A causal machine learning algorithm is used to estimate trades’ price impact
The authors construct a machine-learning-based early-warning system to predict, one year in advance, risks of systemic distress and demonstrate factors which can predict corporate distress.
The authors put forward a publicly available computational template for machine learning, named mlOSP, which presents a unified numerical implementation of RMC approaches for optimal stopping.
MUFG Securities quant uses variational inference to control the mid volatility of options
Barron-DiCamillo also urges regulators to work with industry best practice, not against it
An ML-based algorithm that provides implied volatilities from bid-ask prices is proposed
Microsoft’s analog technology twice as accurate compared to IBM’s quantum kit in Barclays experiment
With smarter large language models, quant grads risk turning into AI-assisted slackers, writes Gordon Lee
Based on a comprehensive sample, the authors benchmark machine learning models in the prediction of financial distress of publicly traded US firms, with gradient-boosted tress outperforming other models in one-year-ahead forecasts.
Risk Live: Regulator concerned rapid AI adoption will favour incumbent vendors; aims to launch sandbox
Swiss bank tested various use cases in the trading business before giving up on the technology
Network analysis helps quant shop spot concentration and contagion risks
Sovereign credit risk modeling using machine learning: a novel approach to sovereign credit risk incorporating private sector and sustainability risks
The authors investigate the effect of spillover effects from private sector risks on sovereign debt risk and the impact of rising sustainability risks on sovereign credit risk using the XGBoost classification algorithm and model interpretability…
Thomas Oliver, head of model validation at Quantifi, explores how the model risk management (MRM) landscape is changing in response to geopolitical uncertainty, increased concerns over counterparty risk, rising interest rates and other related challenges
New large language model aims to supercharge Terminal’s ability to provide sentiment, charting and search
Dutch court case pressures supervisors to accept new tech; explainability the key challenge
Firms look to machine learning and natural language processing to gain advantage over peers
An attention-based deep learning model for trading is presented
Oxford-Man Institute is among those asking: could algorithms gang up and squeeze customers?
The authors summarise ways that machine learning can help categorize textual descriptions of operational loss events into Basel II event types.
Forecasting the loss given default of bank loans with a hybrid multilayer LGD model by extending multidimensional signals
The authors employ signaling theory and machine learning methods to investigate loss given default predictions of commercial banks and propose a method to improve the accuracy of these predictions.
But supervisors cautiously welcome next-gen model risk management
Using reinforcement learning to help replicate asset managers' allocation strategy
The author outlines characteristics of machine learning classifiers, compares methods for dealing with imbalanced data issues, and proposes terms of best practice in model development, evaluation, and validation.