Predictive modeling of stock prices using machine learning algorithms

Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms in predictive modeling of stock prices. The financial markets are known for their volatile nature, making accurate prediction of stock prices a challenging task. Machine learning techniques have gained popularity in recent years for their ability to analyze large datasets and extract meaningful patterns that can be used for predictive purposes. This research aims to investigate the effectiveness of machine learning algorithms in predicting stock prices and to provide insights into the potential benefits and limitations of these techniques in the financial domain. The study begins with a detailed introduction that outlines the background of the research, the problem statement, objectives, limitations, scope, significance of the study, and the structure of the thesis. A comprehensive literature review is conducted in Chapter Two, exploring existing research on stock price prediction using machine learning algorithms. This chapter covers topics such as data preprocessing, feature selection, model selection, evaluation metrics, and various machine learning algorithms commonly used in stock price prediction. Chapter Three focuses on the research methodology, detailing the data collection process, preprocessing steps, feature selection techniques, model selection criteria, evaluation metrics, and experimental setup. The chapter also discusses the implementation of machine learning algorithms such as linear regression, support vector machines, random forests, and neural networks for stock price prediction. Chapter Four presents a detailed discussion of the findings obtained from the experiments conducted in this study. The results of the predictive modeling using different machine learning algorithms are analyzed and compared against each other to evaluate their performance in predicting stock prices accurately. The chapter also discusses the factors influencing the prediction accuracy and provides insights into the strengths and limitations of each algorithm. Lastly, Chapter Five presents the conclusion and summary of the thesis. The key findings of the study are summarized, and recommendations for future research are provided. The research contributes to the existing body of knowledge by demonstrating the potential of machine learning algorithms in predicting stock prices and highlighting the importance of data quality, feature selection, and model evaluation in achieving accurate predictions in the financial markets. In conclusion, this thesis provides valuable insights into the application of machine learning algorithms in predictive modeling of stock prices. The findings of this research contribute to the growing field of financial analytics and offer practical implications for investors, financial analysts, and researchers interested in utilizing machine learning techniques for stock price prediction.