Copertina

Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation

Fuente: Wiley Open Access

Salvato in:

Dettagli Bibliografici
Autori principali:	Jiaming Liu, Xuemei Zhang, Haitao Xiong
Natura:	Artículo Open Access
Pubblicazione:	Wiley 2024
Soggetti:	Journal of Forecasting
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

An ensemble model for stock index prediction based on media attention and emotional causal inference
di: Juanjuan Wang, et al.
Pubblicazione: (2024)

Gated recurrent unit network: A promising approach to corporate default prediction
di: Michał Thor, et al.
Pubblicazione: (2024)

Two‐stage credit risk prediction framework based on three‐way decisions with automatic threshold learning
di: Yusheng Li, et al.
Pubblicazione: (2024)

Explainable machine learning techniques based on attention gate recurrent unit and local interpretable model‐agnostic explanations for multivariate wind speed forecasting
di: Lu Peng, et al.
Pubblicazione: (2024)

Research on occupant injury severity prediction of autonomous vehicles based on transfer learning
di: Na Yang, et al.
Pubblicazione: (2024)

Design of a precise ensemble expert system for crop yield prediction using machine learning analytics
di: Deeksha Tripathi, et al.
Pubblicazione: (2024)

Forecasting realized volatility of crude oil futures prices based on machine learning
di: Jiawen Luo, et al.
Pubblicazione: (2024)

A novel hybrid forecasting model with feature selection and deep learning for wind speed research
di: Xuejun Chen, et al.
Pubblicazione: (2024)

Forecasting healthcare service volumes with machine learning algorithms
di: Dong‐Hui Yang, et al.
Pubblicazione: (2024)

Forecasting corporate financial performance with deep learning and interpretable ALE method: Evidence from China
di: Longyue Liang, et al.
Pubblicazione: (2024)

Forecasting and Pricing in the Carbon Credits Market
di: Yiyang Chen, et al.
Pubblicazione: (2026)

Using deep (machine) learning to forecast US inflation in the COVID‐19 era
di: David Stoneman, et al.
Pubblicazione: (2024)

Forecasting Bitcoin returns: Econometric time series analysis vs. machine learning
di: Theo Berger, et al.
Pubblicazione: (2024)

Structured multifractal scaling of the principal cryptocurrencies: Examination using a self‐explainable machine learning
di: Foued Saâdaoui, et al.
Pubblicazione: (2024)

A novel semisupervised learning method with textual information for financial distress prediction
di: Yue Qiu, et al.
Pubblicazione: (2024)

IWSL Model: A Novel Credit Scoring Model With Interpretable Features for Consumer Credit Scenarios
di: Runchi Zhang, et al.
Pubblicazione: (2025)

Embedding the weather prediction errors (WPE) into the photovoltaic (PV) forecasting method using deep learning
di: Adela Bâra, et al.
Pubblicazione: (2024)

Portfolio management based on a reinforcement learning framework
di: Wu Junfeng, et al.
Pubblicazione: (2024)

Volatility forecasting incorporating intraday positive and negative jumps based on deep learning model
di: Yilun Zhang, et al.
Pubblicazione: (2024)

Harnessing volatility cascades with ensemble learning
di: Mingmian Cheng
Pubblicazione: (2024)

Disciplining growth‐at‐risk models with survey of professional forecasters and Bayesian quantile regression
di: Milan Szabo
Pubblicazione: (2024)

Credit card loss forecasting: Some lessons from COVID
di: Partha Sengupta, et al.
Pubblicazione: (2024)

An Explainable ADASYN‐Based Focal Loss Approach for Credit Assessment
di: Shaukat Ali Shahee, et al.
Pubblicazione: (2025)

A Novel Multiclass Imbalance Classification Framework With Dynamic Evidential Fusion for Credit Rating
di: Wen‐hui Hou, et al.
Pubblicazione: (2025)

A Matrix‐Based Hidden Markov Model for Consumer Credit Analysis
di: Borui Qi, et al.
Pubblicazione: (2026)

A Two‐Stage Interpretable Model to Explain Classifier in Credit Risk Prediction
di: Lu Wang, et al.
Pubblicazione: (2025)

Can intraday data improve the joint estimation and prediction of risk measures? Evidence from a variety of realized measures
di: Zhimin Wu, et al.
Pubblicazione: (2024)

Forecasting multi‐frequency intraday exchange rates using deep learning models
di: Muhammad Arslan, et al.
Pubblicazione: (2024)

New runs‐based approach to testing value at risk forecasts
di: Marta Małecka
Pubblicazione: (2024)

Incorporating media news to predict financial distress: Case study on Chinese listed companies
di: Lifang Zhang, et al.
Pubblicazione: (2024)

Measuring the Default Risk of Small Business Loans: Improved Credit Risk Prediction Using Deep Learning
di: Yiannis Dendramis, et al.
Pubblicazione: (2025)

Ternary Interval Forecasting of Air Pollutant Concentration: A Novel Multivariate Decomposition and Optimal Variable Weight Ensemble Paradigm
di: Zicheng Wang, et al.
Pubblicazione: (2025)

Twitter policy uncertainty and stock returns in South Africa: Evidence from time‐varying Granger causality
di: Kingstone Nyakurukwa, et al.
Pubblicazione: (2024)

Do search queries predict violence against women? A forecasting model based on Google Trends
di: Nicolás Gonzálvez‐Gallego, et al.
Pubblicazione: (2024)

Time‐varying risk preference and equity risk premium forecasting: The role of the disposition effect
di: Kenan Qiao, et al.
Pubblicazione: (2024)

The mean squared prediction error paradox
di: Pablo Pincheira Brown, et al.
Pubblicazione: (2024)

Bayesian Semiparametric Multivariate Realized GARCH Modeling
di: Efthimios Nikolakopoulos
Pubblicazione: (2025)

Corporate financial distress prediction in a transition economy
di: Minh Nguyen, et al.
Pubblicazione: (2024)

Bayesian Markov switching model for BRICS currencies' exchange rates
di: Utkarsh Kumar, et al.
Pubblicazione: (2024)

A GARCH model selection and estimation method based on neural network with the loss function of mean square error and model confidence set
di: Yanhao Huang, et al.
Pubblicazione: (2024)