Date of Award
University Scholars Director
Dr. Christine Chaney
First Advisor/Committee Member
Dr. Carlos Arias
Computer Science, AI, Artificial Intelligence, Neural Networks
Quantitative analysis has been a staple of the financial world and investing for many years. Recently, machine learning has been applied to this field with varying levels of success. In this paper, two different methods of machine learning (ML) are applied to predicting stock prices. The first utilizes deep learning and Long Short-Term Memory networks (LSTMs), and the second uses ensemble learning in the form of gradient tree boosting. Using closing price as the training data and Root Mean Squared Error (RMSE) as the error metric, experimental results suggest the gradient boosting approach is more viable.
Honors Symposium: ML is an unbelievably powerful tool, and the application of ML must be subject to our biblical calling as stewards. As technology progresses to make us increasingly productive, we must direct what we produce towards ends that glorify God. Just as importantly, we must be vigilant to the great temptation to become lost in decadence. ML has wildly successful applications in the financial world that far surpass the scope of this paper, but we cannot lose sight of He who provides. A firm grounding in scripture and a healthy understanding of Providence should be enough to keep those of us who pursue the blessing of technology from becoming lost in our own grandeur.
Cederborg, Carl Samuel, "Machine Learning in Stock Price Prediction Using Long Short-Term Memory Networks and Gradient Boosted Decision Trees" (2021). Honors Projects. 132.
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