Application of Machine Learning in Predicting Stock Prices

Thesis Abstract

Abstract
The stock market is a complex and dynamic system influenced by various factors, making it challenging to predict stock prices accurately. This thesis explores the application of machine learning techniques to predict stock prices, aiming to enhance investment decision-making processes. The study begins with a comprehensive literature review to establish the foundation of machine learning in stock price prediction. Various machine learning algorithms and their applications in financial forecasting are discussed, highlighting their strengths and limitations. The research methodology employed in this study involves data collection from historical stock market data, feature selection, model training, and performance evaluation. The impact of different features on the prediction accuracy of machine learning models is analyzed to identify the most relevant factors influencing stock prices. The study employs quantitative analysis methods to evaluate the performance of machine learning models, such as accuracy, precision, recall, and F1-score. The findings of this research reveal the effectiveness of machine learning models in predicting stock prices compared to traditional statistical methods. The study demonstrates the potential of machine learning algorithms, such as support vector machines, random forests, and neural networks, in capturing complex patterns in stock market data and making accurate predictions. The results also highlight the importance of feature selection and data preprocessing techniques in improving model performance. The discussion of findings delves into the implications of using machine learning in stock price prediction for investors, financial analysts, and market regulators. The study emphasizes the need for transparency, interpretability, and robustness in machine learning models to enhance their credibility and reliability in real-world applications. The limitations of the study, including data availability, model complexity, and market volatility, are also addressed to provide a balanced view of the research outcomes. In conclusion, the application of machine learning in predicting stock prices offers promising opportunities for enhancing investment strategies and risk management in financial markets. By leveraging advanced machine learning techniques and big data analytics, investors can make more informed decisions based on data-driven insights and predictive models. This thesis contributes to the growing body of knowledge on the intersection of machine learning and finance, paving the way for future research in this field.