Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques in predicting stock prices, focusing on the financial market. The study aims to develop and evaluate machine learning models that can effectively forecast stock prices, thereby aiding investors in making informed decisions. The research is motivated by the increasing interest in leveraging advanced technologies to enhance predictive analytics in the financial sector, particularly in stock market trading. The study begins with an introduction that provides background information on the significance of stock price prediction and the role of machine learning in financial forecasting. The problem statement highlights the challenges faced by investors in accurately predicting stock prices and the potential benefits of using machine learning algorithms. The objectives of the study are to develop robust machine learning models for stock price prediction, evaluate their performance, and provide insights into the factors influencing stock price movements. A comprehensive literature review is conducted in Chapter Two, which examines existing research on stock price prediction models, machine learning algorithms, and their applications in financial markets. The review covers various approaches, methodologies, and performance metrics used in predicting stock prices, highlighting the strengths and limitations of different techniques. Chapter Three details the research methodology employed in this study, including data collection, preprocessing, feature selection, model development, and evaluation. The methodology section outlines the steps taken to build and train machine learning models using historical stock price data, technical indicators, and other relevant features. The chapter also discusses the performance metrics used to evaluate the predictive accuracy of the models. In Chapter Four, the findings of the study are presented and discussed in detail. The performance of different machine learning models in predicting stock prices is analyzed, considering factors such as accuracy, precision, recall, and F1 score. The results highlight the effectiveness of certain algorithms in capturing stock price trends and patterns, providing valuable insights for investors and financial analysts. Finally, Chapter Five presents the conclusion and summary of the thesis, summarizing the key findings, contributions, and implications of the study. The conclusion reflects on the research objectives, discusses the limitations of the study, and suggests areas for future research and improvements in predictive modeling techniques. Overall, this thesis contributes to the growing body of knowledge on machine learning applications in financial forecasting, particularly in predicting stock prices, and provides practical insights for investors and financial institutions.

Thesis Overview