Enhancing Financial Risk Prediction Through Echo State Networks and Differential Evolutionary Algorithms in the Digital Era

In the ever-evolving landscape of financial investment, the digital era has ushered in a new paradigm characterized by technological innovation and sustainability considerations. This research paper delves into the intersection of technology, sustainability, and financial risk prediction. With the rise of digital finance and automated investment mechanisms, including blockchain technology and social media-driven market sentiment analysis, discerning investors now focus on sustainability through environmental, social, and corporate governance (ESG) criteria. However, navigating this landscape is not without challenges, such as cybersecurity risks and privacy concerns. The paper addresses these issues by proposing a financial risk prediction model that leverages echo state networks (ESN) and differential evolutionary algorithms. By quantifying various risk indicators through data transformation and employing machine learning techniques, the model enhances the accuracy and robustness of risk identification. The research introduces an optimization methodology for multiple swarm differential planning algorithms, optimizing ESN networks for risk identification within financial investment data. Experimental results validate the efficacy of the proposed method, achieving accuracy levels near 90%. This study contributes valuable insights for the future of intelligent finance by demonstrating the superiority of the MPDE-ESN model in risk recognition. Future research directions include expanding the model’s generalization performance, addressing diverse financial risks, and integrating reinforcement learning for dynamic risk determination. Additionally, optimizing feature dimensions and identifying optimal features remain key areas of investigation in this digital age of financial innovation and sustainability.