Customer churn prediction for commercial banks using customer-value-weighted machine learning models
In this paper the authors propose a framework to address the issue of customer churn prediction, and they quantify customer values with the use of an improved customer value model.
This paper presents a stochastic optimization framework for integrating time-varying factor covariance models in a risk-based portfolio optimization setting.
A multi-step path is forecast using deep learning and parallel computing
Machine learning method edges regression techniques in linking nonlinearities among delinquent borrowers
Quants use neural networks to upgrade classic options pricing model
Superior computational grunt of neural networks is attractive to lenders. Lack of explainability is the downside
The P&L distribution of a complex derivatives portfolio is computed via deep learning
Differential PCA is introduced to reduce the dimensionality in derivative pricing problems
This paper presents several algorithms based on machine learning to solve hedging problems in incomplete markets.
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.
Machine learning could help with loan decisions – but only if banks can explain how it works. And that’s not easy
Initial scepticism about using neural networks for derivatives pricing is giving way to enthusiasm
Buy-siders look to machine learning for clues on the effect of rising prices on portfolios
Intelligent robots can value complex derivatives in minutes rather than hours
Signatures can provide the synthetic data to train deep hedging strategies
Traditionally quants have learnt to pick data apart. Soon they might spend more time making it up
The authors devise a neural network-based compression/completion methodology for financial nowcasting.
Risk Awards 2021: new risk engine can run nearly a billion XVA calculations per second
Quants unveil new technique for controlling extrapolation by neural networks
Introducing a new technique to control the behaviour of neural networks
Differential machine learning produces results “thousands of times faster and with similar accuracy”
A derivative pricing approximation method using neural networks and AAD speeds up calculations
A k-means++-improved radial basis function neural network model for corporate financial crisis early warning: an empirical model validation for Chinese listed companies
This paper aims to simplify the early warning model for financial crises by collecting and analyzing the financial data of Chinese special treatment (ST) companies, normally listed companies and cancel special treatment (CST) companies.
Quants explain the application of the latest techniques