Thesis Abstract

Abstract
This thesis explores the application of machine learning algorithms in developing predictive models for stock market trends. The stock market is a complex and dynamic system influenced by various factors, making it challenging to predict its movements accurately. Machine learning techniques offer a promising approach to tackle this challenge by leveraging historical data to identify patterns and trends that can be used to forecast future market behavior. This research aims to investigate the effectiveness of machine learning algorithms in predicting stock market trends and to provide insights into the factors that drive stock market movements. The study begins with a comprehensive review of the literature on stock market prediction, machine learning algorithms, and their applications in financial markets. The literature review covers various aspects such as the efficient market hypothesis, technical and fundamental analysis, as well as previous studies on using machine learning for stock market prediction. Following the literature review, the research methodology chapter outlines the data collection process, feature selection techniques, model development, and evaluation metrics used in this study. The methodology incorporates the use of historical stock market data, technical indicators, and sentiment analysis to build predictive models using machine learning algorithms such as Decision Trees, Random Forest, Support Vector Machines, and Neural Networks. The findings chapter presents the results of the experimental analysis conducted to evaluate the performance of the predictive models developed in this study. The analysis compares the accuracy, precision, recall, and F1-score of different machine learning algorithms in predicting stock market trends. Additionally, the chapter discusses the importance of feature selection and model hyperparameter tuning in improving the predictive performance of the models. In the conclusion and summary chapter, the key findings of the study are summarized, and the implications of the research are discussed. The study highlights the potential of machine learning algorithms in predicting stock market trends and provides recommendations for future research in this area. Overall, this research contributes to the growing body of knowledge on using machine learning techniques for stock market prediction and offers valuable insights for investors, financial analysts, and policymakers. Keywords Stock Market Prediction, Machine Learning Algorithms, Predictive Modeling, Financial Markets, Feature Selection, Evaluation Metrics.

Thesis Overview