Thesis Abstract

Abstract
This thesis explores the applications of machine learning techniques in predictive modeling of stock prices. The study aims to investigate how various machine learning algorithms can be utilized to predict stock prices accurately, thereby aiding investors and financial analysts in making informed decisions. The research is motivated by the increasing complexity and volatility of financial markets, which necessitate more advanced tools for forecasting stock prices. The study begins with an introduction, providing an overview of the research problem and its significance in the context of financial markets. The background of the study reviews existing literature on stock price prediction using machine learning approaches, highlighting the gaps and areas for further research. The problem statement identifies the challenges and limitations faced in current stock price prediction methods, emphasizing the need for more accurate and reliable forecasting models. The objectives of the study are to evaluate the performance of different machine learning algorithms in predicting stock prices, compare their effectiveness, and identify the most suitable techniques for accurate forecasting. The limitations of the study are also discussed, acknowledging constraints such as data availability, model complexity, and market unpredictability. The scope of the study defines the boundaries and focus areas of the research, outlining the specific stocks, time periods, and evaluation metrics to be considered. The significance of the study lies in its potential to enhance the efficiency and effectiveness of stock price prediction, thereby improving investment decision-making and risk management practices. The structure of the thesis provides an overview of the chapters and their contents, guiding the reader through the research process. Definitions of key terms are provided to clarify the terminology used throughout the thesis. The literature review chapter examines previous studies on stock price prediction using machine learning, analyzing the methodologies, algorithms, and performance metrics employed. The research methodology chapter outlines the data collection process, feature selection techniques, model training and evaluation methods, and performance metrics used to assess the predictive models. The discussion of findings chapter presents a detailed analysis of the experimental results, comparing the performance of different machine learning algorithms in predicting stock prices. The chapter highlights the strengths and weaknesses of each approach, identifying the most effective techniques for accurate forecasting. In conclusion, this thesis contributes to the field of finance by demonstrating the potential of machine learning in improving stock price prediction accuracy. The study provides valuable insights into the strengths and limitations of various algorithms, guiding future research and practical applications in financial markets. Overall, this research enhances our understanding of the role of machine learning in predictive modeling of stock prices, offering new perspectives and opportunities for further advancements in the field.

Thesis Overview