Modeling decision-making dynamics in financial management through biomechanical principles and bio-inspired analytical frameworks

This study presents a novel approach to financial decision-making by integrating biomechanical principles with neural network architectures. The research establishes a framework that models market dynamics using mechanical analogies, incorporating concepts such as market elasticity, stress-strain relationships, and dynamic equilibrium. A bio-inspired neural network architecture is developed to process market indicators and generate trading decisions, combining mechanical parameters with Machine Learning capabilities. The model was validated using market data from 2018–2023, with out-of-sample testing conducted during 2022–2023. Key findings demonstrate significant improvements over traditional approaches: The bio-inspired framework achieved a 73.2% overall decision accuracy rate, surpassing benchmark models by 6.8%. Performance was notably strong during low volatility periods (77.9% accuracy) and showed particular effectiveness in identifying stable market conditions requiring hold decisions (75.9% accuracy). Risk-adjusted returns analysis revealed a Sharpe ratio of 1.18, compared to 0.68 for traditional models and 0.57 for the S&P 500. The framework demonstrated superior downside protection with a maximum drawdown of −14.3% versus -18.9% for conventional approaches. Annual returns of 15.8% were achieved while maintaining lower volatility (12.4%) compared to traditional models (14.7%). Response time analysis showed a 46.2% improvement in reaction to sharp market movements, with an average response time of 4.2 min to significant price drops compared to 7.8 min for traditional models. The system demonstrated robust error recovery capabilities, with a 92.4% success rate in correcting false signals within 2.8 min. These results indicate that integrating biomechanical principles with neural networks provides a more robust and adaptive framework for financial decision-making, offering improved accuracy, risk management, and response capabilities compared to conventional approaches. The framework’s success suggests promising applications in automated trading systems and portfolio management.