Biomimetic Shark Algorithms: Leveraging Natural Predator Strategies for Superior Market Performance and Advanced Accounting Techniques

Abstract

This research delves into the innovative development and application of biomimetic shark algorithms, inspired by the efficient hunting behaviors of sharks, to enhance trading systems in financial markets. By emulating these natural predator strategies, the algorithms aim to improve both adaptability and profitability in trading practices.The study employs advanced methodologies, including pattern recognition through Convolutional Neural Networks (CNNs), sentiment analysis via Natural Language Processing (NLP), and risk management utilizing Value at Risk (VaR) calculations. Key hypotheses tested include the notion that biomimetic algorithms outperform traditional trading methods, enhance prediction accuracy through sentiment analysis, and effectively manage risks using sophisticated statistical models.Validation of these hypotheses is conducted using Matlab, with data sourced from the Istanbul Stock Exchange (Borsa Istanbul) and Konya markets. Results reveal that the biomimetic shark algorithms significantly surpass conventional trading methods in profitability and adaptability, demonstrating an enhanced ability to predict market movements and manage financial risks.This research underscores the transformative potential of interdisciplinary approaches, merging insights from biology, neuroscience, and machine learning to revolutionize financial trading systems and accounting practices. By leveraging natural predator strategies, the study presents a compelling case for the adoption of biomimetic algorithms in finance, advocating for future research to focus on refining these algorithms, integrating real-time data, and broadening their application across various market environments. Ultimately, this work paves the way for innovative solutions in financial trading and accounting, with the potential to reshape industry standards and practices.