Thesis Abstract

Abstract
This thesis explores the applications of machine learning techniques in predicting stock market trends. The stock market is known for its complex and dynamic nature, making it challenging for investors to accurately predict future price movements. Machine learning algorithms have gained significant attention in recent years for their ability to analyze vast amounts of data and identify patterns that can be used to make predictions. This study aims to investigate how machine learning models can be applied to predict stock market trends and potentially improve investment decision-making. The research begins with a comprehensive introduction that provides background information on the stock market and the challenges associated with predicting its trends. The problem statement highlights the limitations of traditional methods and the need for more accurate and reliable prediction models. The objectives of the study are outlined, focusing on the development and evaluation of machine learning algorithms for stock market prediction. The literature review delves into existing research on machine learning applications in finance and stock market prediction. Ten key areas are explored, including different machine learning algorithms, data sources, feature selection techniques, and evaluation metrics. The review provides a foundation for understanding the current state of the field and identifies gaps that this study aims to address. The research methodology section outlines the approach taken to develop and evaluate machine learning models for predicting stock market trends. Eight components are discussed, including data collection and preprocessing, model selection, feature engineering, training and testing procedures, and performance evaluation metrics. The methodology is designed to ensure the robustness and reliability of the prediction models developed in this study. The discussion of findings section presents the results of applying machine learning models to predict stock market trends. The analysis includes an evaluation of model performance, comparison of different algorithms, interpretation of feature importance, and insights into the predictive capabilities of the models. The findings provide valuable information on the effectiveness of machine learning in stock market prediction and its potential impact on investment strategies. In conclusion, this thesis summarizes the key findings and implications of applying machine learning in predicting stock market trends. The study demonstrates the feasibility of using machine learning algorithms to enhance the accuracy of stock market predictions and improve decision-making for investors. The significance of this research lies in its contribution to the field of finance and the potential benefits it offers to market participants seeking to optimize their investment strategies. Overall, this thesis highlights the potential of machine learning techniques in predicting stock market trends and provides valuable insights for future research and practical applications in the financial industry. By leveraging the power of data-driven algorithms, investors can make more informed decisions and navigate the complexities of the stock market with greater confidence. Word Count 399

Thesis Overview