This study examines stock return predictability via lagged financial variables with unknown stochastic properties. We propose a novel testing procedure that (1) robustifies inference to regressors’ degree of persistence, (2) accommodates testing the joint predictive ability of financial variables in multiple regression, (3) is easy to implement as it is based on a linear estimation procedure, and (4) can be used for long-horizon predictability tests. We provide some evidence in favor of short-horizon predictability during the 1927-2012 period. Nevertheless, this evidence almost entirely disappears in the post–1952 period. Moreover, predictability becomes weaker, not stronger, as the predictive horizon increases
The thesis consists of three chapters dealing with predictability in equity markets. The first chapt...
Predictive regressions are a widely used econometric environment for assessing the predictability of...
Two major conclusions follow from this very careful study. First, sophisticated prediction tools do ...
This study examines stock return predictability via lagged financial variables with unknown stochast...
This paper re-examines stock returns predictability over the business cycle using price-dividend and...
Predictive regressions are linear specifications linking a noisy variable such as stock returns to p...
Predictive regressions are linear specifications linking a noisy variable such as stock returns to p...
Predictive regressions are linear specifications linking a noisy variable such as stock returns to p...
Conventional tests of the predictability of stock returns could be invalid, that is reject the null ...
We propose new real-time monitoring procedures for the emergence of end-of-sample predictive regimes...
We re-examine predictability of US stock returns. Theoretically well-founded models predict that sta...
Financial theory and econometric methodology both struggle in formulating models that are logically ...
We examine whether the stock market return is predictable from a range of financial indicators and m...
This paper evaluates the predictability of monthly stock return using out-of-sample (multi-step ahea...
The thesis consists of three chapters dealing with predictability in equity markets. The first chapt...
The thesis consists of three chapters dealing with predictability in equity markets. The first chapt...
Predictive regressions are a widely used econometric environment for assessing the predictability of...
Two major conclusions follow from this very careful study. First, sophisticated prediction tools do ...
This study examines stock return predictability via lagged financial variables with unknown stochast...
This paper re-examines stock returns predictability over the business cycle using price-dividend and...
Predictive regressions are linear specifications linking a noisy variable such as stock returns to p...
Predictive regressions are linear specifications linking a noisy variable such as stock returns to p...
Predictive regressions are linear specifications linking a noisy variable such as stock returns to p...
Conventional tests of the predictability of stock returns could be invalid, that is reject the null ...
We propose new real-time monitoring procedures for the emergence of end-of-sample predictive regimes...
We re-examine predictability of US stock returns. Theoretically well-founded models predict that sta...
Financial theory and econometric methodology both struggle in formulating models that are logically ...
We examine whether the stock market return is predictable from a range of financial indicators and m...
This paper evaluates the predictability of monthly stock return using out-of-sample (multi-step ahea...
The thesis consists of three chapters dealing with predictability in equity markets. The first chapt...
The thesis consists of three chapters dealing with predictability in equity markets. The first chapt...
Predictive regressions are a widely used econometric environment for assessing the predictability of...
Two major conclusions follow from this very careful study. First, sophisticated prediction tools do ...