Articles & Video
Staying afloat: How is quant finance changing under Covid-19?
Is the worst behind us? Or is it still to come?
Truly Explainable AI: Putting the “Cause” in “Because”
There are two serious problems with state-of-the-art machine learning approaches.
Model robustification and the future of quant finance: an interview with J.D. Opdyke, Allstate
Were your models caught by surprise when the pandemic hit? J.D. Opdyke, Allstate, explains how he built a robust framework and shares key learnings from this year.
How is reinforcement learning different from un/supervised learning?
Marshall Chang, Founder and CIO, A.I. Capital Management, shares how to develop and deploy RL trading algorithms.
Beyond Weisfeiler-Lehman: using substructures for provably expressive graph neural networks
How powerful are graph neural networks?
Five issues quants need to address in 2020
QuantMinds editor-in-chief Vincent Beard now summarises the five key conundrums on quants’ minds this year.
Fourier-based methods for the management of complex insurance products
Modelling for variable annuities products – how could the financial and insurance risks be integrated better?
Differential machine learning
Unreasonably effective pricing and risk approximation by automatic differentiation (AAD) combined with machine learning (ML)
Can models predict black swan events?
Maurizio Garro, Senior Lead IBOR Transition Programme, Lloyds Bank, shares his experience building robust models to predict the combined impact of risk factors.
A conversation with “Quant of the Year 2019” Marcos Lopez de Prado
Marcos Lopez De Prado opens up about the issues of econometric investments and how asset management needs new technological innovations.
Innovations in quant trading strategies and modelling
People and markets have changed hugely, and quants need to adapt to this new equilibrium. Therefore, innovation, both technological and strategic, are in the spotlight in this QuantMinds eMagazine.
Unexpected bug in your machine learning: how can you recover?
What went wrong when ML doesn’t work as expected, and how can you mitigate the model risk?
Machine learning with quantum annealing
2018 Quant of the Year Alexei Kondratyev shares his passion for machine learning and quantum computing.
Learn this about options: Pricing is hedging
PhD Candidate at École Polytechnique Marcos Costa Santos Carriera takes a look at applying Q-Learning to option pricing, and its impact on hedging strategies.
What does 2020 have in store for quant finance?
Understanding new trends, challenges, and solutions
From emotions to decisions – a framework for using big data in portfolio management
A case study in using alternative data by SESAMm and La Française Investment Solutions.
What’s the one thing that will disrupt quant finance the most?
QuantMinds International thought leaders talk about the biggest game-changers in the industry!
Artificial intelligence, machine learning, and data in quant finance
In this compilation of articles, FutureQuantMinds explore AI, ML, and the impact of data on quant finance
What’s the best language for machine learning?
Python vs R – Erdem Ultanir, Quantitative Credit Risk Analytics Lead at Barclays, evaluates the two languages
10 ways AI can supercharge trading
To what extent will artificial intelligence and machine learning alter the fundamentals of investing?