Journal of Forecasting最新文献_第6页

Forecasting Corporate Default Risk Across Multiple Horizons With Interpretable Machine Learning 利用可解释的机器学习跨多个视界预测企业违约风险

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-07-03 Epub Date: 2026-03-08 DOI: 10.1002/for.70129

Qingli Dong, Li Li

{"title":"Forecasting Corporate Default Risk Across Multiple Horizons With Interpretable Machine Learning","authors":"Qingli Dong, Li Li","doi":"10.1002/for.70129","DOIUrl":"https://doi.org/10.1002/for.70129","url":null,"abstract":"<div>\u0000 \u0000 <p>Accurate and transparent default prediction is central to credit-risk operations and policy. This paper proposes ELG, an interpretable two-stage machine-learning framework for decision support in corporate default prediction. Stage I uses an explainable, sparsity-controlled selector (E-LassoNet) to produce a compact, auditable feature set; Stage II fits a gradient-boosting model (GBDT) on the retained drivers to capture residual nonlinearity and output multi-horizon default probabilities. Using a large firm-year panel of Chinese listed companies and evaluating six forecast horizons, ELG achieves AUCs in the 0.98–0.99 range and Recalls in the 0.94–0.99 range with 27–28 predictors and outperforms standard classifiers on AUC and G_mean at most horizons. Post-analysis shows financial signals dominate short horizons, while governance and other non-financial indicators grow in importance at longer horizons. By enforcing interpretability ex ante and evaluating with decision-aligned metrics, ELG links transparent feature selection to actionable, horizon-aware forecasts, improving deployability and governance for credit-risk management in emerging markets.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 5","pages":"2266-2299"},"PeriodicalIF":2.6,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386390","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

Term Spread Volatility as a Leading Indicator of Economic Activity 作为经济活动领先指标的期限价差波动

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-07-03 Epub Date: 2026-03-15 DOI: 10.1002/for.70132

Anastasios Megaritis, Dimitrios Bakas, Theodora Bermpei, Athanasios Triantafyllou

{"title":"Term Spread Volatility as a Leading Indicator of Economic Activity","authors":"Anastasios Megaritis, Dimitrios Bakas, Theodora Bermpei, Athanasios Triantafyllou","doi":"10.1002/for.70132","DOIUrl":"https://doi.org/10.1002/for.70132","url":null,"abstract":"<p>In this paper, we examine the macroeconomic predictive power of the volatility of the US Treasury yield curve slope (term spread volatility). Our forecasting exercise shows that US term spread volatility has significant predictive power for US industrial production and employment growth. The predictive power of term spread volatility is stronger at medium- and long-term forecasting horizons and remains robust when well-established predictors of economic activity, such as the term spread and stock market returns, are included. Our results also show that term spread volatility has statistically and economically distinct predictive power compared to other measures of economic uncertainty. Moreover, the predictive power of term spread volatility increases significantly after the 2008 Great Recession, indicating that the relationship between uncertainty about macroeconomic expectations and macroeconomic performance has strengthened in the post–Great Recession period. Finally, our out-of-sample forecasting results show that term spread volatility outperforms the term spread in forecasting economic activity over the longer term.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 5","pages":"2332-2354"},"PeriodicalIF":2.6,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70132","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148386638","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

Sentiment-Driven Forecasting of Carbon Prices: A Hybrid Neural Network Approach Based on BiGRU-Inception-Attention 情绪驱动的碳价格预测：基于BiGRU-Inception-Attention的混合神经网络方法

IF 2.6 3区经济学

Journal of Forecasting Pub Date : 2026-07-03 Epub Date: 2026-03-29 DOI: 10.1002/for.70147

Guojun Wang, Xuan Liu, Zilin Hu, Jing Li, Lin Wang

{"title":"Sentiment-Driven Forecasting of Carbon Prices: A Hybrid Neural Network Approach Based on BiGRU-Inception-Attention","authors":"Guojun Wang, Xuan Liu, Zilin Hu, Jing Li, Lin Wang","doi":"10.1002/for.70147","DOIUrl":"https://doi.org/10.1002/for.70147","url":null,"abstract":"<div>\u0000 \u0000 <p>Carbon trading forecasting faces challenges from strong nonlinearity, multiscale fluctuation, and the influence of market sentiment, which traditional models struggle to capture. To address these issues, this paper proposes a sentiment-driven BiGRU-Inception-Attention hybrid neural network framework for carbon price forecasting. In the first step, we use the BERT-BiLSTM model to construct a carbon news sentiment index. We then apply the XGBoost model to rank feature importance and select the eight most important features from 11 factors. Consequently, we use the BiGRU model to capture temporal dependencies of carbon prices, the Inception model to extract multiscale features, and the Attention mechanism to dynamically weight key features. Using data of China's Hubei carbon market from 2020 to 2024, the proposed approach significantly outperforms both traditional benchmarks and hybrid models without sentiment, achieving an MSE of 0.332 and <i>R</i><sup><i>2</i></sup> of 0.839. The results highlight the importance of sentiment in carbon price formation and provide a practical reference for improving carbon price forecasting and market design.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 5","pages":"2426-2457"},"PeriodicalIF":2.6,"publicationDate":"2026-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148387025","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

Parametric Quantile Regression Using Mixture Distributions: Estimating Value at Risk and Expected Shortfall 使用混合分布的参数分位数回归：估计风险值和预期不足

IF 2.7 3区经济学

Journal of Forecasting Pub Date : 2026-06-02 Epub Date: 2026-01-14 DOI: 10.1002/for.70103

Song Shi, Xinyu Wang

{"title":"Parametric Quantile Regression Using Mixture Distributions: Estimating Value at Risk and Expected Shortfall","authors":"Song Shi, Xinyu Wang","doi":"10.1002/for.70103","DOIUrl":"https://doi.org/10.1002/for.70103","url":null,"abstract":"<div>\u0000 \u0000 <p>Classical quantile regression has an asymmetric absolute error loss function with no derivative at the origin, making parameter estimation difficult. By setting the residual to follow an asymmetric Laplace distribution, quantile regression becomes an equivalent parametric model. However, the asymmetric Laplace distribution does not fit the distribution of asset returns, which is characterized by sharp peaks and heavy tails. To better fit the residuals, we design some general flexible error distributions via mixture distributions whose quantile at a given level is zero. We then propose a novel parametric dynamic quantile regression model using these distributions. It has a smooth loss function, which allows us to apply the maximum likelihood approach to estimate parameters easily. We demonstrate the validity of our model through a risk management application. It has the unique advantage of providing the analytical formulae for value at risk and expected shortfall over other risk measure models. An empirical study of stock markets in 25 countries shows that our model outperforms benchmark models in backtesting out-of-sample forecasts of value at risk and expected shortfall.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 4","pages":"1495-1510"},"PeriodicalIF":2.7,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148147178","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

Image-Based Deep Learning Models for Stock Predictions: Combining Line, Candlestick, and Bar Charts 基于图像的深度学习模型用于股票预测：结合线形图、烛台图和条形图

IF 2.7 3区经济学

Journal of Forecasting Pub Date : 2026-06-02 Epub Date: 2026-01-06 DOI: 10.1002/for.70099

Wei-Chao Lin, Ming-Chang Wang, Chih-Fong Tsai, Jui-Pin Hsu

{"title":"Image-Based Deep Learning Models for Stock Predictions: Combining Line, Candlestick, and Bar Charts","authors":"Wei-Chao Lin, Ming-Chang Wang, Chih-Fong Tsai, Jui-Pin Hsu","doi":"10.1002/for.70099","DOIUrl":"https://doi.org/10.1002/for.70099","url":null,"abstract":"<div>\u0000 \u0000 <p>Recently, numerous studies have appeared in the literature on the use of deep learning techniques to analyze the image patterns for stock prediction. Although there are several different types of imaging techniques that can be used to represent related technical indicators, including line charts, candlestick charts, and bar charts, no one has yet examined the effect of using these types of image visualization techniques on the prediction performance of deep learning models. In this paper, three types of image patterns are compared, specifically, line charts with trading volume information represented by a bar chart, candlestick charts with trading volume information, and a mixed type of image with two other related technical indicators, that is, MACD and RSI. The experimental results that are based on data for six companies from different industries and with different scales of stock price fluctuation show that the mixed image pattern type allows 2-D CNN and VGG16 to perform better than the other two image pattern types in terms of predicting the stock prices for the next day, week, and month. In addition, they outperform the LSTM and 1-D CNN baseline models when using the time series data representing historical stock prices. Furthermore, three ensemble deep learning models are constructed for performance comparison, including VGG16-LSTM, 2-D CNN-LSTM, and the stacking model, in which the VGG16-LSTM and the stacking models perform the best for the prediction of short- to mid-term and mid- to long-term stock prices, respectively.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 4","pages":"1350-1367"},"PeriodicalIF":2.7,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148147072","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 Comparison of Realized Measures of Integrated Volatility: Price Duration- vs. Return-Based Approaches 综合波动率已实现测度的比较：基于价格持续时间与基于回报的方法

IF 2.7 3区经济学

Journal of Forecasting Pub Date : 2026-06-02 Epub Date: 2026-01-20 DOI: 10.1002/for.70095

Björn Schulte-Tillmann, Mawuli Segnon, Timo Wiedemann

{"title":"A Comparison of Realized Measures of Integrated Volatility: Price Duration- vs. Return-Based Approaches","authors":"Björn Schulte-Tillmann, Mawuli Segnon, Timo Wiedemann","doi":"10.1002/for.70095","DOIUrl":"https://doi.org/10.1002/for.70095","url":null,"abstract":"<p>We study the accuracy of a variety of parametric price duration-based realized variance estimators constructed via various financial duration models and compare their forecasting performance with the performance of various nonparametric return-based realized variance estimators. Our financial duration models consist of an autoregressive conditional duration (ACD), its logarithmic version (Log–ACD), and the fractionally integrated ACD (FIACD), as well as the Markov switching multifractal duration (MSMD) model and the factorial hidden Markov duration (FHMD) process. In an empirical study using high-frequency data on 10 stocks traded on the New York stock exchange (NYSE), our in-sample and out-of-sample results show that the parametric price duration-based realized variance (RV) estimators, especially the ACD-based RV estimator, seem to be robust to price jumps and microstructure noise and perform better than the non-parametric return-based RV estimators. Furthermore, we also find that the price duration- and return-based RV models perform relatively well and produce more accurate and valid value-at-risk forecasts than the GARCH(1,1).</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 4","pages":"1579-1600"},"PeriodicalIF":2.7,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70095","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148147228","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

Forecasting New Employment Using Nonrepresentative Online Job Advertisements With an Application to the Italian and EU Labor Market 利用非代表性的在线招聘广告预测新的就业机会，并应用于意大利和欧盟劳动力市场

IF 2.7 3区经济学

Journal of Forecasting Pub Date : 2026-06-02 Epub Date: 2026-01-18 DOI: 10.1002/for.70090

Pietro Giorgio Lovaglio, Mario Mezzanzanica

{"title":"Forecasting New Employment Using Nonrepresentative Online Job Advertisements With an Application to the Italian and EU Labor Market","authors":"Pietro Giorgio Lovaglio, Mario Mezzanzanica","doi":"10.1002/for.70090","DOIUrl":"https://doi.org/10.1002/for.70090","url":null,"abstract":"<p>Using online job advertisement data improves the timeliness and granularity depth of analysis in the labor market in domains not covered by official data. Specifically, its variation over time may be used as an anticipator of official employment variations. However, online job advertisements may not be representative in terms of key labor market variables. The paper presents a methodology that forecasts publicly available official employment LFS recent job starters exploiting its relationship with a bias-corrected version of online job postings, obtained by predictions of a bivariate sample selection model, which jointly estimates the number of vacancies within job profiles and the probability of endogenous selection for nonzero vacancies. LFS new hires (obtained from LFS microdata) were used as benchmark data to measure the bias of online data and adjusted predicted counts. The proposed framework is illustrated using a dataset of Italian online job advertisements spanning from the period 2013-Q2 to 2018-Q2 to forecast quarterly LFS recent job starters 1 year ahead and the Cedefop's Skills-OVATE data using Italy, France, Germany, and Spain in 2022. Results demonstrated that raw vacancies present a strong bias level with respect to benchmark data, whereas sample selection models reduced this bias by half, unlike multilevel estimates. Moreover, LFS forecasts using a VECM that leverages cointegration between LFS recent job starters and adjusted online vacancy series offer a valuable alternative to traditional univariate forecasting methods.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 4","pages":"1529-1558"},"PeriodicalIF":2.7,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70090","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148147254","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

Significance of Technical Indicators in the Era of Machine Learning 技术指标在机器学习时代的意义

IF 2.7 3区经济学

Journal of Forecasting Pub Date : 2026-06-02 Epub Date: 2026-01-18 DOI: 10.1002/for.70104

Ajim Uddin, Ummahani Akter, M. Kabir Hassan

引用次数: 0

Improving Flood Prediction Using Artificial Neural Networks With Optimal Feature Selection on a Benchmark Dataset 基于最优特征选择的人工神经网络在基准数据集上改进洪水预测

IF 2.7 3区经济学

Journal of Forecasting Pub Date : 2026-06-02 Epub Date: 2026-01-12 DOI: 10.1002/for.70096

Jyothish V. R, Sajimon Abraham, Krishna Presannakumar, Leo Abraham

{"title":"Improving Flood Prediction Using Artificial Neural Networks With Optimal Feature Selection on a Benchmark Dataset","authors":"Jyothish V. R, Sajimon Abraham, Krishna Presannakumar, Leo Abraham","doi":"10.1002/for.70096","DOIUrl":"https://doi.org/10.1002/for.70096","url":null,"abstract":"<div>\u0000 \u0000 <p>Disasters significantly impact people's lives; among them, flooding is the worst common, and it causes sudden and secure damage to both lives and property. Addressing such real-time crisis demands intricate and sophisticated flood prediction models with enhanced capabilities. The development of efficient flood prediction models is often hindered by the lack of available datasets and the need for optimal feature. To address the challenge of data availability, in the proposed research, we have manually prepared a novel dataset by collecting data from NASA's (National Aeronautics and Space Administration) Power Project. The proposed dataset is experimentally evaluated and verified and has been organized into a balanced benchmark dataset with 33 features using the SMOTE algorithm. To enhance the provenance of flood prediction model, we propose a novel feature selection method. This method integrates outcomes from three different feature selection techniques to identify the most prominent features. The proposed feature selection method improves the model's performance and efficiency by identifying optimal predictors. Experimental results demonstrate that the artificial neural network trained with the selected relevant features accurately predicts flood occurrences, showing enhanced accuracy compared to state-of-the-art methods.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 4","pages":"1454-1472"},"PeriodicalIF":2.7,"publicationDate":"2026-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148147219","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

Bayesian Forecasting for a Logistic Mixture Double Autoregressive Model Logistic混合双自回归模型的贝叶斯预测

IF 2.7 3区经济学

Journal of Forecasting Pub Date : 2026-06-02 Epub Date: 2026-01-23 DOI: 10.1002/for.70109

Han Li, Qingqing Zhang, Kai Yang

引用次数: 0