Dancing with bulls and bears: Nearest-neighbour forecasts for the Nikkei index

Abstract

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 forecasting performance is measured by Theil's U statistic, our nearest-neighbour predictors perform worse than a random walk, outperforming the random walk directional forecast. When formally testing for forecast accuracy, the results suggest that predictions from a random walk were statistically significantly better than the nearest-neighbour predictors for the entire forecasting period, as well as for one of the subperiods (a `bull' market episode). Finally, when assessing the economic value of the nearest-neighbour predictors in absence of trading costs, the results of using them as a filter technique are superior to a buy-and-hold strategy for both the entire forecasting period and for `bear' market subperiods, where tests of `forecast conditional efficiency' (or `forecast encompassing') detected that the nearest-neighbour predictors contain useful information for forecasting the Nikkei Index that is not contained in the random walk.