07-26, 10:50–11:20 (US/Eastern), Online talks and posters
There is a compelling opportunity for scientists and engineers to leverage their proficiency in mathematics combined with artificial intelligence and data science tools to drive value creation within businesses and ultimately democratize wealth through quantitive finance approaches. Toward this opportunity, this poster describes a course piloted at the Smith School of Chemical Engineering at Cornell University during Spring 2022 to teach quantitative finance to engineers and scientists in Julia.
Finance and economic forecasting, modeling, and decision-making are typically not part of a traditional engineering or physical science curriculum. However, many engineers and scientists are migrating toward employment opportunities in the financial and consulting industries. Further, despite increased market access, there remains a significant barrier to entry for many individuals in our society to the wealth-creation opportunities offered by markets. Toward these unmet needs, we developed the CHEME 5660 Financial Data, Markets, and Mayhem course in collaboration with Polygon.io, a leading financial market data provider. The class, which introduced financial systems, markets, and the tools to analyze and model financial data to engineers and scientists at Cornell University, had an initial enrollment of 60 students from CHEM, Physics/AEP, Engineering Management, CS/ORIE, CBE, BME, CEE, ECE, AEP, and the Johnson Business School. The course content was delivered via a combination of lectures and guided computational sessions enabled by Pluto and Jupyter notebooks. All course materials are open source, including notes, examples, and labs.
CHEME 5660 catalyzed the development of multiple Julia packages to support the course's educational goals. For example, PQPolygonSDK.jl and PQEcolaPoint.jl were developed to support the class. The PQPolygonSDK.jl package is a software development kit for the Polygon.io financial data platform. Polygon.io provides real-time and historical data for various assets. A vital component of the success of CHEME 5660 was access to high-quality data sets supplied by Polygon.io. This data allowed us to study the statistical properties of financial data and other topics, such as modeling and analysis tools for describing and ultimately predicting asset pricing dynamics and issues such as portfolio management and hedging. Further, we used tools from artificial intelligence, such as Markov Decision Processes (MDPs) and model-based and model-free reinforcement learning to study optimal decision-making, dynamic hedging, and trade management using actual data sets (including minute-resolution data). Thus, data provided by Polygon.io through the PQPolygonSDK.jl package enabled Cornell students to learn and explore quantitative finance topics with actual data, which was a critical and differentiating feature of the course. Additionally, the PQEcolaPoint.jl package was developed to study the pricing and trade mechanics of equity derivative products, i.e., options, a central topic in the course. Options are a huge market in the United States; the average daily notional value of traded single-stock options rose to more than $450 billion in 2021, compared with about $405 billion for stocks, according to Cboe Global Markets data (2021). To put these values in perspective, the annual global biopharmaceuticals market was valued at USD 401.32 billion in 2021. Thus, in a single day, the options market in the United States trades more than the entire annual global biopharmaceutical market.
Moving forward, several new packages will be developed to support the course. In particular, we are working on a new portfolio management package that will initially be focused on implementing traditional approaches, such as the data-driven and model-driven Markowitz problem. In addition, we are working on dynamic hedging and high-frequency trading packages that will take advantage of real-time data from Polygon.io. These packages will support new content in the course in market making, i.e., how leading liquidity providers such as Citadel Securities drive efficient markets.
Finally, we’ll share lessons learned from students in the course, students with no computational background, and students whose primary language was not Julia. Accessible and reliable notebook technologies enabled these students' broad adoption of Julia. However, there was significant resistance in some cases because of the well-known “time to first plot” issue and general configuration headaches, especially on Windows. This was especially true in cases where heavy computation, e.g., Monte-Carlo price simulations or extensive portfolio optimization calculations, were attempted on various student machines.
Jeff Varner holds a Ph.D. in Engineering from Purdue University. He spent 18 years as a Professor at the Smith School of Chemical Engineering at Cornell University