Application of Machine Learning in Predicting Stock Prices

Thesis Abstract

Abstract
Stock price prediction is a crucial area in finance and investment that has garnered significant attention from researchers and practitioners. With the emergence of machine learning techniques, there has been a growing interest in utilizing these methods to forecast stock prices accurately. This thesis investigates the application of machine learning algorithms in predicting stock prices, aiming to enhance the performance and reliability of stock market predictions. The study begins with an exploration of the theoretical background of stock price prediction and the role of machine learning in this context. It delves into the various challenges and limitations associated with traditional stock price forecasting methods, highlighting the need for innovative approaches to address these issues effectively. The research objectives are outlined to provide a clear direction for the study. These objectives include developing machine learning models for stock price prediction, evaluating the performance of different algorithms, and comparing the results with traditional forecasting techniques. The study also defines the scope and limitations of the research, setting boundaries for the investigation. A comprehensive literature review is conducted to analyze existing studies on stock price prediction using machine learning. The review covers various machine learning algorithms, data sources, feature selection techniques, and evaluation metrics employed in stock market forecasting. By synthesizing the findings of previous research, the study aims to identify gaps and opportunities for further exploration in the field. The research methodology section outlines the data collection process, feature selection techniques, model development, and evaluation methodologies employed in the study. Various machine learning algorithms, including regression models, support vector machines, neural networks, and ensemble methods, are implemented and compared to assess their effectiveness in predicting stock prices accurately. The findings of the study are presented and discussed in detail, highlighting the performance of different machine learning models in stock price prediction. The results demonstrate the potential of machine learning algorithms to outperform traditional forecasting methods in terms of accuracy and reliability. The implications of these findings for investors, financial analysts, and policymakers are discussed, emphasizing the importance of adopting innovative approaches in stock market prediction. In conclusion, the study summarizes the key findings, implications, and contributions to the field of stock price prediction using machine learning. The study highlights the significance of leveraging advanced computational techniques to enhance the accuracy and efficiency of stock market forecasts. Recommendations for future research and practical applications of machine learning in stock price prediction are provided to guide further exploration in this area. Ultimately, this research contributes to the growing body of knowledge on the application of machine learning in predicting stock prices, offering valuable insights and opportunities for future advancements in the field.