Application of Machine Learning in Predicting Stock Prices

Thesis Abstract

Abstract
The stock market is a complex and dynamic environment that is influenced by numerous factors, making it challenging to predict stock prices accurately. In recent years, the use of machine learning algorithms has gained popularity in financial markets for their ability to analyze vast amounts of data and identify patterns that traditional methods may overlook. This thesis explores the application of machine learning in predicting stock prices, with a focus on enhancing prediction accuracy and efficiency. Chapter One provides an introduction to the research topic, presenting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of key terms. Chapter Two conducts a comprehensive literature review, analyzing existing studies on machine learning techniques in stock price prediction, including their strengths, limitations, and implications for financial markets. Chapter Three outlines the research methodology employed in this study, detailing the data collection process, selection of machine learning algorithms, model training, validation techniques, and evaluation metrics. The chapter also discusses the features used in the predictive models and the preprocessing techniques applied to optimize the data for analysis. Chapter Four presents the findings of the research, including the performance of various machine learning algorithms in predicting stock prices. The chapter discusses the accuracy, precision, recall, and other relevant metrics to assess the effectiveness of the models. Furthermore, the chapter explores the impact of different features and data preprocessing methods on the prediction results. In Chapter Five, the conclusion and summary of the thesis are provided, highlighting the key findings, implications, and recommendations for future research in the field of machine learning for stock price prediction. The study demonstrates the potential of machine learning algorithms to enhance forecasting accuracy in financial markets and offers insights into improving predictive models for better decision-making in investment strategies. Overall, this thesis contributes to the growing body of knowledge on the application of machine learning in predicting stock prices, offering valuable insights for researchers, practitioners, and investors seeking to leverage advanced analytical tools for more accurate and efficient financial forecasting.