In general, times series forecasting is considered as a highly complex problem, which is particularly true for financial time series. In this paper, a fuzzy model evolved through a bio-inspired algorithm is proposed to produce accurate models for the prediction of these time series. The performance of this model is compared to that of a group of state-of-the-art statistical models. A thorough experimental study is designed and carry out in order to assess the merits of the proposal. The experimental results allow us to state that our proposal forecasts consistently outperform the other considered methods.