Deep learning
Machines can read, but do they understand?
A novel NLP application built on a Google transformer model can help predict ratings transitions
Deep learning for discrete-time hedging in incomplete markets
This paper presents several algorithms based on machine learning to solve hedging problems in incomplete markets.
Podcast: NYU’s Kolm on transaction costs and machine learning
TCA methodologies that ignore partial fills “might be off by 20% to 30%”
NLP and transformer models for credit risk
News feeds are factored into models to predict credit events
Wells touts new explainability technique for AI credit models
Novel interpretability method could spur greater use of ReLU neural networks for credit scoring
Show your workings: lenders push to demystify AI models
Machine learning could help with loan decisions – but only if banks can explain how it works. And that’s not easy
How XVA quants learned to trust the machine
Initial scepticism about using neural networks for derivatives pricing is giving way to enthusiasm
Deep XVAs and the promise of super-fast pricing
Intelligent robots can value complex derivatives in minutes rather than hours
Synthetic data enters its Cubist phase
Quants are using the theory of rough paths to distil the essence of financial datasets
Generating financial markets with signatures
Signatures can provide the synthetic data to train deep hedging strategies
Technology innovation of the year: Scotiabank
Risk Awards 2021: New risk engine can run nearly a billion XVA calculations per second
Solving final value problems with deep learning
Pricing vanilla and exotic options with a deep learning approach for PDEs
Setting boundaries for neural networks
Quants unveil new technique for controlling extrapolation by neural networks
Degree of influence: volatility shakes markets and quant finance
Volatility and machine learning were among the top research areas for quants this year
Machine learning hedge strategy with deep Gaussian process regression
An optimal hedging strategy for options in discrete time using a reinforcement learning technique
Detecting changes in asset co-movement using autoencoders
ARR aims to anticipate volatility patterns to provide signals for risk management and trading
Three adjustments in calibrating models with neural networks
New research addresses fundamental issues with ANN approximation of pricing models
Deep learning calibration of option pricing models: some pitfalls and solutions
Addressing model calibration and the issue of no-arbitrage in a deep learning approach
Podcast: Horvath and Lee on market generator models
Quants explain the application of the latest techniques
At Numerai, real-world figures need not apply
AI hedge fund CEO sees the light in black-box technology
The market generator
A generative neural network is proposed to create synthetic datasets that mantain the statistical properties of the original dataset
Interpretability of neural networks: a credit card default model example
Recently developed techniques aimed at answering interpretability issues in neural networks are tested and applied to a retail banking case
Ex-Credit Suisse quants embrace machine learning
Founders of XAI Asset Management grapple with unsupervised learning and the problems of explainability
Goldman improves execution ‘by 50%’ with new algos
Bank uses neural networks and other AI tools to cut slippage in stock trading