Introduction

This course will survey emerging topics in the area of financial technology and also allow students to develop practical programming, statistics, and mathematical skills that are valued in the Fintech industry. We will explore topics from both theoretical and practical perspectives in the areas of automated wealth management, digital lending, peer-to-peer applications including payments and insurance, machine learning financial applications, and digital currency and blockchain trends. During this course, students will have the opportunity to gain an understanding of each of these topics as well as develop their own software in the Python programming language related to these areas. For example, when discussing robo-advising, we will examine how a robo-advisor can select ETFs based on tracking error and expense ratio considerations, construct a portfolio based upon client preferences and asset allocation techniques, and rebalance the portfolio on a regular basis.

Course Outcomes

After successful completion of this course, students will:

• Be conversant in both the main theoretical ideas and high level concepts of the financial technology topics to be discussed.
• Aim to become proficient in the implementation of key ideas behind such topics in the Python programming language.
• Given exposure to core ideas in financial technology as well as develop hands-on programming skills in the Fintech area.
• Given outlines for a main project for this course which may lead into future research projects or a masters thesis topic.
• Finally, this course is a precursor for further Fintech topic-specific graduate courses.

Textbook

McKinney, Wes. Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

Additional References

Whitepapers, Survey Articles, and Video materials will be given to students throughout the class

Grading Policies