Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms in predicting stock market trends. The study aims to develop a predictive model that can effectively forecast the movement of stock prices based on historical data and market variables. The research methodology involves a comprehensive literature review of existing studies on stock market prediction and machine learning techniques. Various machine learning algorithms such as Random Forest, Support Vector Machine, and Neural Networks will be implemented and evaluated for their predictive performance. Chapter One provides an introduction to the research topic, discussing the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter Two presents a detailed literature review covering ten key areas related to stock market prediction and machine learning algorithms. The review includes discussions on previous research findings, methodologies, and challenges in the field. Chapter Three outlines the research methodology, including data collection methods, feature selection techniques, model training, validation, and evaluation procedures. The chapter also discusses the selection of performance metrics to assess the predictive accuracy of the models. Various aspects of the methodology such as data preprocessing, feature engineering, and model selection are elaborated upon. Chapter Four presents an in-depth analysis and discussion of the findings obtained from implementing different machine learning algorithms for stock market prediction. The chapter evaluates the performance of each model based on metrics such as accuracy, precision, recall, and F1-score. The results are compared and interpreted to identify the strengths and weaknesses of each algorithm in predicting stock market trends. Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting recommendations for future studies. The thesis contributes to the field of stock market prediction by showcasing the effectiveness of machine learning algorithms in forecasting stock price movements. The study highlights the importance of data quality, feature selection, and model optimization in developing accurate predictive models for financial markets. In conclusion, this thesis provides valuable insights into the application of machine learning algorithms for stock market prediction and offers a framework for developing robust predictive models in the financial domain. The research findings have practical implications for investors, financial analysts, and policymakers seeking to leverage data-driven approaches for making informed decisions in the stock market.

Thesis Overview