Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms in predicting stock market trends, aiming to enhance decision-making processes for investors and financial analysts. The study investigates the effectiveness and accuracy of various machine learning models in forecasting stock price movements. The research methodology involves collecting historical stock market data, preprocessing and analyzing the data, and implementing machine learning algorithms to build predictive models. Chapter One provides an introduction to the research topic, background information, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter Two presents a comprehensive literature review, covering ten key aspects related to predictive modeling, stock market trends, and machine learning algorithms. Chapter Three outlines the research methodology, including data collection, data preprocessing, feature selection, model training, evaluation metrics, and validation techniques, among others. Chapter Four discusses the findings of the study, analyzing the performance of different machine learning models in predicting stock market trends. The results are interpreted and compared to identify the most effective algorithms for stock price prediction. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing implications for financial decision-making, suggesting future research directions, and highlighting the overall contributions of this study to the field of stock market analysis and machine learning applications. This thesis contributes to the growing body of research on predictive modeling in financial markets and provides valuable insights for investors, traders, and researchers seeking to leverage machine learning techniques for stock market forecasting.

Thesis Overview