Thesis Abstract

Abstract
The financial market is characterized by its dynamic nature, making stock price prediction a challenging and crucial task for investors and financial analysts. This thesis focuses on the application of machine learning techniques in predicting stock prices using time series analysis. The primary objective is to develop predictive models that can effectively forecast stock prices, enabling investors to make informed decisions and optimize their investment strategies. The research begins with an introduction that provides an overview of the project, followed by a background study that examines the existing literature on stock price prediction, machine learning, and time series analysis. The problem statement highlights the challenges and limitations faced in accurately predicting stock prices, emphasizing the need for advanced predictive models. The objectives of the study are outlined to guide the research process, while the limitations and scope of the study clarify the boundaries and extent of the research. The significance of the study lies in its potential to enhance stock price prediction accuracy, contributing to improved investment decisions and financial outcomes for stakeholders in the financial market. The structure of the thesis is detailed to provide a roadmap of the chapters and their respective content, ensuring a coherent and logical flow of information. Additionally, key terms and concepts relevant to the research are defined to facilitate a better understanding of the study. The literature review delves into ten essential aspects related to stock price prediction, machine learning algorithms, time series analysis techniques, and previous studies in the field. This comprehensive review forms the foundation for the research methodology, which includes data collection, preprocessing, feature selection, model training, evaluation, and validation processes. The research methodology section also discusses the selection of machine learning algorithms, parameter tuning, and performance metrics used to assess the predictive models. Chapter four presents a detailed discussion of the findings obtained from implementing various machine learning models for stock price prediction. The analysis includes the evaluation of model performance, comparison of prediction results, identification of key factors influencing stock prices, and insights gained from the predictive models. The discussion emphasizes the strengths and limitations of the models and provides recommendations for future research and practical applications. In conclusion, chapter five summarizes the key findings of the study, reiterates the research objectives, and discusses the implications of the research outcomes. The thesis concludes with a reflection on the contributions of this study to the field of stock price prediction, highlighting the potential impact on investment decision-making and financial market efficiency. Overall, this research advances the understanding of machine learning applications in predicting stock prices and offers valuable insights for investors, financial analysts, and researchers in the finance domain.

Thesis Overview