A Comparative Study on Stock Market Prediction Using Artificial Intelligence and Human Intelligence

Abstract The stock market’s dynamic nature has long attracted both experienced investors and newcomers, offering opportunities for significant gains alongside the risk of rapid losses. Traditionally, market predictions have relied on human expertise, where investors use knowledge, intuition, and awareness of economic and political trends to guide decisions. While effective in many cases, this approach is constrained by emotional bias and limited data-processing capabilities. Recent advances in Artificial Intelligence (AI), particularly in machine learning, have transformed market analysis by enabling the processing of vast datasets and the detection of patterns that may escape human observation. AI models, such as neural networks and decision trees, continuously refine their predictions without fatigue or distraction, providing data-driven insights with speed and precision. This study examines the comparative strengths and weaknesses of AI-driven forecasting and human expert analysis under similar conditions. Rather than seeking to establish superiority, the research aims to explore how human judgment and AI capabilities can work together to enhance accuracy and adaptability in financial forecasting. Keywords: Artificial intelligence, Human intelligence, Machine learning, Neural networks, Decision trees, Sentiment analysis, Financial forecasting, Hybrid prediction model, Market trends, Trading strategy,