The Data Incubator is joined by Jessica Stauth, Investment Managing Director of Quantopian, for the September 2018 installment of our free online webinar series, Data Science in 30 Minutes: Data Modeling the Stock Market Today - Common Pitfalls to Avoid.
The lure of creating models to predict the stock market has drawn talent from fields beyond finance and economics, reaching into disciplines such as physics, computational chemistry, applied mathematics, electrical engineering and perhaps most recently statistics and what we now refer to as data science. The attraction is clear - the stock market (and the economy/internet at large) throws off massive and ever increasing reams of data from garden variety time-series to complex structured data sets like quarterly financials, to unstructured data sets like conference call transcripts, news articles and of course — tweets! While all this data holds promise - it also holds traps and blind alleys that can be deceptively tricky to avoid. In this session we’ll review some of the common (but not easy!) pitfalls to avoid in creating models for predicting stock returns; overfitting & exploding model complexity, non-stationary processes, time-travel illusions, under-estimation of real-world costs, and as many more as we have time to cover.
About the speakers:
Dr. Jessica Stauth is the Investment Managing Director at Quantopian, a crowd-sourced quantitative investment firm, that inspires talented people from around the world to write investment algorithms. Jess and her team are in charge of selecting the algorithms from the Quantopian community, for our portfolio. Quantopian offers license agreements for algorithms that fit our investment strategy, and the licensing authors are paid based on their strategy's individual performance. Previously she has worked as an equity quant analyst at the StarMine Corporation and as a Director of Quant Product Strategy for Thomson Reuters prior to joining Quantopian in August of 2013. Jess holds a PhD from UC Berkeley in Biophysics.
Michael Li founded The Data Incubator, a New York-based training program that turns talented PhDs from academia into workplace-ready data scientists and quants. The program is free to Fellows, employers engage with the Incubator as hiring partners.Previously, he worked as a data scientist (Foursquare), Wall Street quant (D.E. Shaw, J.P. Morgan), and a rocket scientist (NASA). He completed his PhD at Princeton as a Hertz fellow and read Part III Maths at Cambridge as a Marshall Scholar. At Foursquare, Michael discovered that his favorite part of the job was teaching and mentoring smart people about data science. He decided to build a startup to focus on what he really loves.Michael lives in New York, where he enjoys the Opera, rock climbing, and attending geeky data science events.