Thesis Abstract

Abstract
This thesis presents a comprehensive study on the application of machine learning algorithms for predictive modeling of stock market trends. The aim of this research is to develop and evaluate predictive models that can forecast future stock market trends with a high degree of accuracy. The study utilizes historical stock market data and applies various machine learning techniques to analyze and predict stock price movements. The introduction provides an overview of the project, outlining the background of the study and the problem statement. The main objective is to build robust predictive models that can effectively forecast stock market trends. The limitations and scope of the study are also discussed, along with the significance of the research in the field of finance and investment. The literature review delves into existing research on predictive modeling in stock market analysis, highlighting the different machine learning algorithms and techniques used in similar studies. Various factors influencing stock market trends are explored, providing a comprehensive understanding of the complexities involved in predicting stock prices. The research methodology section outlines the approach taken in this study, including data collection, preprocessing, feature selection, model building, and evaluation. Various machine learning algorithms such as linear regression, decision trees, random forests, and neural networks are implemented and compared to identify the most effective model for stock market prediction. The findings from the study are discussed in detail in chapter four, presenting the performance metrics and evaluation results of the predictive models. The analysis of the findings provides insights into the effectiveness of different machine learning algorithms in predicting stock market trends and highlights the key factors influencing the accuracy of the models. In conclusion, this thesis summarizes the key findings and contributions of the research, emphasizing the significance of predictive modeling in stock market analysis. The study demonstrates the potential of machine learning algorithms in forecasting stock market trends and provides valuable insights for investors and financial analysts. Overall, this research contributes to the growing body of knowledge on predictive modeling in finance and provides a foundation for future studies in the field of stock market analysis using machine learning algorithms.

Thesis Overview