Technical paper/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.
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
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.
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.
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.
Model risk quantification for machine learning models in credit risk
This paper analyses bank-specific model risk measurement methods with a focus on implemented model risk rating solutions for MLMs and discusses challenges faced by the validation function.
The fate of zombie firms: prediction, determinants and exit paths
This paper examines how machine learning and statistical methods may be used to predict whether or not zombie firms will escape their fate as zombies.
Enhancing default prediction in alternative lending: leveraging credit bureau data and machine learning
The authors apply machine learning techniques to credit bureau data and loan-specific variables to improve default prediction in the alternative lending sector.
Supervised similarity for high-yield bonds
Quantum cognition ML is used to identify tradable alternatives for high-yield corporate bonds
How magic a bullet is machine learning for credit analysis? An exploration with fintech lending data
The authors apply machine learning techniques to consumer fintech loan data to assess how such techniques can improve out-of-sample default prediction.
Investment decisions driven by fine-tuned large language models and uniform manifold approximation and projection-supported clustering and hierarchical density-based spatial clustering
The author proposes an investment strategy using LLMs and text from social media posts and business and economic news and demonstrate that the strategy outperforms the chosen benchmark.
The prediction of mortgage prepayment risks in the early stages of loan origination: a machine learning approach
The authors put forward a machine learning model for the prediction of mortgage prepayment risks at the loan origination phase.
Advanced visualization for the quant strategy universe: clustering and dimensionality reduction
The authors present a novel visualisation model, based on 5000 quantitative investment strategies, which can identify nonlinear relationships and clustering strategies with similar risk factor exposures.
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.
A model combining Optuna and the light gradient-boosting machine algorithm for credit default forecasting
The authors put forward a default prediction model designed to make the analysis of complex, highly dimensional and imbalanced real-world bank data easier.
Quantum cognition machine learning: financial forecasting
A new paradigm for training machine learning algorithms based on quantum cognition is presented
Analyzing credit risk model problems through natural language processing-based clustering and machine learning: insights from validation reports
The authors use clustering and machine learning techniques to analyze validation reports, providing insights to the development, implementation and maintenance of credit risk models.