Comparing Machine Learning Algorithms for Predicting Stock Prices: A Case Study in the Financial Sector

Thesis Abstract

Abstract
This thesis investigates the application of various machine learning algorithms for predicting stock prices in the financial sector. The study aims to compare the performance of different algorithms in terms of accuracy, efficiency, and robustness in predicting stock prices. The research is motivated by the increasing interest in utilizing machine learning techniques to enhance stock price prediction and investment decision-making processes. The introduction provides an overview of the research topic, highlighting the importance of accurate stock price prediction in the financial sector. The background of the study discusses the current state of stock price prediction methods and the limitations of traditional approaches. The problem statement identifies the challenges faced in stock price prediction and the need for more advanced and accurate prediction models. The objectives of the study are outlined to evaluate and compare the performance of selected machine learning algorithms in predicting stock prices. The limitations of the study acknowledge potential constraints and challenges that may impact the research outcomes. The scope of the study defines the boundaries and focus areas of the research, specifying the types of data, algorithms, and evaluation metrics considered. The significance of the study emphasizes the potential impact of the research findings on improving stock price prediction accuracy and informing investment strategies in the financial sector. The structure of the thesis provides a roadmap for the organization of the research work, outlining the chapters and their respective contents. The definition of terms clarifies key concepts and terminology used throughout the thesis. The literature review chapter synthesizes existing research on machine learning algorithms for stock price prediction, highlighting the strengths and limitations of various approaches. The research methodology chapter describes the data collection process, algorithm selection criteria, evaluation metrics, and experimental setup used to compare the performance of machine learning algorithms. The discussion of findings chapter presents a detailed analysis of the experimental results, comparing the accuracy and efficiency of different algorithms in predicting stock prices. The conclusion chapter summarizes the key findings of the study, discusses the implications for stock price prediction in the financial sector, and suggests future research directions. Overall, this thesis contributes to the ongoing research on stock price prediction by providing a comprehensive comparison of machine learning algorithms and their performance in the financial sector. The findings of this study can potentially enhance predictive modeling techniques and decision-making processes for investors and financial analysts.