A Study of Nature-Inspired Methods for Financial Trend Reversal Detection

This paper presents an application of two nature-inspired algorithms to the financial problem concerning the detection of turning points. Nature-Inspired methods are receiving a growing interest due to their ability to cope with complex tasks like classification, forecasting and anomaly detection problems. A swarm intelligence algorithm, Particle Swarm Optimization (PSO), and an artificial immune system one, the Negative Selection (NS), are applied to the problem of detection of turning points, modeled as an Anomaly Detection (AD) problem, and their performances are compared. Both methods are found to give interesting results with respect to an unpredictable behavior.

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