Application of Machine Learning in Predicting Stock Prices

Thesis Abstract

Abstract
The stock market is a complex and volatile environment, where investors strive to make informed decisions to maximize their returns. Traditional methods of stock price prediction have limitations, prompting the exploration of alternative techniques such as machine learning. This thesis investigates the application of machine learning algorithms in predicting stock prices to provide investors with valuable insights and enhance decision-making processes. The study begins with a comprehensive introduction, presenting the background of the research and highlighting the current challenges in stock price prediction. The problem statement identifies the limitations of traditional methods and the need for innovative approaches to improve accuracy and efficiency. The objective of the study is to evaluate the performance of machine learning models in predicting stock prices and compare them with conventional techniques. The scope of the study focuses on a specific set of stocks and time periods to ensure a detailed analysis. A thorough review of existing literature on stock price prediction and machine learning algorithms is presented in Chapter Two. The literature review explores various methodologies and approaches used in previous studies, highlighting the strengths and weaknesses of different models. This comprehensive analysis provides a foundation for the research methodology and guides the selection of appropriate techniques for prediction. Chapter Three outlines the research methodology, detailing the data collection process, feature selection, model training, and evaluation methods. The chapter includes discussions on data preprocessing techniques, model selection criteria, and performance evaluation metrics. The study employs a combination of supervised and unsupervised learning algorithms to predict stock prices accurately and efficiently. Chapter Four presents a detailed discussion of the findings obtained from the application of machine learning models in predicting stock prices. The chapter evaluates the performance of different algorithms, compares their accuracy and efficiency, and discusses the implications of the results. The findings provide valuable insights into the effectiveness of machine learning in stock price prediction and highlight the potential benefits for investors. Finally, Chapter Five concludes the thesis by summarizing the key findings, discussing the implications of the research, and suggesting future directions for further studies. The conclusion reflects on the significance of applying machine learning in stock price prediction and its potential impact on investment decisions. Overall, this thesis contributes to the existing literature by demonstrating the effectiveness of machine learning algorithms in predicting stock prices and offering practical recommendations for investors and researchers in the field.