Stock Market Prediction Through AI: Analyzing Market Trends With Big Data Integration

Abstract

Stock market prediction has important financial implications and is an active area of research, industry, and academic studies. The impact of the large size of data - measured as type, velocity, volume, and variance (4V) - from multiple sources - structured, semi-structured, and unstructured data - puts mounting pressure on stock market participants to look for effective approaches to analyze the underlying data, to gain a competitive edge in stock investing.

To this end, various data mining algorithms and machine learning techniques are used to investigate stock market predictions and understand capital markets. Market indicators play a key role in stock returns and provide essential lessons for many investors, especially in adopting various conservative trading strategies. However, existing stock studies only address market trend analysis related to large-volume market datasets and extrapolating market trends through the classifier.

This study, "Stock Market Prediction [1]through Artificial Intelligence: Analyzing Market Trends with Big Data Integration," summarizes the literature, evaluates classifier algorithms for trading indicators, and makes practical recommendations for stock investors.