Comparison of Machine Learning Algorithms for Predicting Stock Prices

Thesis Abstract

Abstract
The stock market is a complex and dynamic environment influenced by a multitude of factors, making accurate predictions of stock prices challenging yet crucial for investors and financial analysts. Machine learning algorithms have emerged as powerful tools for forecasting stock prices due to their ability to analyze large volumes of data and identify intricate patterns. The objective of this research is to compare the performance of different machine learning algorithms in predicting stock prices, with the aim of identifying the most effective approach. The study begins with an introduction that highlights the significance of accurate stock price predictions and the role of machine learning algorithms in this context. The background of the study provides an overview of the current state of stock price prediction methods and the increasing reliance on machine learning techniques in financial markets. The problem statement underscores the challenges faced in accurately forecasting stock prices and the need for advanced computational methods to address these issues. The objectives of the study are outlined to evaluate and compare the performance of various machine learning algorithms, such as Support Vector Machines, Random Forest, and Neural Networks, in predicting stock prices. The limitations of the study are acknowledged, including the inherent uncertainties in stock market behavior and the reliance on historical data for forecasting. The scope of the study is defined to focus on comparing the accuracy, efficiency, and robustness of selected machine learning algorithms in predicting stock prices. The significance of the study lies in its potential to provide valuable insights for investors, financial analysts, and researchers seeking to enhance their stock price prediction models. The structure of the thesis is detailed to guide readers through the chapters and sub-sections, highlighting the logical flow of the research. Definitions of key terms are provided to ensure clarity and understanding of the terminology used throughout the thesis. The literature review chapter critically examines existing research on stock price prediction using machine learning algorithms, highlighting the strengths and weaknesses of different approaches. The research methodology chapter outlines the data collection process, feature selection techniques, model training, and evaluation methods employed in the study. The discussion of findings chapter presents a detailed analysis of the performance of each machine learning algorithm in predicting stock prices, highlighting their respective strengths and limitations. In conclusion, this thesis contributes to the field of stock market analysis by providing a comprehensive comparison of machine learning algorithms for predicting stock prices. The study offers valuable insights into the effectiveness of different approaches and identifies potential areas for further research and improvement. Overall, this research aims to enhance the accuracy and reliability of stock price predictions, ultimately benefiting investors and financial stakeholders in making informed decisions in the dynamic stock market environment.