Machine learning
Demand deposit balance prediction models under the interest rate risk in the banking book guidelines: an empirical analysis integrating time-series models and machine learning predictions in Mexican banks
The authors analyze the interest rate risk in the banking book regulations, arguing that financial institutions must develop robust models for forecasting demand deposit balances while adhering to regulatory guidelines.
How investment firms are innovating with quantum technology
Banks and asset managers should be proactive in adopting quantum-safe strategies
Baruch, Princeton cement duopoly in 2026 Quant Master’s Guide
Columbia jumps to third place, ETH-UZH tops European rivals
Best use of machine learning/AI: ActiveViam
Bringing machine intelligence to real-time risk analytics
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.
Quantcast Master’s Series: Jack Jacquier, Imperial College London
A shift towards market micro-structure and ML has reshaped the programme
Interpretable machine learning for default risk prediction in stress testing
This paper proposes a benchmark model which can be used to predict the forward-looking probability of default of a real-world credit card portfolio.
XVA desks prioritise core tech upgrades over AI
Vendor upgrades, cloud-native rebuilds and sensitivities tooling dominate 2026 budget road maps
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