INCORPORATING EMOTIONAL INTELLIGENCE IN SHARK ALGORITHMS: BOOSTING TRADING SUCCESS WITH AFFECTIVE AI

Abstract

The paper investigates the incorporation of emotional AI into Shark Algorithms to enhance trading performance through affective computing. By integrating sentiment analysis, these advanced algorithms are equipped with emotional intelligence to improve the accuracy of price predictions, devise sentiment-driven trading strategies, and optimize risk management techniques. The research highlights that embedding sentiment scores into conventional trading models provides a more nuanced understanding of market behavior, leading to enhanced decision-making processes.Key findings demonstrate that sentiment-enhanced price prediction models outperform traditional methods in capturing market trends, offering a significant edge in forecasting accuracy. The study further reveals the profitability of trading strategies driven by sentiment analysis, as they capitalize on emotional market responses, which are often overlooked by purely quantitative approaches. Additionally, the research emphasizes the effectiveness of sentiment-based position sizing as a tool for risk mitigation, allowing traders to adjust their exposure based on the emotional state of the market, thereby reducing potential losses during periods of heightened volatility.However, the integration of emotional AI into trading algorithms is not without challenges. The paper identifies issues related to the quality and reliability of sentiment data, which can vary widely depending on the source and the methodology used for analysis. Ethical considerations also arise, particularly concerning the potential for market manipulation and the broader impact of emotionally driven trading on financial markets. Technical complexities, such as the integration of real-time sentiment analysis with high-frequency trading systems, present additional hurdles.The study concludes by proposing future research directions, including the development of more sophisticated sentiment analysis techniques that can better capture the nuances of market sentiment. It also suggests advancements in real-time data processing to enhance the responsiveness of trading algorithms. Improved risk management strategies are recommended to address the specific risks associated with sentiment-driven trading. Furthermore, the paper advocates for the establishment of ethical frameworks to guide the use of emotional AI in financial markets, along with long-term studies to assess its impact on market efficiency and stability. The integration of emotional AI into trading algorithms represents a transformative step in financial technology, with the potential to influence market dynamics and trading practices significantly.