Thesis Abstract

Abstract
The stock market is a complex and dynamic system that is influenced by a multitude of factors, making it challenging to predict stock prices accurately. In recent years, machine learning techniques have gained popularity as powerful tools for analyzing and predicting stock price movements. This thesis explores the applications of machine learning in predicting stock prices, focusing on the development and evaluation of predictive models using historical stock market data. Chapter 1 provides an introduction to the research topic, including the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. The chapter sets the stage for the subsequent chapters by outlining the research context and objectives. Chapter 2 presents a comprehensive literature review that examines existing research on machine learning techniques for stock price prediction. The review covers various machine learning algorithms, data sources, feature selection methods, model evaluation techniques, and challenges in predicting stock prices. The chapter synthesizes and analyzes the key findings from the literature to inform the research methodology. Chapter 3 details the research methodology, outlining the steps taken to collect, preprocess, and analyze historical stock market data. The chapter describes the selection of machine learning algorithms, feature engineering strategies, model training and testing procedures, and performance evaluation metrics used in developing predictive models for stock prices. The methodology provides a systematic framework for conducting empirical research on stock price prediction using machine learning techniques. Chapter 4 presents a detailed discussion of the findings from the empirical analysis of stock price prediction models. The chapter examines the performance of different machine learning algorithms in predicting stock prices, analyzes the impact of feature selection on model accuracy, and discusses the implications of the results for investors and financial markets. The discussion highlights the strengths and limitations of the predictive models and offers insights into improving their predictive accuracy. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting avenues for future research. The chapter reflects on the contributions of the study to the field of stock market prediction using machine learning techniques and offers recommendations for practitioners and researchers interested in applying machine learning to predict stock prices. Overall, this thesis contributes to the growing body of research on the applications of machine learning in predicting stock prices. By developing and evaluating predictive models using historical stock market data, the study sheds light on the potential of machine learning techniques to enhance stock price prediction accuracy and offers valuable insights for investors, financial analysts, and researchers seeking to leverage machine learning for stock market analysis.

Thesis Overview