Predictive Modeling of Stock Prices Using Machine Learning Techniques

Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques in predicting stock prices, aiming to improve the accuracy of forecasting in financial markets. Stock price prediction is a crucial task for investors, traders, and financial analysts, as it helps in making informed decisions and maximizing returns on investments. Traditional methods of stock price prediction often rely on statistical models and technical analysis, which may have limitations in capturing the complex and dynamic nature of financial markets. Machine learning algorithms offer a promising alternative by leveraging historical data to identify patterns and trends that can be used to predict future stock prices. Chapter 1 provides an introduction to the research topic, outlining the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definition of terms. The chapter sets the stage for the research by highlighting the importance of accurate stock price prediction and the potential of machine learning techniques in addressing this challenge. Chapter 2 presents a comprehensive literature review on stock price prediction, machine learning algorithms, and their applications in the financial domain. The chapter covers ten key aspects related to the research topic, including the existing methods of stock price prediction, the advantages of machine learning techniques, and previous studies that have applied machine learning in financial forecasting. Chapter 3 describes the research methodology adopted in this study, detailing the data collection process, feature selection, model development, and evaluation metrics. The chapter outlines the steps taken to preprocess the data, train and test machine learning models, and assess their performance in predicting stock prices accurately. Chapter 4 presents an elaborate discussion of the findings obtained from applying various machine learning algorithms to predict stock prices. The chapter analyzes the performance of different models, identifies the strengths and weaknesses of each approach, and discusses the implications of the results for financial market participants. Chapter 5 concludes the thesis by summarizing the key findings, discussing the contributions of the study, and suggesting directions for future research. The chapter highlights the significance of using machine learning techniques for stock price prediction and underscores the importance of ongoing advancements in this field for improving decision-making in financial markets. Overall, this thesis contributes to the growing body of literature on stock price prediction by demonstrating the potential of machine learning techniques in enhancing forecasting accuracy. By leveraging historical data and advanced algorithms, this research aims to provide valuable insights for investors and financial professionals seeking to make informed decisions in dynamic market environments.