AI Prediction of Stock Market Trends: An Overview for Non-Technical Researchers

The ability to forecast stock market patterns has emerged as an alluring use of artificial intelligence (AI) and machine learning, thanks to the field’s fast breakthroughs in both fields. For non-technical researchers, this publication offers a thorough and understandable introduction of AI-driven stock market forecast methods. The aim is to demystify the difficulties associated with AI-based predictions and provide non-experts a basic comprehension of the approaches used. The study examines the numerous data sources utilized in the setting of AI-driven stock market forecasting, including historical stock prices, financial statements, market sentiment, and macroeconomic indicators. To make it easier to prepare data for AI algorithms, data pretreatment and feature engineering approaches are discussed in a non-technical way. Support Vector Machines, Random Forests, and Deep Neural Networks—three important AI models used in stock market prediction—are introduced with an emphasis on comprehending their high-level operation. The review also covers difficulties and restrictions related to AI predictions, such as poor data quality and model overfitting. Algorithmic biases, market manipulation, and responsible AI usage in finance are further ethical issues that are covered. The study provides a concise summary of key findings for non-technical scholars, enabling them to understand the potential and constraints of AI in forecasting stock market developments. Non-technical researchers may make intelligent judgements, participate in conversations, and significantly progress this game-changing subject by gaining this fundamental information.