Thesis Abstract

Abstract
Stock market prediction has long been a topic of interest for investors, analysts, and researchers alike. With the advancement of technology and the availability of vast amounts of data, machine learning techniques have emerged as powerful tools for predicting stock market trends. This thesis explores the applications of machine learning in predicting stock market trends, aiming to improve the accuracy and efficiency of stock market forecasting. The thesis begins with an introduction, providing an overview of the research topic and the motivation behind the study. The background of the study discusses the evolution of stock market prediction methods and the role of machine learning in this field. The problem statement highlights the challenges faced in stock market prediction and the need for more accurate and reliable forecasting methods. The objectives of the study are outlined, focusing on developing machine learning models for stock market trend prediction. The limitations of the study are identified, acknowledging potential constraints and challenges that may impact the research outcomes. The scope of the study defines the boundaries within which the research will be conducted, outlining the specific aspects of stock market prediction that will be explored. The significance of the study emphasizes the potential benefits of using machine learning in stock market prediction, including improved decision-making and risk management for investors. The structure of the thesis provides an overview of the chapters and sections that will be included, guiding the reader through the research process. Definitions of key terms are provided to ensure clarity and understanding of the terminology used throughout the thesis. Chapter two presents a comprehensive literature review, analyzing existing research on machine learning techniques for stock market prediction. The review covers various approaches, methodologies, and findings in the field, highlighting the strengths and limitations of different models. Chapter three details the research methodology, including data collection, preprocessing, feature selection, model training, and evaluation techniques. The chapter outlines the steps taken to develop and validate machine learning models for predicting stock market trends, ensuring transparency and reproducibility in the research process. Chapter four presents a detailed discussion of the findings, analyzing the performance of the machine learning models in predicting stock market trends. The chapter explores the accuracy, efficiency, and robustness of the models, identifying key factors that influence their predictive capabilities. In conclusion, chapter five provides a summary of the research findings and their implications for stock market prediction. The thesis highlights the contributions of the study to the field of machine learning and stock market forecasting, as well as potential avenues for future research. Overall, this thesis contributes to the ongoing efforts to enhance stock market prediction using machine learning techniques, offering insights into the applications, challenges, and opportunities in this rapidly evolving field.

Thesis Overview