it affects the basic option writing strategy that might be followed by a financial institution, we conduct an empirical simulation, with and without hedging, using data from 1976 to 1996. Results indicate that imperfect models and inaccurate volatility forecasts create sizable risk exposure for option writers. We consider to what extent the damage due to model risk can be limited by pricing options using a higher volatility than the best estimate from historical data.

Firm investment decisions are shown to be directly related to financial factors. Investment decisions of firms with high creditworthiness (according to traditional financial ratios) are extremely sensitive to the availability of internal funds; less creditworthy firms are much less sensitive to internal fund availability. This large sample evidence is based on an objective sorting mechanism and supports the results of Kaplan and Zingales (1997), who also find that investment outlays of the least constrained firms are the most sensitive to internal cash flow.

A rational analysis of analyst behavior predicts that analysts immediately and without bias incorporate information into their forecasts. Several studies document analysts' tendency to systematically underreact to information. Underreaction is inconsistent with rationality. Other studies indicate that analysts systematically overreact to new information or that they are systematically optimistic. This study discriminates between these three hypotheses by examining the interaction between the nature of information and the type of reaction by analysts. The evidence indicates that analysts underreact to negative information, but overreact to positive information. These results are consistent with systematic optimism in response to information.

In this paper we utilize White's Reality Check bootstrap methodology (White (1999)) to evaluate simple technical trading rules while quantifying the data‐snooping bias and fully adjusting for its effect in the context of the full universe from which the trading rules were drawn. Hence, for the first time, the paper presents a comprehensive test of performance across all technical trading rules examined. We consider the study of Brock, Lakonishok, and LeBaron (1992), expand their universe of 26 trading rules, apply the rules to 100 years of daily data on the Dow Jones Industrial Average, and determine the effects of data‐snooping.

We model the time‐series relation between price and intrinsic value as a cointegrated system, so that price and value are long‐term convergent. In this framework, we compare the performance of alternative estimates of intrinsic value for the Dow 30 stocks. During 1963–1996, traditional market multiples (e.g., B/P, E/P, and D/P ratios) have little predictive power. However, a V/P ratio, where V is based on a residual income valuation model, has statistically reliable predictive power. Further analysis shows time‐varying interest rates and analyst forecasts are important to the success of V. Alternative forecast horizons and risk premia are less important.

As a consequence of optimal investment choices, a firm's assets and growth options change in predictable ways. Using a dynamic model, we show that this imparts predictability to changes in a firm's systematic risk, and its expected return. Simulations show that the model simultaneously reproduces: (i) the time‐series relation between the book‐to‐market ratio and asset returns; (ii) the cross‐sectional relation between book‐to‐market, market value, and return; (iii) contrarian effects at short horizons; (iv) momentum effects at longer horizons; and (v) the inverse relation between interest rates and the market risk premium.

This paper presents an exact finite‐sample statistical procedure for testing hypotheses about the weights of mean‐variance efficient portfolios. The estimation and inference procedures on efficient portfolio weights are performed in the same way as for the coefficients in an OLS regression. OLS t‐ and F‐statistics can be used for tests on efficient weights, and when returns are multivariate normal, these statistics have exact t and F distributions in a finite sample. Using 20 years of data on 11 country stock indexes, we find that the sampling error in estimates of the weights of a global efficient portfolio is large.

We document strong positive correlation between changes in institutional ownership and returns measured over the same period. The result suggests that either institutional investors positive‐feedback trade more than individual investors or institutional herding impacts prices more than herding by individual investors. We find evidence that both factors play a role in explaining the relation. We find no evidence, however, of return mean‐reversion in the year following large changes in institutional ownership—stocks institutional investors purchase subsequently outperform those they sell. Moreover, institutional herding is positively correlated with lag returns and appears to be related to stock return momentum.