Predictive Modeling of Stock Prices Using Machine Learning Techniques

Thesis Abstract

Abstract
The prediction of stock prices has long been a challenging and crucial task in the financial sector. With the emergence of machine learning techniques, there has been a growing interest in leveraging these advanced methodologies to enhance the accuracy and efficiency of stock price forecasting. This thesis aims to investigate the application of machine learning techniques for predictive modeling of stock prices, with a focus on exploring the potential benefits and limitations of these approaches. Chapter One provides an introduction to the research topic, discussing the background of the study, the problem statement, objectives, limitations, scope, significance, and the structure of the thesis. The definitions of key terms are also outlined to provide a clear understanding of the research context. Chapter Two presents a comprehensive literature review that examines existing studies and methodologies related to stock price prediction using machine learning techniques. The review covers topics such as time series analysis, feature selection, model evaluation, and the comparison of various machine learning algorithms for stock price prediction. Chapter Three details the research methodology employed in this study, including data collection, preprocessing, feature engineering, model selection, evaluation metrics, and validation techniques. The chapter also discusses the selection criteria for machine learning algorithms and parameter tuning strategies. Chapter Four presents an in-depth discussion of the findings obtained from implementing different machine learning models for stock price prediction. The chapter includes detailed analyses of the model performance, feature importance, and the impact of various factors on the prediction accuracy. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research, and providing recommendations for future studies in this field. The chapter also highlights the contributions of this research work to the existing literature on predictive modeling of stock prices using machine learning techniques. Overall, this thesis contributes to the ongoing research efforts in the field of financial forecasting by demonstrating the potential of machine learning techniques for enhancing stock price prediction accuracy. The findings of this study offer valuable insights for investors, financial analysts, and researchers seeking to improve their understanding of stock market dynamics and make more informed investment decisions.