A similarity-based classification model is proposed whereby densities of positive and negative returns in a delay-embedded input space are estimated from a graphical representation of the data using an eigenvector centrality measure, and subsequently combined under Bayes' theorem to predict the probability of upward/downward movements. Application to directional forecasting of the daily close price of the Dow Jones Industrial Average over a 20-year out-of-sample period yields performance superior to random walk and logistic regression models, and on a par with that of multilayer perceptrons. A feature of the classifier is that it is parameter free, parameters entering the model only via the measure used to determine pairwise similarity between data points. This allows intuitions about the nature of time series to be elegantly integrated into the model. The recursive nature of eigenvector centrality makes it better able to deal with sparsely populated input spaces than conventional approaches based on density estimation. Copyright © 2013 John Wiley & Sons, Ltd.