Interpretability of a supervised learning-based trading strategy

Faculty Mentor/s:

Prof. Patrick Zoro, Finance

Student names: 

Chaitali Shinde

Huanxiu Wu

Junying Wu

Qixin Fu

Project Description:

This project will work to develop an interpretability of the supervised learning-based trading strategy using different approaches and get insight into what signals are driving a trading strategy. Students will publish their research findings in a disciplinary journal, as well as learning cutting-edge machine learning techniques, Python coding, and data analytics.

Month/Year Project Began:
May 2022