Neural networks
A credit card fraud detection model based on a stacked temporospatial graph attention residual network
The authors put forward a model based on a stacked temporospatial graph attention residual network dedicated to credit card fraud detection.
Green risk identification and risk measurement in fintech: a particle swarm optimization fuzzy analytic hierarchy process and sparrow search algorithm quantile regression neural network approach
Robust financial calibration: a Bayesian approach for neural stochastic differential equations
AI as pricing law
Probabilistic classification with discriminative and generative models: credit-scoring application
The author investigates how probabilistic classification can be used to enhance credit-scoring accuracy, offering a robust means for assessing model performance under various reliability criteria
Deep learning alpha signals from limit order books
An analysis on network architectures applied to limit order book data is presented
Machine learning and a Hamilton–Jacobi–Bellman equation for optimal decumulation: a comparison study
This paper ascertains a decumulation strategy for the holder of a defined contribution pension plan with an approach based on neural network optimization.
On deep portfolio optimization with stocks, bonds and options
The authors put forward a neural-network machine learning algorithm for time-inconsistent portfolio optimization.
An explicit scheme for pathwise cross valuation adjustment computations
The authors put forward a simulation/regression scheme for a class of anticipated backward stochastic differential equations, where the coefficient entails a conditional expected shortfall of the martingale part of the solution.
Bank FX market-makers ramp up AI usage
Barclays applies tech to predictions, while HSBC and ING look at pricing accuracy
Why AI will never predict financial markets
Laws that govern swings in asset prices are beyond statistical grasp of machine learning technology, argues academic Daniel Bloch
The power of neural networks in stochastic volatility modeling
The authors apply stochastic volatility models to real-world data and demonstrate how effectively the models calibrate a range of options.
Quant of the year: Julien Guyon
Risk Awards 2025: Volatility modeller par excellence (and football fan) achieved breakthrough with joint calibration of S&P and Vix options
Rising star in quant finance: Milena Vuletić
Risk Awards 2025: Machine learning-based volatility model confounds sceptics
Research on the multifractal volatility of Chinese banks based on the synthetic minority oversampling technique, edited nearest neighbors and long short-term memory
The authors propose the SMOTEENN-LSTM method to predict risk warnings for Chinese banks, demonstrating the improved performance of their model relative to commonly used methods.
Quantifying credit portfolio sensitivity to asset correlations with interpretable generative neural networks
This study introduces a method for assessing the impact of asset correlations on credit portfolio value-at-risk using variational autoencoders (VAEs), offering a more interpretable approach than previous methods and improving model interpretability.
Can machine learning help predict recessions? Not really
Artificial intelligence models stumble on noisy data and lack of interpretability
Neural joint S&P 500/VIX smile calibration
A one-factor stochastic local volatility model can solve the joint calibration problem
JP Morgan pulls plug on deep learning model for FX algos
US bank turns to less complex models that are easier to explain to clients
Overfitting in portfolio optimization
The authors measure the performance of sample-based rolling-window neural network (NN) portfolio optimization strategies and demonstrate that correctly set up NN-based strategies can outperform the 1/N strategy.
Neural stochastic differential equations for conditional time series generation using the Signature-Wasserstein-1 metric
Using conditional neural stochastic differential equations, the authors propose a means to improve the efficiency of generative adversarial networks and test their model against other classical approaches.
An optimal control strategy for execution of large stock orders using long short-term memory networks
Using a general power law in the Almgren and Chriss model and real data, the authors simulate the execution of a large stock order with an appropriately trained LSTM network.
Robust pricing and hedging via neural stochastic differential equations
The authors propose a model called neural SDE and demonstrate how this model can make it possible to find robust bounds for the prices of derivatives and the corresponding hedging strategies.
Rising star in quantitative finance: Sigurd Emil Rømer
Risk Awards 2023: Doctoral dissertation outlines more efficient way to simulate rough volatility models