Machine learning
Getting more for less: better A / B testing via causal regularisation
A causal machine learning algorithm is used to estimate trades’ price impact
Understanding and predicting systemic corporate distress: a machine-learning approach
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.
Toward a unified implementation of regression Monte Carlo algorithms
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.
How a machine learning model closed a hidden FX arbitrage gap
MUFG Securities quant uses variational inference to control the mid volatility of options
Citi cyber chief says AI providing new weapons in hacking wars
Barron-DiCamillo also urges regulators to work with industry best practice, not against it
Obtaining arbitrage-free FX implied volatility by variational inference
An ML-based algorithm that provides implied volatilities from bid-ask prices is proposed
Optical computer beats quantum tech in tricky settlement task
Microsoft’s analog technology twice as accurate compared to IBM’s quantum kit in Barclays experiment
The chatbot and the quant: GPT shakes finance education
With smarter large language models, quant grads risk turning into AI-assisted slackers, writes Gordon Lee
Benchmarking machine learning models to predict corporate bankruptcy
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.
FCA may offer its market data to surveillance tech start-ups
Risk Live: Regulator concerned rapid AI adoption will favour incumbent vendors; aims to launch sandbox
UBS found no advantage in quantum computing – ex data chief
Swiss bank tested various use cases in the trading business before giving up on the technology
How Man Numeric found SVB red flags in credit data
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…
Model risk management is evolving: regulation, volatility, machine learning and AI
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
BloombergGPT: Terminal giant enters the LLM race
New large language model aims to supercharge Terminal’s ability to provide sentiment, charting and search
OK regulator? How AI became respectable for AML controls
Dutch court case pressures supervisors to accept new tech; explainability the key challenge
The haves and the ‘have bots’: can AI give vol forecasters an edge?
Firms look to machine learning and natural language processing to gain advantage over peers
Momentum transformer: an interpretable deep learning trading model
An attention-based deep learning model for trading is presented
Can algos collude? Quants are finding out
Oxford-Man Institute is among those asking: could algorithms gang up and squeeze customers?
Machine learning for categorization of operational risk events using textual description
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.
Bot’s job? Quants question AI’s model validation powers
But supervisors cautiously welcome next-gen model risk management
Asset allocation with inverse reinforcement learning
Using reinforcement learning to help replicate asset managers' allocation strategy
Imbalanced data issues in machine learning classifiers: a case study
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.