Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques in predicting stock prices using time series analysis. The stock market is a complex and dynamic system influenced by various factors, making accurate predictions challenging. Traditional methods often struggle to capture the intricate patterns in stock price movements, leading to inaccurate forecasts. Machine learning, with its ability to adapt and learn from data, offers a promising approach to improving prediction accuracy in stock price forecasting. The study begins with an introduction to the research problem, highlighting the challenges faced in predicting stock prices and the significance of employing machine learning algorithms. The background of the study provides context on the stock market environment, emphasizing the need for innovative approaches to enhance prediction accuracy. The problem statement identifies the limitations of traditional forecasting methods and sets the stage for exploring machine learning as a solution. The objectives of the study are outlined to guide the research process, focusing on developing and evaluating machine learning models for stock price prediction. The limitations of the study are acknowledged, including data availability, model complexity, and the inherent unpredictability of financial markets. The scope of the study defines the specific aspects of stock price prediction that will be addressed, such as feature selection, model training, and performance evaluation. The significance of the study lies in its potential to improve stock price prediction accuracy, aiding investors, financial analysts, and policymakers in making informed decisions. The structure of the thesis provides an overview of the chapters, highlighting the sequence of topics covered in the research. Definitions of key terms are provided to clarify terminology used throughout the thesis. The literature review explores existing research on stock price prediction and machine learning techniques, identifying trends, challenges, and opportunities in the field. Ten key aspects of the literature are analyzed, including relevant algorithms, datasets, evaluation metrics, and comparison studies. This review sets the foundation for the research methodology, guiding the selection of appropriate techniques and tools for model development. The research methodology details the steps taken to collect, preprocess, and analyze stock price data for model training and testing. Eight key components of the methodology are discussed, covering data acquisition, feature engineering, model selection, hyperparameter tuning, and performance evaluation. The rationale behind each methodological decision is explained to ensure transparency and reproducibility of the results. The discussion of findings presents the results of the machine learning models developed for stock price prediction, highlighting their performance, strengths, and limitations. The analysis of results sheds light on the effectiveness of different algorithms, feature sets, and training strategies in forecasting stock prices accurately. Insights gained from the findings inform recommendations for improving prediction models and addressing challenges encountered during the research. In conclusion, the study summarizes the key findings, contributions, and implications of applying machine learning in predicting stock prices using time series analysis. The thesis highlights the potential of machine learning to enhance stock price forecasting accuracy and outlines future research directions to further advance the field. Overall, this research contributes to the growing body of knowledge on leveraging machine learning for financial prediction tasks, offering valuable insights for practitioners and researchers in the field.

Thesis Overview