Predictive Modeling of Stock Prices using Machine Learning Algorithms

Thesis Abstract

Abstract
This thesis presents a comprehensive study on the application of machine learning algorithms in predicting stock prices. The primary objective of this research is to develop predictive models that can accurately forecast stock prices based on historical data and market trends. The study focuses on the implementation and evaluation of various machine learning techniques, including regression analysis, decision trees, support vector machines, and neural networks. Chapter One provides an introduction to the research topic, background information on stock price prediction, a statement of the problem, research objectives, limitations, scope, significance of the study, and an overview of the thesis structure. The chapter also includes definitions of key terms related to the research. Chapter Two consists of a detailed literature review that explores previous studies and research findings related to stock price prediction using machine learning algorithms. This chapter covers ten key aspects, including the history of stock price prediction, machine learning techniques, data preprocessing methods, feature selection, model evaluation metrics, and challenges in stock price forecasting. Chapter Three outlines the research methodology employed in this study. This chapter includes the research design, data collection methods, data preprocessing techniques, model development procedures, evaluation metrics, and validation strategies. Additionally, it discusses the selection of machine learning algorithms and the rationale behind their choice for stock price prediction. Chapter Four presents a comprehensive discussion of the findings obtained from the application of machine learning algorithms to predict stock prices. The chapter analyzes the performance of different models, compares their accuracy and efficiency, and discusses the implications of the results. It also highlights the strengths and limitations of the predictive models developed. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research, and offering recommendations for future studies in the field of stock price prediction using machine learning algorithms. The chapter also reflects on the significance of the research in enhancing decision-making processes in the financial markets. In conclusion, this thesis contributes to the existing body of knowledge on stock price prediction by demonstrating the effectiveness of machine learning algorithms in forecasting stock prices. The research findings provide valuable insights for investors, financial analysts, and policymakers seeking to make informed decisions in the dynamic and competitive stock market environment.