Machine learning
The role of personal credit in small business risk assessment: a machine learning approach
The authors investigate how personal credit data can be combined with business-level and tradeline variables in a machine learning framework to enhance default prediction.
Machine learning in oil market volatility forecasting: the role of feature selection and forecast horizon
This paper investigates oil market volatility prediction, showing financial variables to dominate short-horizon forecasting, while macroeconomic and sentiment factors increase in importance at longer horizons
Technical trading versus buy and hold: a framework using common indicators in the US stock market
The author proposes a technical trading framework which incorporates trend-following, conditional active trading, stop-loss mechanisms and trading volume in formulating strategies
Almost two-thirds of banks now run XVAs on cloud
Risk Benchmarking study finds a majority of big dealers tapping cloud capacity, some exclusively, with others migrating
Supervised similarity for firm linkages
Quantum fidelity is used to capture dependency structures in equity
AI as pricing law
A neural network tailored to financial asset pricing principles is introduced
Deep self-consistent learning of local volatility
This paper offers an algorithm for calibrating local volatility from market option prices using deep self-consistent learning, by approximating both market option prices and local volatility using deep neural networks.
The AI explainability barrier is lowering
Improved and accessible tools can quickly make sense of complex models
BlackRock, BGI and the big quant pivot
How the world’s largest asset manager revived the fortunes of its struggling west coast unit
Rates investors unmoved by stories of AI bliss, or doom
Research shows downward moves in US Treasury yields around generative AI model releases
Why know-it-all LLMs make second-rate forecasters
A bevy of experiments suggests LLMs are ill-suited for time-series forecasting
Addressing climate-related risks in banking: a framework for sustainable risk management and regulatory alignment
This paper puts forward a dual-layer approach to climate risk management with utilises root cause-based analysis and severity assessments to prioritize and address climate-related risks.
A comprehensive explainable approach for imbalanced financial distress prediction
The authors suggest an explainable machine learning method for imbalanced financial distress prediction which uses extreme gradient boosting.
Trump’s FX impact: a tale of two terms
Traders say Trump version 2.0 is already proving a much trickier task to manage than the original, and have had to adapt
Removing the barriers to AI success
Addressing challenges of data quality and governance to scale AI for competitive advantage
The AI bot that left the garage
Senior operational risk exec explains how hidden third-party feature could lead to systemic risk
Quants use AI to shush noisy order-book data
Signals from clusters of seemingly informed trading perform better, researchers say
Building reliable and successful LLM-based workflows
How AI is reshaping analytics, compliance and modelling in finance
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.
Stock-picking bots and models that don’t trade: AI at Vontobel
Early experiments are already bearing fruit, in sometimes surprising ways
Podcast: Villani and Musaelian on a quantum boost for machine learning
Classical methods struggle with highly dimensional problems. Quantum cognition takes a different approach, as hedge fund duo explain