This study provides an assessment of machine learning applied to the field of univariate financial time series analysis. Drawing on widely accepted ARIMA models, competing machine learning approaches are compared and various training scenarios and network architectures are discussed to identify the optimal network approach for daily return series forecasts. As a result of this exercise, plain vanilla recurrent neural networks with one layer describe a sensible choice.