Detection of turning points is a critical task for financial forecasting applications. This chapter proposes a comparison between two different classification approaches on such a problem. Nature-Inspired methodologies are attracting growing interest due to their ability to cope with complex tasks like classification, forecasting, and anomaly detection problems. A swarm intelligence algorithm, namely Particle Swarm Optimization (PSO), and an artificial immune system algorithm, namely Negative Selection (NS), have been applied to the task of detecting turning points, modeled as an Anomaly Detection (AD) problem. Particular attention has also been given to the choice of the features considered as inputs to the classifiers, due to the significant impact they may have on the overall accuracy of the approach. In this work, starting from a set of eight input features, feature selection has been carried out by means of a greedy hill climbing algorithm, in order to analyze the incidence of feature reduction on the global accuracy of the approach. The performances obtained from the two approaches have also been compared to other traditional machine learning techniques implemented by WEKA and both methods have been found to give interesting results with respect to traditional techniques.