Modeling decision-making dynamics in financial management through biomechanical principles and bio-inspired analytical frameworks
Abstract
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.
References
1. Pandey, A., & Mishra, A. (2024). Application of Artificial Intelligence in Sports Analytics: Analysing the Ethical and Legal Perspectives. In Sports Analytics: Data-Driven Sports and Decision Intelligence (pp. 163-184). Cham: Springer Nature Switzerland.
2. Chen, W., Milosevic, Z., Rabhi, F. A., & Berry, A. (2023). Real-time analytics: Concepts, architectures and ML/AI considerations. IEEE Access.
3. So, M. K., Chu, A. M., Lo, C. C., & Ip, C. Y. (2022). Volatility and dynamic dependence modeling: Review, applications, and financial risk management. Wiley Interdisciplinary Reviews: Computational Statistics, 14(5), e1567.
4. Fan, X., Tao, C., & Zhao, J. (2024). Advanced stock price prediction with xlstm-based models: Improving long-term forecasting.
5. Ade, M. (2023). Forecasting Volatility: Comparative Analysis of ARIMA, GARCH, and Deep Learning Models for Predicting Stock Market Volatility.
6. Zheng, H., Wu, J., Song, R., Guo, L., & Xu, Z. (2024). Predicting financial enterprise stocks and economic data trends using machine learning time series analysis.
7. Nahar, J., Rahaman, M. A., Alauddin, M., & Rozony, F. Z. (2024). Big Data in Credit Risk Management: A Systematic Review Of Transformative Practices And Future Directions. International Journal of Management Information Systems and Data Science, 1(04), 68-79.
8. Nahar, J., Hossain, M. S., Rahman, M. M., & Hossain, M. A. (2024). Advanced Predictive Analytics For Comprehensive Risk Assessment In Financial Markets: Strategic Applications And Sector-Wide Implications. Global Mainstream Journal of Business, Economics, Development & Project Management, 3(4), 39-53.
9. Qin, J., Cong, X., Ma, D., & Rong, X. (2024). Dynamic quantile connectedness between oil and stock markets: The impact of the interest rate. Energy Economics, 136, 107741.
10. Osterrieder, J., Arakelian, V., Coita, I. F., Hadji-Misheva, B., Kabasinskas, A., Machado, M., & Mare, C. (2023). An Overview-stress test designs for the evaluation of AI and ML Models under shifting financial conditions to improve the robustness of models. Available at SSRN 4634266.
11. Indumathi N et al., Impact of Fireworks Industry Safety Measures and Prevention Management System on Human Error Mitigation Using a Machine Learning Approach, Sensors, 2023, 23 (9), 4365; DOI:10.3390/s23094365.
12. Parkavi K et al., Effective Scheduling of Multi-Load Automated Guided Vehicle in Spinning Mill: A Case Study, IEEE Access, 2023, DOI:10.1109/ACCESS.2023.3236843.
13. Ran Q et al., English language teaching based on big data analytics in augmentative and alternative communication system, Springer-International Journal of Speech Technology, 2022, DOI:10.1007/s10772-022-09960-1.
14. Ngangbam PS et al., Investigation on characteristics of Monte Carlo model of single electron transistor using Orthodox Theory, Elsevier, Sustainable Energy Technologies and Assessments, Vol. 48, 2021, 101601, DOI:10.1016/j.seta.2021.101601.
15. Huidan Huang et al., Emotional intelligence for board capital on technological innovation performance of high-tech enterprises, Elsevier, Aggression and Violent Behavior, 2021, 101633, DOI:10.1016/j.avb.2021.101633.
16. Sudhakar S, et al., Cost-effective and efficient 3D human model creation and re-identification application for human digital twins, Multimedia Tools and Applications, 2021. DOI:10.1007/s11042-021-10842-y.
17. Prabhakaran N et al., Novel Collision Detection and Avoidance System for Mid-vehicle Using Offset-Based Curvilinear Motion. Wireless Personal Communication, 2021. DOI:10.1007/s11277-021-08333-2.
18. Sengan Sudhakar and S. Chenthur Pandian, (2016), ‘Hybrid Cluster-based Geographical Routing Protocol to Mitigate Malicious Nodes in Mobile Ad Hoc Network, InderScience-International Journal of Ad Hoc and Ubiquitous Computing, vol. 21, no. 4, pp. 224-236. DOI:10.1504/IJAHUC.2016.076358.
19. Balajee A et al., Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest, J Ambient Intell Human Comput, 2021, DOI:10.1007/s12652-020-02869-0.
20. Omnia SN et al., An educational tool for enhanced mobile e-Learning for technical higher education using mobile devices for augmented reality, Microprocessors, and Microsystems, 83, 2021, 104030, DOI:10.1016/j.micpro.2021.104030.
21. Firas TA et al., Strategizing Low-Carbon Urban Planning through Environmental Impact Assessment by Artificial Intelligence-Driven Carbon Foot Print Forecasting, Journal of Machine and Computing, 4(4), 2024, doi: 10.53759/7669/jmc202404105.
22. Shaymaa HN, et al., Genetic Algorithms for Optimized Selection of Biodegradable Polymers in Sustainable Manufacturing Processes, Journal of Machine and Computing, 4(3), 563-574, https://doi.org/10.53759/7669/jmc202404054.
23. Hayder MAG et al., An open-source MP + CNN + BiLSTM model-based hybrid model for recognizing sign language on smartphones. Int J Syst Assur Eng Manag (2024). https://doi.org/10.1007/s13198-024-02376-x
24. Bhavana Raj K et al., Equipment Planning for an Automated Production Line Using a Cloud System, Innovations in Computer Science and Engineering. ICICSE 2022. Lecture Notes in Networks and Systems, 565, 707–717, Springer, Singapore. DOI:10.1007/978-981-19-7455-7_57.
25. Basu, R., Lim, W. M., Kumar, A., & Kumar, S. (2023). Marketing analytics: The bridge between customer psychology and marketing decision‐making. Psychology & Marketing, 40(12), 2588-2611.
26. Mandal, A. K., Sarma, P. K. D., Dehuri, S., & Mazumdar, N. (2024). Bioinspired Machine Learning: A New Era in Sustainable Energy Systems. Biomass and Solar‐Powered Sustainable Digital Cities, 57-80.
27. Petitjean, N., Canadas, P., Royer, P., Noël, D., & Le Floc'h, S. (2023). Cartilage biomechanics: From the basic facts to the challenges of tissue engineering. Journal of Biomedical Materials Research Part A, 111(7), 1067-1089.
28. Aftab, J., Abid, N., Sarwar, H., Amin, A., Abedini, M., & Veneziani, M. (2024). Does corporate social responsibility drive financial performance? Exploring the significance of green innovation, green dynamic capabilities, and perceived environmental volatility. Corporate Social Responsibility and Environmental Management, 31(3), 1634-1653.
29. Thompson, A. J., Orué, A. I. C., Nair, A. J., Price, J. R., McMurtrie, J., & Clegg, J. K. (2021). Elastically flexible molecular crystals. Chemical Society Reviews, 50(21), 11725-11740.
30. Nenchev, D. N., & Iizuka, R. (2021). Emergent humanoid robot motion synergies derived from the momentum equilibrium principle and the distribution of momentum. IEEE Transactions on Robotics, 38(1), 536-555.
31. Xu, Z., Lu, J., Lu, D., Li, Y., Lei, H., Chen, B., ... & Wang, W. (2024). Rapidly damping hydrogels engineered through molecular friction. Nature Communications, 15(1), 4895.
32. Sterke, B., Jabeen, S., Baines, P., Vallery, H., Ribbers, G., & Heijenbrok-Kal, M. (2024). Direct biomechanical manipulation of human gait stability: A systematic review. Plos one, 19(7), e0305564.
33. Purwanto, B. (2024). Optimal portfolio and the integrated strategy. Management Analysis Journal, 13(2), 130-139
34. Brunel, J. L., Idzorek, C. T. M., & Mulvey, C. J. M. (2020). Principles of asset allocation. Portfolio Management in Practice, Volume 1: Investment Management, 1, 211.
Copyright (c) 2024 Jun Ye
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright on all articles published in this journal is retained by the author(s), while the author(s) grant the publisher as the original publisher to publish the article.
Articles published in this journal are licensed under a Creative Commons Attribution 4.0 International, which means they can be shared, adapted and distributed provided that the original published version is cited.
Submit a Paper
