Robo Advising Using Python: A New Finance Course

Abstract

With the advancement of FinTech and Robo-advising, financial services will gradually reach low-income households who need such services the most. To meet this trend, our universities should train our next-generation financial planners who understand the related theories and techniques. In this paper, we discuss a course proposal with six prerequisites related to finance, accounting, statistics, and programming. This course consists of three parts: theory review, such as Sharpe/Treynor/Sortino ratios, the Black-Litterman model (1991), and Parametric Portfolio Policies (Brandt et al. 2009); Python basics; and applying Python to theories: such as how to allocate assets, how to find an optimal portfolio by maximizing its Sharpe ratio, and how to apply the Black-Litterman model by embedding investors’ risk preference and views. Excel is used to help students understand the theories better, verify simple results from Python, and help future customers peek into the black-box of robo-advising. A few Python programs and Excel VBAs are included.

Keywords: Financial planners, FinTech, Robo-advising, Low-income households, Python, Finance-major

JEL Classification: G00, G19, G14