Autocorrelation in an unobservable global trend: does it help to forecast market returns?. (2017) - Peresetsky, Anatoly A.; Yakubov, Ruslan I.
In this paper, a Kalman filter-type model is used to extract a global stochastic trend from discrete non-synchronous data on daily stock market index returns from different markets. The model allows for the autocorrelation in the global stochastic trend, which means that its increments are predictable. It does not necessarily mean the predictability of market returns, since the global trend is unobservable. The performance of the model for the forecast of market returns is explored for three markets: Japan, UK, USA.