Thesis Abstract

Abstract
This thesis explores the applications of machine learning in predicting stock market trends. The stock market is a complex and dynamic system that is influenced by various factors, making it challenging to predict with traditional methods. Machine learning techniques have gained popularity in recent years for their ability to analyze large volumes of data and identify patterns that can be used to make predictions. In this study, we aim to investigate the effectiveness of machine learning algorithms in predicting stock market trends and to compare their performance with traditional forecasting methods. The research begins with an introduction to the topic, providing background information on the stock market and the challenges of predicting its trends. The problem statement highlights the limitations of traditional forecasting methods and the potential benefits of using machine learning algorithms. The objectives of the study are outlined, focusing on evaluating the performance of various machine learning models in predicting stock market trends. The literature review examines existing research on the application of machine learning in stock market prediction. Ten key studies are reviewed, highlighting the different approaches and techniques used in previous research. This review serves as a foundation for understanding the current state of the field and identifying gaps that this study aims to address. The research methodology section outlines the approach taken to conduct the study, including data collection, feature selection, model training, and evaluation. Eight key components are discussed, covering the dataset used, preprocessing steps, feature engineering techniques, model selection, hyperparameter tuning, evaluation metrics, and validation methods. The methodology is designed to ensure the robustness and reliability of the results obtained. The discussion of findings chapter presents an in-depth analysis of the results obtained from applying machine learning algorithms to predict stock market trends. The performance of each model is evaluated based on key metrics such as accuracy, precision, recall, and F1 score. The findings are compared with traditional forecasting methods to assess the effectiveness of machine learning in this context. In the conclusion and summary chapter, the key findings of the study are summarized, and the implications of the results are discussed. The limitations of the study are acknowledged, and recommendations for future research are provided. The thesis concludes with a reflection on the significance of using machine learning in predicting stock market trends and its potential impact on financial markets. Overall, this thesis contributes to the growing body of research on the applications of machine learning in finance and provides insights into the effectiveness of these techniques in predicting stock market trends. The findings offer valuable guidance for researchers, practitioners, and investors seeking to leverage machine learning for improved decision-making in the financial markets.

Thesis Overview