BLOG INSIGHTS AND ANALYSIS

As August, and summer, slowly comes to a close our interns will inevitably depart for new academic years or adventures ahead. While most of our interns are still around, we threw a casual pizza and build-your-own-sundae night. It was great to see our interns, strangers to us and each other a mere three months ago, comfortably mingle with their teams and Quantbot employees. As interns complete their projects we hope they can take the experience and accomplishments from this summer into their future successful careers. 

A big thank you to all our amazing interns this year 👏

You can’t be in NYC without getting some NY style pizza!

Our incredible admin Lily being Queen of the Ice Cream 

It’s always a surprise to see the businesses that can fit in a NYC skyscraper. In a nondescript office building in the middle of a nondescript Midtown block with barely any signage, there happens to be a top-of-art squash facility 5 floors in the air. Who knew? Thankfully Quantbot did, and that’s where we had our latest intern outing. Interns faced company employees, and each other, to see who’s squashing was the best. Some went all out, while others ate and drank where squash happened to be playing. Either way it was fun!

The group getting the low down on squash

Squash!

More squash!

…and Tequila!

This week our summer interns had the opportunity to go on a walking history and food tour of Lower Manhattan. Starting at the African Burial Ground National Monument, we then walked through Foley Square to get to Chinatown, where we stopped for our first food stop, fried dumplings.

Starting the tour in front of the African Burial Ground

Crossing over Canal Street, we walked through Little Italy, and after a sit down pizza break, we continued the tour toward the Bowery and the Lower East Side, where we tasted knishes from a Jewish bakery that’s over 100 years old. In each neighborhood we learned about the lives of African, Irish, Chinese, Italian and Jewish immigrants in NYC, their trials and triumphs moving to a new home and how they in turn shaped the city and the country.

Walking through Chinatown on our way to Little Italy

Finishing off our tour at a German beer garden, we hope the interns got a chance to see and appreciate some of the many iconic neighborhoods New York has to offer. At the very least they certainly got plenty to eat!

Last month, a few team members from our London office participated in an enjoyable bouldering outing. Nuo, a DTL member, visited the London office to work and engage in valuable team bonding. The outing was not only fun but also helped strengthen team relationships. The team is enthusiastic about future opportunities to challenge themselves both mentally and physically!

Left to right: Cindy Xin, Nuo Tian, and Alexandra Gkolia

What’s better than one Datathon? How about two?!

On Friday April 19, Quantbot was thrilled to participate in Schonfeld’s Early Engagement Summit PhD Datathon. Using Code Willing’s dynamic data processing and modeling platform, PhDs from around the country worked in competing groups to find and model alpha signals from pre-determined datasets. This event gave PhD candidates, whose disciplines ranged from Mathematics and Statistics to Economics, the opportunity to test quantitative methods on real financial data, and to encourage their future engagement in the Quant space. 

Ashar welcomes PhD candidates before the Schonfeld Datathon

Quantbot employee, Greg, mentors his team through the Schonfeld Datathon

The winning Schonfeld Datathon team

Since everything comes better in pairs, the very next day Quantbot sponsored a separate Datathon at Carnegie Mellon, also powered by Code Willing’s platform. Participating students came from either CMU’s Computational Finance Master’s program (MSCF) or their undergraduate Math program. It was a victory for MSCF since a group of their students took home the top prize, but the 2nd-place undergraduates were equally competitive. After introducing the students to each competition, Ashar Mahoob, Quantbot’s CIO, handed out the prizes on both days to the winning teams. We were thrilled to foster an interest in Quant Finance in all the students, from PhD to undergrad, and we hope they will consider it as a viable career option. We are excited to repeat our success in future!

Quantbot mentor, Rui, celebrates with her winning team at the CMU Datathon

The official victory photo of the CMU Datathon-winning team

Some thoughts from the Quantbot mentors:

Mentoring cwiq-team-central was extremely rewarding.  They are a bright and enthusiastic group of students who worked together quite well and sought my guidance regularly.  I think the most important lessons I hope the team took away are how rich the field of alpha research is in terms of the breadth of both datasets and the machine learning techniques that can be applied, and how powerful Quantbot’s platform is for quickly developing new ideas and evaluating their performance. – Greg Sternberg

It was a rewarding experience mentoring the winning team, cwiq-team-code. They were a group of motivated young professionals who were highly collaborative and eager to learn. I was amazed by the abundance of brilliant ideas they tested in such a short time. The Quantbot platform proved indispensable in handling these computationally heavy and data-intensive tasks. I hope this victory will inspire them to take on even more interesting and critical challenges that impact global markets – Rui Xiong, Datathon winning team mentor

And an afterword from our CIO, Ashar:

Quantbot Hackathon 2024 provided CMU’s Masters and Undergrad students a first-hand opportunity to work on a real-life alpha research project, under tight time and budget constraints, using real world datasets. I wish I had had an opportunity to participate in such a project when I was completing the MSCF program at CMU years ago. The students had a fun day of learning and competing. Additionally, all the mentors were very impressed by how well the students coordinated the research process and were able to come up with out-of-sample model performance that was in-line with observed performance in-sample.