Stock Market Data Using Data Mining For Feature Extraction

Abstract

This paper presents a robust approach for feature extraction from stock market data by combining Principal Component Analysis (IPCA) and Moving Averages (MA). IPCA reduces dimensionality, capturing underlying patterns, while MAs identify trends and cyclic behaviors. The synergistic integration of these techniques enhances the extraction of essential features for stock market analysis. Research method effectively uncovers relevant information, offering valuable insights for trading and investment decisions. It addresses dimensionality challenges and identifies meaningful patterns, promoting a deeper understanding of market dynamics.