Thesis Abstract

Abstract
This thesis focuses on the application of machine learning algorithms for predictive modeling of stock market trends. The stock market is a complex system influenced by various factors, making it challenging to predict with traditional methods. Machine learning offers a powerful tool to analyze historical stock data and identify patterns that can be used to forecast future trends. The objective of this study is to develop and evaluate machine learning models for predicting stock market trends, with a focus on accuracy and effectiveness. The research begins with a comprehensive review of the existing literature on stock market prediction and machine learning techniques. Various studies and approaches are examined to provide a solid foundation for the development of the predictive models in this thesis. In the methodology section, the research design and data collection processes are outlined. The study utilizes historical stock market data to train and test machine learning models, including regression, classification, and ensemble methods. Feature selection techniques are employed to identify the most relevant variables for predicting stock market trends. The findings chapter presents the results of the predictive modeling experiments conducted in this study. The performance of different machine learning algorithms is evaluated based on metrics such as accuracy, precision, recall, and F1 score. The findings provide insights into the effectiveness of various models in predicting stock market trends. The discussion chapter critically analyzes the results and discusses the implications of the findings. The strengths and limitations of the predictive models are assessed, along with recommendations for future research and practical applications in the stock market domain. In conclusion, this thesis contributes to the field of stock market prediction by demonstrating the potential of machine learning algorithms in forecasting market trends. The study highlights the importance of accurate and reliable predictive models for investors, traders, and financial analysts. By leveraging machine learning techniques, it is possible to improve the accuracy of stock market predictions and make more informed investment decisions. Keywords Predictive modeling, Stock market trends, Machine learning algorithms, Financial forecasting, Data analysis.

Thesis Overview