Wharton FinTech
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Wharton FinTech

Mark Higgins, Co-Founder and Chief Analytics Officer of Beacon— A Next Generation Platform for Quants

In this episode, I’m joined by Mark Higgins, Co-Founder and Chief Analytics Officer of , a development environment for quants, built to balance the rigor of enterprise software with the freedom of quantitative development and problem solving in public markets. Mark and I cover a range of topics, including:

Mark’s Background:

“My first job before I was at Goldman, I was the only developer at this little FinTech that was building trading system for for a Virginia power utility… We had to figure it all out on the spot, which was a great learning experience for me. They had their own, initial market crises when power prices (which normally were like $30, a megawatt hour) went to like $3,000 because some power plant unexpectedly went offline. And so half the hedge funds made a ton of money when power prices went from $30 to $3,000. And the other half of them went bankrupt.”

Mark began his career as a PhD in physics, before moving over to the world of finance. His start in the world of trading and derivatives began at a small commodity trading desk, building risk trading systems as the team’s sole quant. A few years later, he would go on to Goldman Sachs on the Foreign Exchange Trading desk, where he met Kirat Singh his co-founder at Beacon, and began to work on Goldman’s SecDB platform.

Goldman and SecDB:

“But you know, [Kirat and I] kind of grew up at Goldman, in the SecDB platform, which is a really interesting combination of a derivatives focused-trading and risk management system. So the system where you do trades with people, if you book them, and then you can see all the portfolio of stuff that you have and see how much money you make or lose in different market scenarios… And then they have this development platform that was part of the system, which is where all of us would actually write all of our code. So all the data was there that we needed, all the trades and positions and market data and reference data and everything was already in there.”

Mark and Kirat cut their teeth at Goldman where they became familiar with testing software at scale in the same environment that trades and risk were being analyzed. From their time at SecDB, and subsequently at JP Morgan and BAML, Mark and Kirate started to sketch the outline of the architectural model and environment that would later become Beacon.

Founding Beacon and solving the enterprise development problem:

“Maybe one guy downloads, Python, and another person builds stuff in C++ and another one uses R. And all those things solve these little point business problems when they come up. But then three years later, you’ve got hundreds of these little disconnected point solutions, and everything kind of grinds to a halt. ”

Mark explains how quants’ tendency to begin problem solving “side-of-desk” can work in the short run, but will ultimately hamstring a trading desk. We continue to discuss the benefits of Beacon’s production environment and to what degree he thinks Goldman’s SecDB was to credit for their performance in the 2008 financial crisis.

The Appeal of Finance for PhDs:

“I didn’t even know that quantitative finance existed when I was doing my physics PhD. Physics is exciting and intellectually interesting, but it’s slow — you work on something for months, or years, and you publish papers. And there are maybe six people in the world who know what your thing is.”

We discuss whether Finance (specifically quantitative work) is still appealing for PhDs coming out of today’s doctorate programs.

The Latest Applications of AI/ML in trading:

“… and so the idea with deep hedging, is to say, well, instead of all this sort of risk neutral gunk, we’re just going to train a neural net to figure out the best hedges for your derivatives portfolio. And in the limit, where all those risk neutral assumptions apply, then the best hedges are the risk neutral hedges, and so it reproduces all the stuff that we’ve traditionally done, but then it sort of generalizes to the space where risk neutral pricing doesn’t work. ”

Mark explains “deep hedging” a new application of machine learning and neural nets facilitated by technologies such as Beacon.

About Mark Higgins:

Mark Higgins (PhD) is a veteran of the finance industry, and a true technologist with a PhD in astrophysics. Before co-founding Beacon with Kirat Singh in 2014, Mark spent eight years at JP Morgan, launching and delivering the Athena project, and co-heading the quantitative research group for the investment bank. Prior to JP Morgan he spent eight years at Goldman Sachs where he worked on Goldman’s legendary SecDB.


About the Author:

is a second-year MBA Candidate at The Wharton School, where he is part of the Wharton FinTech Podcast team. He has a passion for the nerdy corners of financial services, venture capital, and all things FinTech. Don’t hesitate to reach out with questions, comments, feedback, and opportunities at ajanss@wharton.upenn.edu.


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