Machine learning
Machines can read, but do they understand?
A novel NLP application built on a Google transformer model can help predict ratings transitions
Insider dealing: amping up surveillance measures
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…
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
AI helps one investor screen targets against UN ethical goals
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.
Wells touts new explainability technique for AI credit models
Novel interpretability method could spur greater use of ReLU neural networks for credit scoring
Building forward-looking scenarios: why you’re doing it wrong
Rick Bookstaber and colleagues describe a process for constructing effective scenarios
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
Fast pricing of American options under variance gamma
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…
What can we learn from what a machine has learned? Interpreting credit risk machine learning models
This paper studies a few popular machine learning models using LendingClub loan data, and judges these on performance and interpretability
EBA to consult on banks’ machine learning use
Watchdog will set out stance on ML-based capital models amid conflicting guidance from supervisors
The case for reinforcement learning in quant finance
The technology behind Google’s AlphaGo has been strangely overlooked by quants
How XVA quants learned to trust the machine
Initial scepticism about using neural networks for derivatives pricing is giving way to enthusiasm
Goal-based wealth management with reinforcement learning
A combination of machine learning techniques provides multi-period portfolio optimisation
How algos are helping inflation-wary investors
Buy-siders look to machine learning for clues on the effect of rising prices on portfolios
Quants turn to machine learning to unlock private data
Replication could allow financial firms to use – and monetise – data that was previously off-limits
Deep XVAs and the promise of super-fast pricing
Intelligent robots can value complex derivatives in minutes rather than hours
Banks fear Fed crackdown on AI models
Dealers say agencies’ request for info could prompt new rules that stifle model innovation
Synthetic data enters its Cubist phase
Quants are using the theory of rough paths to distil the essence of financial datasets
Quantum computing experts voice explainability fears
Risk Live: big speed-ups for quantum-powered models could prompt bigger questions from regulators
Generating financial markets with signatures
Signatures can provide the synthetic data to train deep hedging strategies
Acadian builds ‘green screen’ to auto-filter ESG phoneys
$110 billion quant investor creates automated system to spot greenwashers