Investigating Neuroscience-Inspired Shark Algorithms: Mimicking Human Decision-Making in Trading Systems and Their Implications for Accounting, Risk Management, Technical Analysis, and the Stock Market - GANJI, Farshad (International Journal of Business Management and Entrepreneurship, 2025)
This research investigates the transformative potential of neuroscience-inspired shark algorithms in stock market trading, aiming to replicate human decision-making processes. By integrating advanced machine learning techniques—such as deep learning and natural language processing—with neuroscience principles, we develop adaptive and robust trading systems. The study specifically examines the implications of these algorithms for accounting, risk management, technical analysis, and the financial markets of Istanbul (Borsa Istanbul) and Konya.The research formulates hypotheses to compare the performance of these advanced algorithms against traditional trading models. Utilizing mathematical modeling and Matlab coding, we validate the theoretical concepts underlying these algorithms. Our findings indicate that incorporating neuroscience and machine learning principles significantly enhances trading performance and market prediction accuracy. The empirical analysis confirms that neuroscience-inspired algorithms outperform conventional algorithms in adaptability and profitability. Notably, the integration of sentiment analysis from natural language processing further enhances prediction accuracy by effectively capturing market sentiment. Techniques such as convolutional neural networks (CNNs) have proven particularly effective in identifying patterns within financial data, which are essential for forecasting future market movements. The implications for financial markets, especially in Istanbul and Konya, are substantial. Advanced trading algorithms are expected to improve market efficiency, attract international investors, and promote market stability. Additionally, these algorithms offer notable benefits for accounting and auditing practices by automating data processes and enhancing the accuracy of financial data analysis. In conclusion, this research highlights the significant advancements that neuroscience-inspired trading algorithms can bring to the financial sector. We recommend future studies focus on integrating real-world data, addressing ethical considerations, and conducting cross-market analyses to further explore the efficacy and application of these innovative algorithms. By leveraging insights from neuroscience and machine learning, the financial industry can achieve greater accuracy and efficiency in trading and risk management practices.