Thesis Abstract

Abstract
The rapid advancement of technology has revolutionized the financial markets, leading to increased interest in utilizing machine learning algorithms for stock price prediction. This thesis explores the application of machine learning techniques in predicting stock prices and aims to contribute to the existing body of knowledge in this field. The study begins with a comprehensive review of relevant literature on stock price prediction, machine learning algorithms, and their applications in the financial sector. The research methodology section outlines the data collection process, feature selection methods, and model evaluation techniques employed in this study. The findings from the empirical analysis reveal the effectiveness of machine learning algorithms in predicting stock prices compared to traditional statistical methods. The discussion of the findings highlights the key factors influencing stock price prediction accuracy, such as data quality, feature selection, model complexity, and hyperparameter tuning. The study concludes with a summary of the key findings, implications for practitioners and policymakers, limitations of the study, and recommendations for future research directions. Overall, this thesis contributes to the growing body of literature on machine learning applications in finance by providing insights into the challenges and opportunities of predicting stock prices using advanced computational techniques. The findings of this study have practical implications for investors, financial analysts, and policymakers seeking to leverage machine learning algorithms for more accurate and timely stock price predictions.

Thesis Overview