Which sessions and speakers should you absolutely not miss in Barcelona? We asked J.D Opdyke, Chief Analytics Officer at Sachs Capital Group Asset Management to recommend the speakers and presentations you have to see and which sessions would make up his ideal conference day.
One that all attendees cannot miss is the opening talk by Bruno Dupire, “Optimal, Super, Sub and Deep Hedges.” There are certainly many reasons Dr. Dupire should lead off as QuantMinds’ keynote speaker, but first among them must be his longstanding status as a top expert in the field whose seminal contributions are not only extremely wide-ranging and widely used in applied settings, but also deep in their rigor and analytical sophistication. This talk is sure to be both deep and analytically substantive (no overly general fluff pieces), but also broad enough to be relevant to wide ranging audiences of serious quants. This will undoubtedly be a great way to kick off the event!
Jonathan Chan’s presentation on “Portfolio optimization with options and reinforcement learning” is an absolute “must see.” Having read his related paper on this with Thomas Huckle, the work is intriguing in 1. the breadth of the topic – it tackles a big, largely unsolved problem; 2. the very real need it solves in a broad investing space; 3. the analytical rigor it brings to bear on the problem; and 4. the applied nature and useability of the proferred solution. I have no doubt its derivations will be useful and used well beyond the scope of the talk/paper, and the evolution of this work going forward is a “can’t miss” for anyone even remotely related to the options space.
One that all attendees cannot miss is the opening talk by Bruno Dupire, “Optimal, Super, Sub and Deep Hedges.” There are certainly many reasons Dr. Dupire should lead off as QuantMinds’ keynote speaker, but first among them must be his longstanding status as a top expert in the field whose seminal contributions are not only extremely wide-ranging and widely used in applied settings, but also deep in their rigor and analytical sophistication.
Similar reasons to attend apply to Alexandre Antonov’s presentation on “Efficient Multidimensional Regression”. This is a very important problem posing challenges across a very broad range of application in quantitative finance. Issues of scalability and exponential data growth guarantee that the very real need for efficient solutions here will remain with us indefinitely.
If past is prologue, Antonov’s extensive history of presenting such useful, applied solutions in these settings guarantees this as a “must see.”
If you’re looking for blockchain insights you cannot beat Alex Lipton’s extensive historical knowledge and deep analytical expertise and experience.
He is a pioneer in this (and many other) sub-fields of quantitative finance and his talk on “Blockchain and Distributed Ledgers in Retrospective and Perspective” will almost certainly encompass wide-ranging applicability even beyond its already broad bounds.
Vladimir Piterbarg’s presentation on “Alternatives to deep neural networks for function approximations in finance” is a critically important topic generally, but especially for those who have struggled with the very real (and often insidious) limitations of neural networks in this space. Piterbarg’s work should help to bring the pendulum back towards the rational middle ground when it comes to the arguable over-adoption of neural network approaches by finance quants. Not surprisingly, this appears to be joint work with Alexandre Antonov.
Chris Kenyon is a longtime applied quant with a history of tackling very tough analytical challenges and providing very practical, useable solutions to them.
His talk on “CO2eVA: pricing carbon externalities transition” should be no exception, and the range of application of its work will in all likelihood extend well beyond pricing carbon specifically.
Cassie Kozyrkov is widely known for her pragmatic and accessible approach to implementing data science/machine learning at scale, without sacrificing the essential analytic rigor that so many managers in this space, to the detriment of their teams and institutions, drift away from. The second point here is critical, and why her talk on “Why do businesses fail on machine learning?” should not disappoint: expect it to provide high-level yet actionable insights into identifying and avoiding pitfalls that even seasoned quant veterans can easily miss, while not losing focus on the analytical rigor, rather than management style, that must remain the primary driver of any successful machine learning initiative.
Finally, for those interested in ESG, Aymeric Kalife’s talk, “Reconciling Sustainability with Profitability and Customers’ Risk Appetites” should not be missed (catch Dr. Kalife’s recent related article in QuantMinds Emagazine).
Dr. Kalife brings the rigor of a serious finance quant to a relevant and popular topic desperately in need of such, and attendees will no doubt gain not only insights but also tools and approaches needed when ‘reconciling’ sustainability and profitability, if and where and when needed.
The best day at QuantMinds would begin with making two into one.
First, I would begin with a full day of John Hull’s master classes. Nobody can go wrong attending these: the most seasoned quants will come away much richer for the experience. This would be followed by a full day of the following schedule:
Bruno Dupire’s talk on “Optimal, Super, Sub and Deep Hedges.”
Jonathan Chan’s presentation on “Portfolio optimization with options and reinforcement learning”
Alexandre Antonov’s talk on “Efficient Multidimensional Regression”
Alex Lipton’s presentation, “Blockchain and Distributed Ledgers in Retrospective and Perspective”
Vladimir Piterbarg’s talk on “Alternatives to deep neural networks for function approximations in finance” (joint work with Alexandre Antonov)
Chris Kenyon’s talk on “CO2eVA: pricing carbon externalities transition”
Cassie Kozyrkov’s presentation, “Why do businesses fail on machine learning?”
And finally, Aymeric Kalife’s talk, “Reconciling Sustainability with Profitability and Customers’ Risk Appetites”
This would be followed by an evening of dining, networking, and social gatherings.
If I could only attend/watch one session at QuantMinds International 2022, I would make two into one (sorry, as a quant in the quant candystore that is QuantMinds, one simply cannot be boxed in…! but this is why QuantMinds is the ONE conference that should be at the top of all finance quants’ lists): Alexandre Antonov’s presentation on “Efficient Multidimensional Regression”, and Jonathan Chan’s presentation on “Portfolio optimization with options and reinforcement learning”
Jonathan Chan’s presentation on “Portfolio optimization with options and reinforcement learning” is an absolute “must see.” Having read his related paper on this with Thomas Huckle, the work is intriguing in 1. the breadth of the topic – it tackles a big, largely unsolved problem; 2. the very real need it solves in a broad investing space; 3. the analytical rigor it brings to bear on the problem; and 4. the applied nature and useability of the preferred solution. I have no doubt its derivations will be useful and used beyond the scope of the talk/paper, and the evolution of this work going forward is a “can’t miss” for anyone even remotely related to the options space.
Similar reasons to attend apply to Alexandre Antonov’s presentation on “Efficient Multidimensional Regression”. This is a very important problem posing challenges across a very broad range of application in quantitative finance.
Issues of scalability and exponential data growth guarantee that the very real need for efficient solutions here will remain with us indefinitely.
