Journal of Forecasting最新文献_第10页

Are professional forecasters inattentive to public discussions about inflation? The case of Argentina 专业预测人员是否对公众关于通货膨胀的讨论缺乏关注？阿根廷的案例

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-09 DOI: 10.1002/for.3141

J. Daniel Aromí, Martín Llada

引用次数: 0

Forecasting corporate financial performance with deep learning and interpretable ALE method: Evidence from China 用深度学习和可解释 ALE 方法预测企业财务业绩：来自中国的证据

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-01 DOI: 10.1002/for.3138

Longyue Liang, Bo Liu, Zhi Su, Xuanye Cai

{"title":"Forecasting corporate financial performance with deep learning and interpretable ALE method: Evidence from China","authors":"Longyue Liang, Bo Liu, Zhi Su, Xuanye Cai","doi":"10.1002/for.3138","DOIUrl":"10.1002/for.3138","url":null,"abstract":"Forecasting and analyzing corporate financial performance are of significant value to investors, managers, and regulators. In this paper, we constructed the one-dimensional convolutional neural networks (1D-CNN) and long short-term memory (LSTM) deep learning models to investigate the feasibility of forecasting corporate financial performance with deep learning models, using the corporate financial features and environment, social and governance (ESG) rating index of Chinese A-share listed corporation data from 2015 to 2021. Five evaluation metrics were employed to measure models' forecasting effects, and four competing machine learning models were built to verify the improvement in forecasting accuracy brought by the deep learning models. Furthermore, we also introduced the Accumulated Local Effects method to explore the forecasting processes of the deep learning models. The empirical results show the following: (1) Deep learning models can effectively extract the time-series information in corporate data, thereby solving the task of predicting corporate financial performance with high accuracy. (2) The introduction of ESG information significantly contributes to the forecasting accuracy of corporate financial performance. For both 1D-CNN and LSTM models, the ESG rating index can provide additional useful information for forecasting. (3) The interpretable results reveal the preference and emphasis of the two deep learning models for the different features. This further proves the robustness and reliability of deep learning models in forecasting corporate financial performance.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2540-2571"},"PeriodicalIF":3.4,"publicationDate":"2024-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141056494","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

Data patterns that reliably precede US recessions 美国经济衰退前的可靠数据模式

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-30 DOI: 10.1002/for.3140

Edward E. Leamer

引用次数: 0

A novel semisupervised learning method with textual information for financial distress prediction 一种利用文本信息进行财务困境预测的新型半监督学习方法

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-24 DOI: 10.1002/for.3136

Yue Qiu, Jiabei He, Zhensong Chen, Yinhong Yao, Yi Qu

引用次数: 0

Forecasting Chinese crude oil futures volatility: New evidence based on dual feature processing of large-scale variables 预测中国原油期货波动：基于大规模变量双重特征处理的新证据

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-24 DOI: 10.1002/for.3131

Gaoxiu Qiao, Yijun Pan, Chao Liang, Lu Wang, Jinghui Wang

{"title":"Forecasting Chinese crude oil futures volatility: New evidence based on dual feature processing of large-scale variables","authors":"Gaoxiu Qiao, Yijun Pan, Chao Liang, Lu Wang, Jinghui Wang","doi":"10.1002/for.3131","DOIUrl":"10.1002/for.3131","url":null,"abstract":"This paper aims to study the volatility forecasting of Chinese crude oil futures from the large-scale variables perspective by considering both the information on international futures markets volatility and technical indicators of Chinese crude oil futures. We employ the dual feature processing method (LASSO-PCA) by integrating least absolute shrinkage and selection operator (LASSO) and principal component analysis (PCA) to extract important factors of the large-scale exogenous variables. Besides the traditional ordinary least squares (OLS) estimation, the nonlinear support vector regression (SVR) approach is employed to integrate with the LASSO-PCA method. The empirical results show that both the OLS and SVR combined with LASSO-PCA can improve the forecasting accuracy, especially SVR-LASSO-PCA owns the best forecasting performance. The analysis of the selected variables finds international futures volatility is chosen more frequently. These results are further validated through a series of robust experiments. Finally, the time difference between China and the United States is also considered in order to obtain more reasonable out-of-sample forecasting.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2495-2521"},"PeriodicalIF":3.4,"publicationDate":"2024-04-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140663593","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 card loss forecasting: Some lessons from COVID 信用卡损失预测：COVID 的一些经验教训

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-23 DOI: 10.1002/for.3137

Partha Sengupta, Christopher H. Wheeler

{"title":"Credit card loss forecasting: Some lessons from COVID","authors":"Partha Sengupta, Christopher H. Wheeler","doi":"10.1002/for.3137","DOIUrl":"10.1002/for.3137","url":null,"abstract":"Models developed by banks to forecast losses in their credit card portfolios have generally performed poorly during the COVID-19 pandemic, particularly in 2020, when large forecast errors were observed at many banks. In this study, we attempt to understand the source of this error and explore ways to improve model fit. We use account-level monthly performance data from the largest credit card banks in the U.S. between 2008 and 2018 to build models that mimic the typical model design employed by large banks to forecast credit card losses. We then fit these on data from 2019 to 2021. We find that COVID-period model errors can be reduced significantly through two simple modifications: (1) including measures of the macroeconomic environment beyond indicators of the labor market, which served as the primary macro drivers used in many pre-pandemic models and (2) adjusting macro drivers to capture persistent/sustained changes, as opposed to temporary volatility in these variables. These model improvements, we find, can be achieved without a significant reduction in model performance for the pre-COVID period, including the Great Recession. Moreover, in broadening the set of macro influences and capturing sustained changes, we believe models can be made more robust to future downturns, which may bear little resemblance to past recessions.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2448-2477"},"PeriodicalIF":3.4,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140668615","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

Prediction of wind energy with the use of tensor-train based higher order dynamic mode decomposition 利用基于张量列车的高阶动态模式分解预测风能

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-14 DOI: 10.1002/for.3126

Keren Li, Sergey Utyuzhnikov

引用次数: 0

Multivariable forecasting approach of high-speed railway passenger demand based on residual term of Baidu search index and error correction 基于百度搜索指数残差项和误差修正的高速铁路客运需求多变量预测方法

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-14 DOI: 10.1002/for.3134

Hongtao Li, Xiaoxuan Li, Shaolong Sun, Zhipeng Huang, Xiaoyan Jia

{"title":"Multivariable forecasting approach of high-speed railway passenger demand based on residual term of Baidu search index and error correction","authors":"Hongtao Li, Xiaoxuan Li, Shaolong Sun, Zhipeng Huang, Xiaoyan Jia","doi":"10.1002/for.3134","DOIUrl":"10.1002/for.3134","url":null,"abstract":"Accurate prior information of passenger flow demand on high-speed railway is of great significance for the operation and the management of transportation systems. Various factors in modern social life have caused uncertainty at demand. Recently, individuals are increasingly depending on the online search results when choosing among different transportation modes, services, and destinations, which provide important basic information for forecasting the travel demand. This study employs Baidu search index to assist in capturing volatility of high-speed railway passenger demands, offering insights into the travel inclinations and travelers' actions. Furthermore, we have given more in-depth attention and analysis to their residual term accounting for the random nature caused by various factors. To this end, a sophisticated deep analysis mechanism based on data decomposition has been devised to extract and analyze the valuable information concealed within the residuals, so as to enhance the comprehension of the variability inherent in the high-speed railway passenger flow. Meanwhile, an error correction strategy is implemented for all residual terms to improve further their forecasting accuracy. Experimental results from two real-world datasets demonstrate the effectiveness and robustness of the developed hybrid approach across several popular evaluation indicators. Therefore, this study can function as a reliable instrument, provide sensible data-driven guidance for resource allocation and make scientific decisions in the railway industry.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2401-2433"},"PeriodicalIF":3.4,"publicationDate":"2024-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140578114","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

Hybrid forecasting of crude oil volatility index: The cross-market effects of stock market jumps 原油波动指数的混合预测：股市跳跃的跨市场效应

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-11 DOI: 10.1002/for.3132

Gongyue Jiang, Gaoxiu Qiao, Lu Wang, Feng Ma

引用次数: 0

Are national or regional surveys useful for nowcasting regional jobseekers? The case of the French region of Pays-de-la-Loire 国家或地区调查对预测地区求职者是否有用？以法国卢瓦尔河地区为例

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-04-11 DOI: 10.1002/for.3125

Clément Cariou, Amélie Charles, Olivier Darné

引用次数: 0