Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms in predictive modeling of stock prices. The study aims to develop a robust and accurate predictive model that can forecast future stock prices based on historical data. With the rapid advancements in technology and the availability of vast amounts of financial data, machine learning techniques have shown promising results in predicting stock market trends. This research focuses on implementing various machine learning algorithms, such as random forest, support vector machines, and neural networks, to analyze historical stock data and predict future price movements. The thesis begins with an introduction that provides an overview of the research topic and outlines the objectives of the study. The background of the study highlights the importance of stock price prediction in financial markets and the potential benefits of using machine learning algorithms for this purpose. The problem statement identifies the challenges in accurately predicting stock prices and emphasizes the need for advanced analytical techniques to improve forecasting accuracy. The objectives of the study define the specific goals and aims to be achieved through the research. The limitations of the study are also discussed, acknowledging the constraints and potential challenges that may impact the research outcomes. The scope of the study outlines the specific boundaries and focus areas of the research, including the selection of stocks, data sources, and evaluation metrics. The significance of the study emphasizes the practical implications of developing accurate stock price prediction models for investors, financial analysts, and market participants. The structure of the thesis provides an overview of the organization and flow of the research document, highlighting the chapters and sections included in the study. The literature review chapter presents an in-depth analysis of existing research and literature related to stock price prediction and machine learning algorithms. This section covers key concepts, theories, and methodologies used in previous studies, providing a comprehensive background for the current research. The research methodology chapter describes the data collection process, feature selection techniques, model development, and evaluation methods employed in the study. The detailed methodology ensures transparency and reproducibility of the research findings. The discussion of findings chapter presents the results of the predictive modeling experiments conducted using various machine learning algorithms. This section evaluates the performance of the models, compares different techniques, and interprets the predictive accuracy of each approach. The conclusion and summary chapter summarize the key findings, implications, and contributions of the research. The conclusion also discusses the practical applications of the predictive models developed and suggests potential areas for future research and improvement. In conclusion, this thesis contributes to the growing body of literature on stock price prediction and machine learning applications in financial markets. By developing and evaluating predictive models using advanced algorithms, this research aims to provide valuable insights for investors, traders, and financial analysts seeking to make informed decisions in the dynamic and complex stock market environment.

Thesis Overview