Fintech (Financial Technology) enables more people to create economic and social value at a much lower cost. However, there is currently no reliable fintech application for asset valuation, which is crucial for investment decisions. Existing automated trading applications, in general, adopt black box algorithms with no theoretical guidance, i.e., we have little idea why those algorithms perform well sometimes and under which conditions the algorithms would fail. In this project, we propose a new methodology for asset valuation by considering insights from classic economic theory and machine learning in data science. We first propose an indicator of asset valuation based on classic economic theory. Then we collect historical data for each argument in the simple indicator and utilize machine learning methods to assess the effectiveness of the simple indicator in explaining asset valuation and informing investment. Finally, we propose an automated trading strategy based on the simple indicator for further industry applications and test its performance in comparison to the conventional strategies. Junior and Senior Students who majored in Economics or Data Science are encouraged to apply.
The undergraduate scholars involved are supported by the pioneering Summer Research Scholar Program at Duke Kunshan University. Our Summer 2021 scholars leverage a variety of financial & economic datasets such as the “Alpha Vantage realtime stock API.”
Saad Lahrichi, Jiayi Li, Xinyu Tian, and Carlos-Gustavo Salas-Flores majors in Data Science; Tianyu Wu (Student Project Lead) majors in Mathematics and Computational Science, Duke Kunshan University
Saad Lahrichi, Ph.D. student in Computer Science, University of Montana
Jiayi Li, Master student in Mathematics of Finance, Columbia University
Xinyu Tian, Master student in Computer Science, Duke University
Carlos-Gustavo Salas-Flores, Software Development Engineer, Amazon
Tianyu Wu, Master student in data analytics, Northwestern University
Luyao Zhang*, Tianyu Wu#, Saad Lahrichi#, Jiayi Li#, Carlos-Gustavo Salas-Flores#. “A Data Science Pipeline for Algorithmic Trading: A Comparative Study of Applications for Finance and Cryptoeconomics.” 2022 IEEE International Conference on Blockchain (Blockchain), Aug. 2022, 10.1109/blockchain55522.2022.00048.
Presented by Tianyu Wu at the First International Symposium on Recent Advances of Blockchain Evolution: Architecture, Intelligence, Incentives, and Applications (BlockchianEvo 2022), Espoo, Finland, August 22-25, 2022, IEEE conference proceedings.