Thesis Abstract

Abstract
The dynamic and complex nature of stock markets presents challenges for investors seeking to make informed decisions. In this study, we explore the application of machine learning algorithms to predict stock market trends. The aim is to develop a predictive model that can analyze historical stock data and provide insights into future market movements. Chapter 1 introduces the research topic by outlining the background of the study, stating the problem statement, setting the objectives, highlighting the limitations and scope of the study, discussing the significance of the research, and providing an overview of the thesis structure. Chapter 2 comprises a comprehensive literature review that examines existing studies on stock market prediction, machine learning algorithms, and their applications in financial markets. The review covers various methodologies, techniques, and models used in predicting stock market trends. In Chapter 3, the research methodology is detailed, including the data collection process, data preprocessing techniques, selection of machine learning algorithms, model training and evaluation methods, and performance metrics used to assess the predictive model. Chapter 4 presents an in-depth discussion of the findings obtained from applying machine learning algorithms to predict stock market trends. The chapter analyzes the effectiveness of different algorithms, identifies key factors influencing market trends, and discusses the implications of the results in the context of investment decision-making. Chapter 5 concludes the thesis by summarizing the key findings, discussing the implications of the research, highlighting the contributions to the field of finance and machine learning, and suggesting areas for future research. The study aims to enhance our understanding of stock market dynamics and provide a valuable tool for investors to make more informed decisions. Overall, this thesis contributes to the growing body of research on predictive modeling in financial markets and demonstrates the potential of machine learning algorithms in forecasting stock market trends. The findings have practical implications for investors, financial analysts, and policymakers seeking to leverage technology for improved decision-making in the stock market.

Thesis Overview