Thesis Abstract

Abstract
This thesis presents a comprehensive investigation into the application of machine learning algorithms for predictive modeling of stock market trends. With the rapid advancement of technology and the increasing complexity of financial markets, there is a growing need for accurate and efficient tools to forecast stock price movements. Machine learning techniques offer a promising solution by leveraging historical data to identify patterns and make predictions. The primary objective of this study is to develop and evaluate machine learning models for predicting stock market trends, with a focus on enhancing predictive accuracy and robustness. Chapter 1 provides an introduction to the research topic, highlighting the background of the study, problem statement, objectives, limitations, scope, significance, structure of the thesis, and definitions of key terms. The chapter sets the foundation for the subsequent chapters by outlining the research context and objectives. Chapter 2 presents a detailed literature review, which explores existing research on stock market prediction, machine learning algorithms, and their applications in financial markets. The review encompasses ten key areas, including the efficient market hypothesis, technical analysis, fundamental analysis, sentiment analysis, and the use of neural networks in stock price forecasting. By synthesizing relevant literature, this chapter informs the development of the research methodology. Chapter 3 outlines the research methodology employed in this study. The methodology encompasses eight key components, including data collection, feature selection, model selection, model training, evaluation metrics, hyperparameter tuning, validation techniques, and performance comparison. The chapter details the steps taken to preprocess data, select appropriate features, train and optimize machine learning models, and assess their predictive performance. Chapter 4 presents an in-depth discussion of the findings obtained through the application of machine learning algorithms to predict stock market trends. The chapter evaluates the performance of various models, analyzes the predictive accuracy, examines feature importance, and discusses the implications of the results. The discussion sheds light on the strengths and limitations of the models and provides insights for future research. Chapter 5 serves as the conclusion and summary of the thesis, summarizing the key findings, implications, and contributions of the study. The chapter also highlights the limitations of the research and proposes recommendations for future work in the field of predictive modeling of stock market trends using machine learning algorithms. Overall, this thesis contributes to the growing body of research on stock market prediction by demonstrating the effectiveness of machine learning algorithms in forecasting stock price movements. The findings of this study have practical implications for investors, financial analysts, and researchers seeking to leverage machine learning techniques for enhancing stock market predictions.

Thesis Overview