Tenfold increase in web-enabled devices via 5G and IoT means explosion in cyber threats, says official
Quants use neural networks to upgrade classic options pricing model
Closely watched arbitrage spread poor predictor of a merger deal’s success, quant firm finds
Superior computational grunt of neural networks is attractive to lenders. Lack of explainability is the downside
This study proposes an advanced credit evaluation method for nonperforming consumer loans, which may serve as a new investment opportunity in the post-pandemic era.
Quants achieve more speed by reducing number of dimensions in price calculations
This paper surveys the impressively broad range of machine learning methods and application areas for credit risk.
Comprehensive Capital Analysis and Review consistent yield curve stress testing: from Nelson–Siegel to machine learning
This paper develops different techniques for interpreting yield curve scenarios generated from the FRB’s annual CCAR review.
Differential PCA is introduced to reduce the dimensionality in derivative pricing problems
Firms adapt backtests and tread lightly to address “huge” overfitting risk, magnified by scarce data
A novel NLP application built on a Google transformer model can help predict ratings transitions
Joe Schifano, global head of regulatory affairs at Eventus, examines how volatility resulting from the Covid‑19 pandemic has made markets more susceptible to insider dealing activity, prompting regulators to urge firms to reinforce surveillance measures…
TCA methodologies that ignore partial fills “might be off by 20% to 30%”
News feeds are factored into models to predict credit events
PanAgora develops two-stage process that aims to weed out the greenwashers
An interpretable Comprehensive Capital Analysis and Review (CCAR) neural network model for portfolio loss forecasting and stress testing
This paper proposes an interpretable nonlinear neural network model that translates business regulatory requirements into model constraints.
Novel interpretability method could spur greater use of ReLU neural networks for credit scoring
Rick Bookstaber and colleagues describe a process for constructing effective scenarios
Machine learning could help with loan decisions – but only if banks can explain how it works. And that’s not easy
This research develops a new fast and accurate approximation method, inspired by the quadratic approximation, to get rid of the time steps required in finite-difference and simulation methods, while reducing error by making use of a machine learning…
This paper studies a few popular machine learning models using LendingClub loan data, and judges these on performance and interpretability
Watchdog will set out stance on ML-based capital models amid conflicting guidance from supervisors
The technology behind Google’s AlphaGo has been strangely overlooked by quants
Initial scepticism about using neural networks for derivatives pricing is giving way to enthusiasm