Goyal and Welch (2006) argue that the historical average excess stock return forecasts future excess stock returns better than regressions of excess returns on predictor variables. In this paper, we show that many predictive regressions beat the historical average return, once weak restrictions are imposed on the signs of coe!cients and return forecasts. The out-of-sample explanatory power is small, but nonetheless is economically meaningful for mean-variance investors. Even better results can be obtained by imposing the restrictions of steady-state valuation models, thereby removing the need to estimate the average from a short sample of volatile stock returns.

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A number of variables are correlated with subsequent returns on the aggregate US stock market in the 20th Century. Some of these variables are stock market valuation ratios, others reflect patterns in corporate finance or the levels of shortand long-term interest rates. Amit Goyal and Ivo Welch (2004) have argued that in-sample correlations conceal a systematic failure of these variables out of sample: None are able to beat a simple forecast based on the historical average stock return. In this note we show that forecasting variables with significant forecasting power insample generally have a better out-of-sample performance than a forecast based on the historical average return, once sensible restrictions are imposed on the signs of coefficients and return forecasts. The out-of-sample predictive power is small, but we find that it is economically meaningful. We also show that a variable is quite likely to have poor out-of-sample performance for an extended period of time even when the variable genuinely predicts returns with a stable coefficient.

for useful comments. We are grateful to Ken French and Robert Shiller for providing us with some of the data used in this study. All errors and omissions remain our responsibility.

A number of variables are correlated with subsequent returns on the aggregate US stock market in the 20th Century. Some of these variables are stock market valuation ratios, others reflect patterns in corporate finance or the levels of shortand long-term interest rates. Amit Goyal and Ivo Welch (2004) have argued that in-sample correlations conceal a systematic failure of these variables out of sample: None are able to beat a simple forecast based on the historical average stock return. In this note we show that forecasting variables with significant forecasting power insample generally have a better out-of-sample performance than a forecast based on the historical average return, once sensible restrictions are imposed on the signs of coefficients and return forecasts. The out-of-sample predictive power is small, but we find that it is economically meaningful. We also show that a variable is quite likely to have poor out-of-sample performance for an extended period of time even when the variable genuinely predicts returns with a stable coefficient.

High levels of impulsivity and stereotypy were significant predictors of SIB in a large and diverse sample of people with confirmed autism diagnoses. Future research is needed on the effects of reducing impulsivity and stereotypy on the outcomes of treatment, early intervention and attempts to prevent the development of SIB.

We implement a multifrequency volatility decomposition of three exchange rates and show that components with similar durations are strongly correlated across series. This motivates a bivariate extension of the Markov-Switching Multifractal (MSM) introduced in Calvet and Fisher (J. Econ. 105 (2001) 27, J. Financ. Econ. 2 (2004) 49). Bivariate MSM is a stochastic volatility model with a closed-form likelihood. Estimation can proceed by maximum likelihood for state spaces of moderate size, and by simulated likelihood via a particle filter in high-dimensional cases. We estimate the model and confirm its main assumptions in likelihood ratio tests. Bivariate MSM compares favorably to a standard multivariate GARCH both in-and out-of-sample. A parsimonious multifrequency factor structure is finally proposed for multivariate settings with potentially many assets. r