Thesis Abstract

Abstract
This thesis investigates the use of machine learning techniques in predicting stock market trends, aiming to enhance decision-making processes for investors and financial professionals. The application of machine learning algorithms in analyzing stock market data has gained significant attention in recent years due to its potential to provide valuable insights and improve forecasting accuracy. The study explores various machine learning models, including regression analysis, classification algorithms, and neural networks, to predict stock price movements and identify profitable investment opportunities. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. The chapter sets the foundation for understanding the importance of applying machine learning in stock market prediction. Chapter 2 presents a comprehensive literature review encompassing ten key areas related to the application of machine learning in stock market prediction. The review covers the evolution of machine learning in financial markets, various machine learning techniques, challenges, and opportunities in stock market prediction, and recent advancements in the field. Chapter 3 details the research methodology employed in this study, including data collection methods, feature selection techniques, model development, evaluation metrics, and validation procedures. The chapter outlines the steps taken to preprocess the data, train the machine learning models, and assess their predictive performance. Chapter 4 discusses the findings of the study, presenting a detailed analysis of the predictive accuracy of the machine learning models in forecasting stock market trends. The chapter evaluates the performance of different algorithms, identifies key factors influencing prediction outcomes, and discusses the implications of the results for investment decision-making. Chapter 5 offers a conclusion and summary of the thesis, highlighting the key findings, contributions, limitations, and recommendations for future research. The study underscores the potential of machine learning in enhancing stock market prediction accuracy and emphasizes the importance of continuous research and innovation in this area. In conclusion, this thesis contributes to the growing body of knowledge on the applications of machine learning in predicting stock market trends. By leveraging advanced algorithms and data-driven techniques, investors and financial professionals can gain valuable insights into market dynamics, improve decision-making processes, and enhance their overall investment performance.

Thesis Overview