Exploring Stock Market Forecasting through Improved Machine Learning Methodology

Abstract

This paper investigates the enhancement of machine learning methodologies for stock market forecasting, an area critically important for financial analytics and investment strategies. The study systematically compares traditional and advanced machine learning techniques to identify the most effective methods for predicting stock prices. Key components of the research include the utilization of ensemble methods, feature engineering, and deep learning algorithms, particularly Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks, known for their proficiency in handling sequential data. The methodology encompasses a comprehensive preprocessing stage where financial data, including historical prices and volume, are cleansed and transformed into a machine-learnable format. Feature engineering is emphasized to extract and select temporal and technical indicators that significantly impact predictive accuracy. The research further explores the integration of ensemble methods that combine the strengths of various simple models to improve prediction reliability and accuracy.