Thesis Abstract

Abstract
This thesis explores the applications of machine learning techniques in predicting stock prices, aiming to enhance investment decision-making processes. The research investigates the effectiveness of various machine learning algorithms, such as neural networks, support vector machines, and decision trees, in forecasting stock prices based on historical data and market indicators. The study also examines the impact of different data preprocessing techniques and feature selection methods on the predictive accuracy of the models. Chapter One provides an introduction to the research topic, presenting the background of the study, defining the problem statement, outlining the objectives, discussing the limitations and scope of the study, highlighting the significance of the research, and presenting the structure of the thesis. Chapter Two comprises a comprehensive literature review that examines existing studies and theories related to machine learning in stock price prediction. This chapter aims to provide a theoretical framework for the research and identify gaps in the current literature. Chapter Three describes the research methodology employed in this study, including data collection procedures, data preprocessing techniques, feature selection methods, model selection criteria, and evaluation metrics. The chapter also discusses the experimental setup and validation strategies used to assess the performance of the machine learning models in predicting stock prices. Chapter Four presents the findings of the research, including the performance comparison of various machine learning algorithms, the impact of data preprocessing techniques on model accuracy, and the significance of feature selection in improving predictive performance. This chapter also discusses the implications of the results and provides insights into the practical applications of machine learning in stock price prediction. Chapter Five concludes the thesis by summarizing the key findings of the research, discussing the implications for investment decision-making, and suggesting future research directions. The study contributes to the field of finance and machine learning by demonstrating the potential of advanced computational techniques in improving stock price prediction accuracy and supporting informed investment decisions. Overall, this thesis provides a comprehensive analysis of the applications of machine learning in predicting stock prices, offering valuable insights for investors, financial analysts, and researchers interested in leveraging advanced technologies for enhancing financial decision-making processes. Word Count 261

Thesis Overview

The project titled "Applications of Machine Learning in Predicting Stock Prices" aims to explore the use of machine learning algorithms to predict stock prices in financial markets. The rapid advancements in technology have led to an increased interest in utilizing machine learning techniques to analyze vast amounts of financial data and make informed decisions in the stock market. The research will begin with a comprehensive introduction, providing background information on the significance of stock price prediction in financial markets. The problem statement will highlight the challenges faced by investors and traders in making accurate predictions due to the complex and volatile nature of the stock market. The objectives of the study will outline the specific goals and outcomes that the research aims to achieve. The limitations of the study will acknowledge any constraints or restrictions that may impact the research process or findings. The scope of the study will define the boundaries within which the research will be conducted, focusing on specific machine learning algorithms and stock market data. The significance of the study will emphasize the potential benefits of using machine learning in predicting stock prices, such as improved accuracy and efficiency in decision-making. The structure of the thesis will provide an overview of the chapters and subtopics that will be covered in the research work, guiding the reader through the organization of the study. The definition of terms will clarify key concepts and terminology related to machine learning and stock market prediction, ensuring a clear understanding of the research context. The literature review will explore existing studies and research on machine learning applications in predicting stock prices, highlighting the different algorithms and methodologies used in previous research works. The research methodology will outline the approach and techniques that will be employed to collect, analyze, and interpret data for the study, including data sources, variables, and research design. The discussion of findings will present the results of the analysis conducted using machine learning algorithms to predict stock prices, discussing the accuracy, reliability, and implications of the findings. The conclusion and summary will summarize the key findings of the research, discuss the implications for investors and traders, and suggest future research directions in the field of machine learning and stock market prediction. Overall, the project "Applications of Machine Learning in Predicting Stock Prices" aims to contribute to the understanding and application of machine learning techniques in financial markets, providing valuable insights and tools for making informed investment decisions.