Artificial Neural Network Enhanced With Bio-Inspired Optimization Algorithms for Predicting the Financial Stress in the Eurozone

Abstract

Traditional econometric models frequently fail to adequately reflect the ability to estimate future values of macroeconomic and financial variables, which is crucial for the implementation of macroeconomic policies. This study aims to tackle this problem by predicting the financial stress index (FSI) of the Eurozone for a sample period spanning from January 1995 to August 2023 using artificial neural networks (ANN) optimized by bio-inspired optimisation algorithms like the Invasive Weed Optimization (IWO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Cultural Algorithm (CA), and Artificial Bee Colony (ABC). The prediction error of the ANN is greatly reduced by the ANN optimized by FA (ANN-FA) model, which reduces it by roughly 97.57% for the calibration sample and 97.94% for the validation sample. The second-best model for FSI prediction in the Eurozone is the ANN optimized by PSO (ANN-PSO), which reduces the ANN's prediction error by 94.48% and 95.38% for the calibration and validation samples, respectively. The models are assessed for performance using accuracy metrics, and the metrics are visualized using chord, bump, and Taylor diagrams. These results imply that bio-inspired optimization can significantly enhance predictive reliability in monitoring financial stress conditions.