Applications of Deep Learning in Predicting Stock Market Trends

Thesis Abstract

Abstract
This thesis explores the applications of deep learning techniques in predicting stock market trends. The stock market is known for its dynamic and unpredictable nature, making it a challenging domain for traditional statistical models to accurately forecast trends. Deep learning, a subset of artificial intelligence, has gained popularity in recent years due to its ability to analyze vast amounts of data and extract complex patterns. This study aims to leverage the power of deep learning algorithms to enhance the accuracy and efficiency of stock market trend predictions. The research begins with a comprehensive review of existing literature on deep learning applications in finance and stock market prediction. Various deep learning models and methodologies used in predicting stock market trends are examined to provide a solid foundation for the research. In the methodology chapter, the research approach is outlined, including data collection, preprocessing, feature selection, model training, and evaluation. The study employs historical stock market data, including price movements, trading volumes, and other relevant indicators, to train and test the deep learning models. The findings chapter presents the results of the experiments conducted using the deep learning models. The performance metrics such as accuracy, precision, recall, and F1 score are used to evaluate the effectiveness of the models in predicting stock market trends. The discussion provides insights into the strengths and limitations of the deep learning approach in stock market prediction. The conclusion summarizes the key findings of the study and discusses the implications of using deep learning techniques in predicting stock market trends. The study contributes to the existing body of knowledge by demonstrating the potential of deep learning in improving the accuracy of stock market predictions and guiding investment decisions. Overall, this thesis sheds light on the opportunities and challenges of applying deep learning in the financial domain, specifically in predicting stock market trends. The results of this research can benefit investors, financial analysts, and researchers seeking to enhance their decision-making processes in the ever-changing landscape of the stock market.