Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques in predictive modeling of stock prices. The use of machine learning in financial markets has gained significant attention due to its potential to uncover patterns and trends that traditional statistical methods may overlook. The study aims to develop a predictive model that can forecast stock prices with a high level of accuracy, thereby assisting investors in making informed decisions. The research begins with a comprehensive review of existing literature on machine learning applications in financial forecasting. Various algorithms and techniques used in stock price prediction are examined to provide a solid foundation for the study. Chapter three outlines the methodology employed in the research, including data collection, preprocessing, feature selection, model training, and evaluation. The empirical analysis in chapter four presents the findings of applying machine learning algorithms to historical stock price data. Different models, such as linear regression, decision trees, random forests, and neural networks, are tested and compared based on their predictive performance. The results highlight the effectiveness of certain algorithms in capturing the complex patterns of stock price movements. Furthermore, the study discusses the implications of the findings and their relevance to the financial industry. The limitations of the research, such as data quality and model interpretability, are acknowledged, along with suggestions for future research directions in enhancing predictive accuracy. In conclusion, the thesis summarizes the key findings and contributions of the study in advancing the field of predictive modeling of stock prices using machine learning techniques. The potential benefits of implementing such models in real-world trading scenarios are discussed, emphasizing the importance of continuous refinement and validation to ensure robust and reliable predictions.

Thesis Overview