Disappearing Dividends: Implications for the Dividend–Price Ratio and Return Predictability

The conventional dividend–price ratio is highly persistent, and the literature reports mixed evidence on its role in predicting stock returns. We argue that the decreasing number of firms with a traditional dividend‐payout policy is responsible for these results, and develop a model in which the long‐run relationship between the dividends and stock price is time varying. An adjusted dividend–price ratio that accounts for the time‐varying long‐run relationship is considerably less persistent. Furthermore, the predictive regression model that employs the adjusted dividend–price ratio as a regressor outperforms the random‐walk model. These results are robust with respect to the firm size.     

Performance of French intensive care units: A directional distance function approach at the patient level

By using a novel adaptation of the free-disposable hull analysis of productivity, we assess the medical and technical efficiency of patient care in 25 Parisian intensive care units (ICUs) during 2000. The robust free disposable hull (RFDH) as defined by [Cazals et al., 2002. Nonparametric frontier estimation: a robust approach. Journal of Econometrics 106, 1–25] reduces the impact outliers may have on findings by employing Monte-Carlo techniques and repeated sample selection. Among our key findings, there was no overall significant correlation between medical and technical efficiencies for all the ICUs, therefore performing well in one does not guarantee good performance in the other. We also found that over 80% of inefficiency is concentrated in less than 20% of the sampled patients.     

Profiling high-frequency equity price movements in directional changes

Market prices are traditionally sampled in fixed time intervals to form time series. Directional change (DC) is an alternative approach to record price movements. Instead of sampling at fixed intervals, DC is data driven: price changes dictate when a price is recorded. DC provides us with a complementary way to extract information from data. It allows us to observe features that may not be recognized in time series. The argument is that time series and DC-based analysis complement each other. With data sampled at irregular time intervals in DC, however, some of the time series indicators cannot be used in DC-based analysis. For example, returns must be time adjusted and volatility must be amended accordingly. A major objective of this paper is to introduce indicators for profiling markets under DC. We analyse empirical high-frequency data on major equities traded on the UK stock market, and through DC profiling extract information complementary to features observed through time series profiling.     

An extension of the multi-output state-contingent production model

This paper presents new developments on the state-contingent theory of production under uncertainty with stochastic prices. Our main purpose is to generalize the usual finite discrete state-contingent production model to infinite dimensional, possibly uncountable spaces which look like a more realistic framework. Usual duality results are established in this general context, shedding some light on the links between risk-neutral probabilities and shadow prices. A direct generalized production risk premium is defined and is shown to be independent of the inputs level when the technology is output translation homothetic. In such a case, the technology exhibits constant absolute riskiness. We thank Bob Chambers for his helpful comments.