What is the QuantMinds Digital Week?
Three days of exclusive webinars delivered by leading quants.
We will gather the community online to discuss COVID-19 and key topics for quants such as machine learning, and IBOR reform.
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Here's what's happening this week:
Machine learning asset allocation
Presented by Marcos Lopez de Prado, CIO, True Positive Technologies
Convex optimisation solutions tend to be unstable, to the point of entirely offsetting the benefits of optimisation. For example, in the context of financial applications, it is known that portfolios optimised in sample often underperform the naïve (equal weights) allocation out of sample. This instability can be traced back to two sources:
- noise in the input variables; and
- signal structure that magnifies the estimation errors in the input variables.
There is abundant literature discussing noise induced instability. In contrast, signal induced instability is often ignored or misunderstood. We introduce a new optimisation method that is robust to signal induced instability.
Neural networks with asymptotics control
Presented by Alexandre Antonov, Chief Analyst, Danske Bank
Artificial neural networks (ANNs) have recently been proposed as accurate and fast approximators in various derivatives pricing applications. ANNs typically excel in fitting functions they approximate at the input parameters they are trained on, and often are quite good in interpolating between them. However, for standard ANNs, their extrapolation behaviour – an important aspect for financial applications – cannot be controlled due to complex functional forms typically involved. We overcome this significant limitation and develop a new type of neural networks that incorporate large-value asymptotics, when known, allowing explicit control over extrapolation.
Corona-immunise your portfolio: from global macro trends to corona-proof quant investing
Presented by Svetlana Borovkova, Associate Professor Of Quantitative Finance, Vrije Universiteit Amsterdam
One can only speculate how the world will look like after the coronavirus epidemic. But some of the macroeconomic and consumer trends we can observe already now. Using alternative data such as sentiment and search behaviour, I will outline several emerging trends and translate them into scenarios, which can be used to assess stock portfolios in terms of their resistance in the post-corona world. I will address factors such as quality and sustainability, but also other, new post-corona factors will play important role in immunising your stock portfolio against corona effects. Finally, I will touch upon risk and modelling challenges of recently observed negative oil prices.
Looking forward to backward-looking rates: completing the generalised forward market model
Presented by Fabio Mercurio, Global Head of Quant Analytics, Bloomberg L.P.
In this talk, we show how the generalised forward market model (FMM) introduced by Lyashenko and Mercurio (2019) can be extended to make it a complete term-structure model describing the evolution of all points on a yield curve, as well as that of the bank account. The ability to model the bank account, in addition to the forward curve, is going to be of crucial importance once Libor rates are replaced with setting-in-arrears rates in derivative and cash contracts, where fixings are defined in terms of the realised bank account values.