Journal of Forecasting最新文献_第2页

Learnable Aggregate Federated Learning Initialized Based on Contribution Evaluation 基于贡献评价初始化的可学习聚合联邦学习

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-10-01 Epub Date: 2026-06-23 DOI: 10.1002/for.70187

Chunhua Ju, Zhonghua Shen, Pengtong Weng, Fuguang Bao, Yuheng Jin, Desheng Cheng

{"title":"Learnable Aggregate Federated Learning Initialized Based on Contribution Evaluation","authors":"Chunhua Ju, Zhonghua Shen, Pengtong Weng, Fuguang Bao, Yuheng Jin, Desheng Cheng","doi":"10.1002/for.70187","DOIUrl":"https://doi.org/10.1002/for.70187","url":null,"abstract":"<div>\u0000 \u0000 <p>Federated learning generally suffers from slow convergence and suboptimal model performance due to client drift caused by data heterogeneity. Although extensive research has been devoted to addressing these issues, adaptive optimization algorithms that combine model training states and are based on client contribution evaluation remain scarce. With the increasing diversity of data distributions and complexity of model architectures, the convergence of federated learning models often requires more training iterations. To verify the application value of adaptive strategies in federated learning scenarios, this study proposes a Learnable Aggregate Federated Learning algorithm with initialization based on contribution evaluation (LAFL-AI). At the initial stage of each training round on the server, the algorithm first quantifies the contribution of each client to the global model by exploring the correlation between local and global gradients, and adaptively initializes the aggregation weights of each local model for the global aggregation process directly based on the results of this contribution evaluation. Meanwhile, to further improve the generalization performance of the global model, a class-balanced proxy dataset is constructed in this study to train and obtain the optimal weights for global aggregation. Experimental results show that the adaptive initialization strategy for aggregation weights based on contribution evaluation proposed in this paper can not only accelerate the training efficiency of aggregation weights but also significantly improve the convergence speed of the global model. Compared with other baseline algorithms, LAFL-AI enables fast convergence of the global model and can achieve equivalent or even better training results with fewer server training rounds.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3327-3341"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100393","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Artificial Neural Network Enhanced With Bio-Inspired Optimization Algorithms for Predicting the Financial Stress in the Eurozone 基于仿生优化算法的人工神经网络预测欧元区金融压力

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-10-01 Epub Date: 2026-07-01 DOI: 10.1002/for.70189

Munir Abdulsaleh, Murad A. Bein

{"title":"Artificial Neural Network Enhanced With Bio-Inspired Optimization Algorithms for Predicting the Financial Stress in the Eurozone","authors":"Munir Abdulsaleh, Murad A. Bein","doi":"10.1002/for.70189","DOIUrl":"https://doi.org/10.1002/for.70189","url":null,"abstract":"<div>\u0000 \u0000 <p>Traditional econometric models frequently fail to adequately reflect the ability to estimate future values of macroeconomic and financial variables, which is crucial for the implementation of macroeconomic policies. This study aims to tackle this problem by predicting the financial stress index (FSI) of the Eurozone for a sample period spanning from January 1995 to August 2023 using artificial neural networks (ANN) optimized by bio-inspired optimisation algorithms like the Invasive Weed Optimization (IWO), Firefly Algorithm (FA), Particle Swarm Optimization (PSO), Cultural Algorithm (CA), and Artificial Bee Colony (ABC). The prediction error of the ANN is greatly reduced by the ANN optimized by FA (ANN-FA) model, which reduces it by roughly 97.57% for the calibration sample and 97.94% for the validation sample. The second-best model for FSI prediction in the Eurozone is the ANN optimized by PSO (ANN-PSO), which reduces the ANN's prediction error by 94.48% and 95.38% for the calibration and validation samples, respectively. The models are assessed for performance using accuracy metrics, and the metrics are visualized using chord, bump, and Taylor diagrams. These results imply that bio-inspired optimization can significantly enhance predictive reliability in monitoring financial stress conditions.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3358-3367"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149099862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Threshold Asymmetric Conditional Autoregressive Range (TACARR) Model 阈值非对称条件自回归范围（TACARR）模型

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-10-01 Epub Date: 2026-06-21 DOI: 10.1002/for.70185

Isuru Ratnayake, V. A. Samaranayake

{"title":"Threshold Asymmetric Conditional Autoregressive Range (TACARR) Model","authors":"Isuru Ratnayake, V. A. Samaranayake","doi":"10.1002/for.70185","DOIUrl":"https://doi.org/10.1002/for.70185","url":null,"abstract":"<p>This paper introduces a Threshold Asymmetric Conditional Autoregressive Range (TACARR) model for analyzing the daily price ranges of financial assets. The proposed formulation assumes that the conditional expected range switches between two regimes, representing upward and downward market states, with the disturbance distribution also allowed to vary across regimes. A self-adjusting threshold component, determined by past values of the series, is used to identify the prevailing market regime. In this way, the model is able to capture asymmetric and heteroscedastic volatility behavior in financial markets. The TACARR model is designed to address several limitations of existing price range models, including the Conditional Autoregressive Range (CARR), Asymmetric CARR (ACARR), Feedback ACARR (FACARR), and Threshold Autoregressive Range (TARR) models, with and without exogenous variables. Model parameters are estimated using the maximum likelihood (ML) method, and a simulation study shows that the proposed estimation procedure performs well. The empirical performance of the TACARR model under several distributional assumptions is evaluated using IBM stock and S&P 500 index, and the results show that it serves as a flexible and competitive alternative to existing models for both in-sample fitting and out-of-sample volatility forecasting.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3298-3326"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70185","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing the Potential of Random Forest Technique With an Adaptive Horse Herd Optimization Framework for Electric Load Prediction 用自适应马群优化框架增强随机森林技术在电力负荷预测中的潜力

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-10-01 Epub Date: 2026-05-26 DOI: 10.1002/for.70180

Manvi Mishra, Priya Mahajan, Rachana Garg

{"title":"Enhancing the Potential of Random Forest Technique With an Adaptive Horse Herd Optimization Framework for Electric Load Prediction","authors":"Manvi Mishra, Priya Mahajan, Rachana Garg","doi":"10.1002/for.70180","DOIUrl":"https://doi.org/10.1002/for.70180","url":null,"abstract":"<div>\u0000 \u0000 <p>The vast application of optimization algorithms has been observed in the field of machine learning for improving the accuracy of the model. A typical setback observed in optimization algorithms is the issue of premature convergence which might occur due to the absence of extraordinary exploration of the complex and continuous search space. Therefore, the present study attempts to introduce an imperative improvement in conventional horse herd optimization (HHO) which significantly boosts the optimizing capability by resolving the issue of premature convergence. This improved HHO (iHHO) is the integration of conventional HHO with the hunting and encircling characteristics of the conventional grey wolf optimization (GWO) algorithm. The performance of the proposed iHHO algorithm has been evaluated on standard benchmark functions and compared with conventional HHO, GWO, and other variants of basic variants of HHO. The statistical significance of the results is verified using the Friedman ranking and Wilcoxon pairwise tests, while computational analysis is demonstrated using Big-O analysis. Later, the practical applicability of the proposed iHHO algorithm has been analyzed by optimizing the hyperparameters of random forest (RF) for developing the effective electric load forecasting model. The error matrices evaluation, stability test, and the Diebold–Mariano test have been performed to highlight the superiority of the iHHO optimized RF model. Furthermore, the performance validation has been extended by comparing the present state of art with the other recently developed models.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3148-3169"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Credit Risk Evaluation Framework Based on Heterogeneous Information: Integrating Three-Way Decision and Graph Sample and Aggregate 基于异构信息的信用风险评估框架：三向决策与图样本与集合的集成

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-10-01 Epub Date: 2026-06-12 DOI: 10.1002/for.70183

Jia-Wei Zhang, Jian-Qiang Wang, Wen-Hui Hou, Xiao-Kang Wang, Ya-Nan Wang

{"title":"Credit Risk Evaluation Framework Based on Heterogeneous Information: Integrating Three-Way Decision and Graph Sample and Aggregate","authors":"Jia-Wei Zhang, Jian-Qiang Wang, Wen-Hui Hou, Xiao-Kang Wang, Ya-Nan Wang","doi":"10.1002/for.70183","DOIUrl":"https://doi.org/10.1002/for.70183","url":null,"abstract":"<div>\u0000 \u0000 <p>Evaluating credit risk is crucial for financial institutions because it determines whether loans should be granted to applicants, impacting potential returns and losses for the institution. However, modeling the heterogeneous information of applicants while simultaneously balancing institutional gains and losses faced by the institution is a complex challenge. To address the issues, this study proposes a credit risk evaluation framework with heterogeneous information: combining three-way decision (TWD) theory and graph sample and aggregate (GraphSAGE) learning. This framework (i) integrates a fuzzy similarity relation based on fuzzy theory, constructing a GraphSAGE model for graph-structured data, (ii) combines the results of GraphSAGE with the TWD theory to divide loan applications into three regions: positive, negative, and boundary, and (iii) adds additional information for instances of the boundary for final evaluations, providing new perspectives for research in credit risk evaluation. The experiment results demonstrate the excellent effectiveness of the method in credit risk evaluation, coping well with data imbalances while balancing losses and gains.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3278-3297"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100128","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Forecasting and Pricing in the Carbon Credits Market 碳信用市场的预测与定价

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-10-01 Epub Date: 2026-05-25 DOI: 10.1002/for.70168

Yiyang Chen, Rogemar Mamon, Fabio Spagnolo, Nicola Spagnolo, Heng Xiong

{"title":"Forecasting and Pricing in the Carbon Credits Market","authors":"Yiyang Chen, Rogemar Mamon, Fabio Spagnolo, Nicola Spagnolo, Heng Xiong","doi":"10.1002/for.70168","DOIUrl":"https://doi.org/10.1002/for.70168","url":null,"abstract":"<div>\u0000 \u0000 <p>The European Union Emissions Trading System has emerged as a cornerstone of climate policy, with carbon allowance prices exhibiting complex dynamics influenced by policy reforms, market shocks, and structural transitions. This study examines European Union Allowances (EUA) spot-price dynamics through four canonical processes and their regime-switching hidden Markov model (HMM) extensions, estimated using rolling windows to capture time-varying parameters. Our framework integrates in-sample fitting, multi-horizon forecasting, density calibration, and hedging performance evaluation, revealing that regime switching systematically enhances model flexibility and accuracy, particularly for jump-diffusion specifications. By incorporating maturity-matched risk-free rates and statistical tests, we demonstrate that regime-switching models better capture state-dependent behaviors and provide more reliable derivatives pricing and risk management insights. These findings offer practical value for market participants and policymakers in navigating carbon market risks and designing effective hedging strategies.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3101-3127"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhancing Neural Network Volatility Forecasting: Decomposed Volatility Modeling 增强神经网络波动率预测：分解波动率模型

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-10-01 Epub Date: 2026-05-26 DOI: 10.1002/for.70179

Xuzheng Ren, Pengfan Sun, Haoren Zhu, Pengfei Zhao

{"title":"Enhancing Neural Network Volatility Forecasting: Decomposed Volatility Modeling","authors":"Xuzheng Ren, Pengfan Sun, Haoren Zhu, Pengfei Zhao","doi":"10.1002/for.70179","DOIUrl":"https://doi.org/10.1002/for.70179","url":null,"abstract":"<div>\u0000 \u0000 <p>Volatility, as a measure of uncertainty, plays a crucial role in various financial activities such as risk management. Existing methods, including GARCH models and neural network (NN) approaches have their limitations. In this paper, we introduce a novel “decomposed volatility modeling” (<i>DVM</i>) framework, which decomposes the raw volatility signals into two distinct components: the GARCH component and the GARCH Remainder component, which are independently modeled using GARCH and NN techniques. The GARCH component preserves stylized facts signals without distortion by NN, ensuring their retention in the forecast. Simultaneously, the GARCH Remainder, whose stationarity is improved by filtering out the stylized facts signals from the raw volatility series, is modeled using NN. DVM leverages the strengths of both GARCH and NN, which enhances predictive performance. We validate the proposed framework using a comprehensive dataset comprising six financial assets, covering stock indices, cryptocurrencies, and macroeconomic data. The experimental results substantiate that <i>DVM</i> significantly enhances the predictive performance compared with using GARCH or NN model alone, highlighting the efficacy of disentangling the stylized facts signals from the raw volatility series.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 7","pages":"3128-3147"},"PeriodicalIF":2.6,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149100593","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Novel Stock Index Prediction of Artificial Gorilla Troops Optimization and Bayesian Regression Dual Ensemble With Dynamic Valid Residual Correction Network Architecture 一种具有动态有效残差校正网络结构的人工大猩猩优化与贝叶斯回归对偶集成股票指数预测方法

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-08-05 Epub Date: 2026-05-10 DOI: 10.1002/for.70170

Yuhao Cheng, Yichen Yang, Chengli Zheng

{"title":"A Novel Stock Index Prediction of Artificial Gorilla Troops Optimization and Bayesian Regression Dual Ensemble With Dynamic Valid Residual Correction Network Architecture","authors":"Yuhao Cheng, Yichen Yang, Chengli Zheng","doi":"10.1002/for.70170","DOIUrl":"https://doi.org/10.1002/for.70170","url":null,"abstract":"<div>\u0000 \u0000 <p>Financial series are challenging to predict owing to their inherent nonlinearity and significant noise. Mainstream prediction typically employs single ensemble models, which suffer from low prediction accuracy and instability. Furthermore, the predictive performance of models with error correction networks has still been unsatisfactory because of the significant randomness and chaos in prediction error. Therefore, we propose a novel dual ensemble (DE) of artificial gorilla troops optimization (AGTO) and the Bayesian regression, combined with dynamic valid residual correction network (DVRCN) architecture for stock index prediction. This dual ensemble-dynamic valid residual correction network (DE-DVRCN) model leverages nine machine learning models and six distinct objectives-based AGTO algorithm to construct the first-layer ensembles. Next, the Bayesian regression is employed to construct the second-layer ensemble with these single ensembles. We use variational mode decomposition-sample entropy network to isolate valid residual information from prediction error, which is then combined with LASSO regression to predict error. Finally, the predicted error is merged with DE predictions to establish the DE-DVRCN model. The new model combines the strengths of different ensemble strategies, and DVRCN eliminates interference from invalid residual information in predictions. Experiments are conducted on three major stock indices in the Chinese market; it turns out that our model markedly outperforms individual models and single ensembles in predictions.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 6","pages":"2991-3010"},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148695776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Point and Risk estImation Using an enSemble of Models for Nowcasting: PRISM-Now 利用临近预报模型集合的点和风险估计：PRISM-Now

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-08-05 Epub Date: 2026-04-16 DOI: 10.1002/for.70161

Beomseok Seo, Hyungbae Cho, Dongjae Lee

{"title":"Point and Risk estImation Using an enSemble of Models for Nowcasting: PRISM-Now","authors":"Beomseok Seo, Hyungbae Cho, Dongjae Lee","doi":"10.1002/for.70161","DOIUrl":"https://doi.org/10.1002/for.70161","url":null,"abstract":"<p>We propose PRISM-Now, a novel ensemble forecasting system for near-term GDP projection. Recognizing that relevant economic information evolves over time, we treat forecasts from multiple base models as draws from a mixture distribution of “good” and “bad” estimates, whose composition changes continuously and cannot be identified ex ante. To improve forecasting accuracy, PRISM-Now adaptively selects an aggregation quantile using contemporaneous ensemble distributional information, including changes in central tendency, dispersion, and skewness. Empirical results show that PRISM-Now outperforms alternative ensemble methods, including simple averaging and approaches that rely on backward-looking information. Using Korean GDP data, we further find that conventional models perform relatively well for nowcasting (\u0000<span></span><math>\u0000 <mi>t</mi>\u0000 <mo>+</mo>\u0000 <mn>0</mn></math>) when near-complete data are available, while big data and machine learning models exhibit stronger performance for one-quarter-ahead forecasts (\u0000<span></span><math>\u0000 <mi>t</mi>\u0000 <mo>+</mo>\u0000 <mn>1</mn></math>) in the absence of realized information. Models incorporating text and sentiment data are particularly effective during the COVID-19 period. Overall, these findings highlight the value of dynamic ensembling in adapting to rapidly changing economic conditions.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 6","pages":"2760-2784"},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70161","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combining Sampling Methods, Cost-Sensitive Learning, and Ensemble Techniques for Highly Class-Imbalanced Financial Distress Prediction 结合抽样方法、成本敏感学习和集成技术进行高度类别不平衡的财务困境预测

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-08-05 Epub Date: 2026-04-27 DOI: 10.1002/for.70162

Wei-Qiang Huang, Lianlian Wang

{"title":"Combining Sampling Methods, Cost-Sensitive Learning, and Ensemble Techniques for Highly Class-Imbalanced Financial Distress Prediction","authors":"Wei-Qiang Huang, Lianlian Wang","doi":"10.1002/for.70162","DOIUrl":"https://doi.org/10.1002/for.70162","url":null,"abstract":"<div>\u0000 \u0000 <p>In highly class-imbalanced financial distress prediction, standard classifiers often fail as they assume balanced class distributions and equal misclassification costs. We propose a heterogeneous voting ensemble method that addresses extreme class imbalance by synergizing sampling, cost-sensitive learning, and ensemble techniques. First, coordinating sampling and cost-sensitive learning reduces model bias in identifying distressed firms. Sampling rebalances class distributions, which reduces representation bias and provides an effective data foundation for cost-sensitive learning. Cost-sensitive learning assigns higher costs to distressed firms, adjusting the optimization objective to minimize total misclassification costs. Second, by aggregating diverse base classifiers, the heterogeneous voting ensemble reduces potential additional variance introduced by sampling and cost-sensitive learning, thereby improving generalization. Using data from China, Poland, and the United States, we show that the proposed method significantly outperforms benchmark models in detecting distressed firms. Moreover, the improved predictions translate into reduced misclassification costs, particularly when the cost difference between distressed and healthy firms is greater. Ablation experiments validate the effectiveness of each component, where sampling contributes most to detection accuracy for distressed firms, followed by cost-sensitive learning, while the ensemble ensures model stability. These results highlight that combining sampling, cost-sensitive learning, and ensemble techniques is crucial for stakeholders (e.g., investors, creditors, and regulators) to detect rare events such as financial distress.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 6","pages":"2861-2889"},"PeriodicalIF":2.6,"publicationDate":"2026-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148696624","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0