Thesis Abstract

Abstract
This thesis explores the application of machine learning techniques in predicting stock market trends, focusing on developing predictive models to assist investors in making informed decisions. The study aims to leverage historical stock market data and machine learning algorithms to forecast future price movements and trends with a high degree of accuracy. The research methodology involves data collection, preprocessing, feature selection, model training, validation, and evaluation. Chapter One introduces the research topic, providing background information on the stock market, the significance of predictive modeling, and the limitations and scope of the study. The problem statement highlights the challenges faced by investors in predicting stock market trends and the need for accurate forecasting models. The objectives of the study are to develop and evaluate machine learning models for predicting stock market trends. The chapter concludes with a discussion on the structure of the thesis and definitions of key terms. Chapter Two presents a comprehensive literature review on machine learning techniques used in stock market prediction. The review covers topics such as time series analysis, regression models, neural networks, support vector machines, decision trees, and ensemble methods. The chapter explores existing research studies, methodologies, findings, and limitations in the field of stock market prediction using machine learning techniques. Chapter Three details the research methodology employed in developing predictive models for stock market trends. The methodology includes data collection from financial databases, preprocessing to clean and transform the data, feature selection to identify relevant predictors, model training using machine learning algorithms, validation to assess model performance, and evaluation to measure predictive accuracy. The chapter also discusses the tools and software used in the study. Chapter Four presents a detailed discussion of the findings obtained from applying machine learning techniques to predict stock market trends. The chapter includes analyses of model performance, accuracy metrics, comparison with baseline models, and insights gained from the predictive models. The discussion covers the strengths and limitations of the models developed and provides recommendations for future research and practical applications. Chapter Five concludes the thesis with a summary of the key findings, contributions to the field, limitations of the study, implications for investors and practitioners, and avenues for future research. The conclusion highlights the significance of predictive modeling in stock market analysis and the potential benefits of using machine learning techniques for making informed investment decisions. In conclusion, this thesis contributes to the growing body of research on predictive modeling of stock market trends using machine learning techniques. The study provides valuable insights into the application of machine learning algorithms in forecasting stock prices and trends, offering potential benefits for investors, financial analysts, and decision-makers in the stock market.

Thesis Overview