In this paper we apply nearest-neighbour local predictors, inspired by the
literature on forecasting in nonlinear systems, to the Nikkei 225 Index of
the Tokyo Stock Market for the period 1 January 1986-5 June 1997. When fore
casting performance is measured by Theil's U statistic, our nearest-neighbo
ur predictors perform worse than a random walk, outperforming the random wa
lk directional forecast. When formally testing for forecast accuracy, the r
esults suggest that predictions from a random walk were statistically signi
ficantly better than the nearest-neighbour predictors for the entire foreca
sting period, as well as for one of the subperiods (a 'bull' market episode
). Finally, when assessing the economic value of the nearest-neighbour pred
ictors in absence of trading costs, the results of using them as a filter t
echnique are superior to a buy-and-hold strategy for both the entire foreca
sting period acid for 'bear' market subperiods, where tests of 'forecast co
nditional efficiency' (or 'forecast encompassing') detected that the neares
t-neighbour predictors contain useful information for forecasting the Nikke
i Index that is not contained in the random walk. (C) 1999 Elsevier Science
B.V. All rights reserved. JEL classification: C53; G15.