Application of Machine Learning in Predicting Stock Prices

Thesis Abstract

Abstract
The stock market is a complex and dynamic system that is influenced by a multitude of factors, making predicting stock prices a challenging task. In recent years, the application of machine learning techniques has gained popularity as a promising approach to forecasting stock prices due to its ability to analyze large volumes of data and extract patterns. This thesis explores the use of machine learning algorithms in predicting stock prices and aims to provide insights into the effectiveness of these techniques in the context of financial markets. The study begins with an introduction to the background of the research, highlighting the significance of predicting stock prices for investors and financial analysts. The problem statement identifies the challenges associated with traditional stock price prediction methods and sets the stage for the research objectives, which include evaluating the performance of machine learning algorithms in forecasting stock prices. The limitations and scope of the study are also outlined to provide a clear understanding of the research boundaries. Chapter two presents a comprehensive review of the literature on stock price prediction, covering ten key themes such as traditional forecasting methods, machine learning algorithms, and financial market dynamics. The review synthesizes existing research findings and identifies gaps in the literature that warrant further investigation. Chapter three details the research methodology employed in this study, including data collection, preprocessing, model selection, and evaluation metrics. The methodology section outlines the steps taken to train and test machine learning models using historical stock price data and discusses the rationale behind the chosen approach. Chapter four presents the findings of the study, including the performance metrics of different machine learning algorithms in predicting stock prices. The discussion section analyzes the results, compares the performance of various models, and interprets the implications for investors and financial analysts. In the final chapter, chapter five, the thesis concludes with a summary of the key findings and contributions of the study. The conclusion reflects on the effectiveness of machine learning in predicting stock prices, discusses the limitations of the research, and suggests avenues for future research in this field. Overall, this thesis contributes to the existing body of knowledge on stock price prediction by demonstrating the potential of machine learning techniques in forecasting financial markets. The findings of this study have practical implications for investors and financial institutions seeking to improve their decision-making processes and enhance their understanding of stock market dynamics.