Committee Meeting, Free for Members, Group Meeting, Interest Group Meetings, Quantitative Investing, Virtual Events & Programming

Quantitative Investing Group Meeting

Monday, September 19 | 5:00 PM - 6:30 PM

Share Event

Loading Events

Wait! A Note on Registration:

We’ve launched Cvent—our new events platform!

Registration for any event with a start date after Sept. 28 now requires a CFA Institute account.

I don’t have a CFA Institute account

  • No problem! You’ll have the chance to create one prior to registration.

I already have a CFA Institute account

  • Great! Be sure to use your existing credentials at registration.
Cvent Transition Guide

Group Description

The Quantitative Investing Group brings together professionals seeking to incorporate cutting edge quantitative investment techniques and alternative data sets in their investing and risk processes. Members include (but are not limited to) discretionary and systematic portfolio managers, risk managers, traders and fundamental analysts, data strategists, quantitative researchers, and others. The topic covered range from quantitative alpha generation, big data as well as alternative datasets, quantamental signals i.e. the intersection of fundamental analysis and quantitative decision making, mathematical and statistical aspects of modern quantitative analysis, use of programming languages or quant tools, Natural Language Processing, machine learning for investing and risk management, theory and implementation of AI in finance and more.

JOIN THIS GROUP

Guest Speaker

Nicole Koenigstein, Data Science and Technology Lead, impactvise, Quantitative Research Consultant, quantmate

Nicole Koenigstein is a Data Scientist & Quantitative Researcher currently working as Data Science and Technology Lead at impactvise, an ESG analytics company, and Technology and Quantitative Research Consultant at quantmate, an innovative FinTech startup that leverages alternative data as part of its predictive modeling strategy. She is the author of Mathematics for Machine Learning with NLP and Python.