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.
Novel interpretability method could spur greater use of ReLU neural networks for credit scoring
Machine learning could help with loan decisions – but only if banks can explain how it works. And that’s not easy
Data gaps and potential biases must be accounted for in approaches to tackling money laundering
Dealers say agencies’ request for info could prompt new rules that stifle model innovation
The authors propose a new modeling approach that incorporates trend, seasonality and weather conditions as explicative variables in a shallow neural network with an autoregressive feature.
Quants unveil new technique for controlling extrapolation by neural networks
Introducing a new technique to control the behaviour of neural networks
New research addresses fundamental issues with ANN approximation of pricing models
Addressing model calibration and the issue of no-arbitrage in a deep learning approach
Deep learning is opening up new frontiers in financial engineering and risk management
In this paper, the authors study an evolutionary framework for the optimization of various types of neural network structures and parameters.
Energy firms explore how artificial intelligence can boost returns
Standard Chartered quant proposes machine-learning technique to better capture rate dynamics
Quant speaks of collaboration with Nasa and machine-learning algos for yield curves
Artificial neural networks can replace PCA for yield curves analysis