Journal of Forecasting最新文献_第10页

Economic Forecasting With German Newspaper Articles 用德国报纸文章进行经济预测

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-11-06 DOI: 10.1002/for.3211

Tino Berger, Simon Wintter

{"title":"Economic Forecasting With German Newspaper Articles","authors":"Tino Berger, Simon Wintter","doi":"10.1002/for.3211","DOIUrl":"https://doi.org/10.1002/for.3211","url":null,"abstract":"<p>We introduce a new leading indicator for the German business cycle based on the content of newspaper articles from the <i>Süddeutsche Zeitung</i>. We use the rapidly evolving technique of Natural Language Processing (NLP) to transform the content of daily newspaper articles between 1992 and 2021 into topic time series using an LDA model. These topic time series reflect broad areas of the German economy since 1992, in particular the recession phases of the High-Tech Crisis, the Great Financial Crisis and the Covid-19 pandemic. We use the Newspaper Indicator in a Probit model to demonstrate that our data can be considered as a new leading indicator for predicting recession periods in Germany. Moreover, we show in an out-of-sample forecast experiment that our newspaper data have a predictive power for the German business cycle across 12 target variables that is as strong as established survey indicators. Industrial Production, the Stock Market Index DAX, and the Consumer Price Index for Germany can even be predicted out-of-sample more accurately with our newspaper data than with survey indices of the Ifo Institute and the OECD.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"497-512"},"PeriodicalIF":3.4,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3211","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143112563","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 Beta Using Ultra High Frequency Data 使用超高频数据预测Beta

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-11-05 DOI: 10.1002/for.3204

Jian Zhou

引用次数: 0

Robust Estimation of Multivariate Time Series Data Based on Reduced Rank Model 基于降秩模型的多元时间序列数据稳健估计

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-10-25 DOI: 10.1002/for.3205

Tengteng Xu, Ping Deng, Riquan Zhang, Weihua Zhao

{"title":"Robust Estimation of Multivariate Time Series Data Based on Reduced Rank Model","authors":"Tengteng Xu, Ping Deng, Riquan Zhang, Weihua Zhao","doi":"10.1002/for.3205","DOIUrl":"https://doi.org/10.1002/for.3205","url":null,"abstract":"<div>\u0000 \u0000 <p>Multivariate time series analysis uncovers the intricate relationships among multiple variables, which plays a vital role in areas such as policy-making and business decision-making. This paper employs a reduced rank regression model to investigate a robust estimation method for multivariate time series data using an \u0000<span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msub>\u0000 <mrow>\u0000 <mi>ℓ</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mn>1</mn>\u0000 </mrow>\u0000 </msub>\u0000 </mrow>\u0000 <annotation>$$ {ell}_1 $$</annotation>\u0000 </semantics></math> penalty. The goal is to achieve rapid parameter estimation while ensuring robustness in the analysis of time series data. This study provides a detailed description of the solution process and examines the theoretical properties of the proposed method. To evaluate its effectiveness, the proposed model is compared with full-rank regression and the multivariate regression with covariance estimation (MRCE) method through simulations, as well as an analysis of the Sceaux household electric power consumption data. The results indicate that the proposed model performs well.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"474-484"},"PeriodicalIF":3.4,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143119427","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

Exploring Multisource High-Dimensional Mixed-Frequency Risks in the Stock Market: A Group Penalized Reverse Unrestricted Mixed Data Sampling Approach 股票市场中多源高维混频风险的探索：一种组惩罚反向无限制混合数据抽样方法

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-10-09 DOI: 10.1002/for.3191

Xingxuan Zhuo, Shunfei Luo, Yan Cao

{"title":"Exploring Multisource High-Dimensional Mixed-Frequency Risks in the Stock Market: A Group Penalized Reverse Unrestricted Mixed Data Sampling Approach","authors":"Xingxuan Zhuo, Shunfei Luo, Yan Cao","doi":"10.1002/for.3191","DOIUrl":"https://doi.org/10.1002/for.3191","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper introduces a novel forecasting approach that addresses a significant challenge in applied research: effectively utilizing high-dimensional and mixed-frequency data from multiple sources to explain and predict variables that respond at high frequency. This approach combines a mixed data sampling model and group variable selection methods, resulting in the development of the Group Penalized Reverse Unrestricted Mixed Data Sampling Model (GP-RU-MIDAS). The GP-RU-MIDAS model is designed to achieve various research objectives, including analyzing mixed-frequency data in reverse, estimating high-dimensional parameters, identifying key variables, and analyzing their relative importance and sensitivity. By applying this model to uncover uncertainties in stock market returns, the following notable results emerge: (1) GP-RU-MIDAS improves the selection of relevant variables and enhances forecasting accuracy; (2) various risks impact stock market returns in diverse ways, with effects varying over time and exhibiting continuous trends, phase shifts, or extreme levels; and (3) stock market volatility and the Euro to RMB exchange rate significantly influence stock market returns over different forecasting periods, with a generally positive and dynamic impact. In conclusion, the GP-RU-MIDAS model demonstrates robustness and utility in complex data analysis scenarios, providing insights into the nuanced realm of stock market risk assessment.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"459-473"},"PeriodicalIF":3.4,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143113506","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 Multifrequency Data Fusion Deep Learning Model for Carbon Price Prediction 碳价格预测的多频数据融合深度学习模型

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-10-02 DOI: 10.1002/for.3198

Canran Xiao, Yongmei Liu

{"title":"A Multifrequency Data Fusion Deep Learning Model for Carbon Price Prediction","authors":"Canran Xiao, Yongmei Liu","doi":"10.1002/for.3198","DOIUrl":"https://doi.org/10.1002/for.3198","url":null,"abstract":"<div>\u0000 \u0000 <p>In response to the global need for effective management of carbon emissions and alignment with sustainable development goals, predicting carbon trading prices accurately is critical. This study introduces a multifrequency data fusion carbon price prediction model (MFF-CPPM), addressing the nonlinear characteristics of carbon trading prices and inconsistent feature factor frequencies. The MFF-CPPM consists of a feature-extraction frontend, a multifrequency data fusion transformer, and a fusion regression layer, offering a novel methodological approach in forecasting studies. The model's validity was tested in Guangdong, China's largest carbon trading pilot market. The results demonstrated that the MFF-CPPM outperformed baseline models in terms of carbon price-prediction accuracy and trend forecasting. Additional trials conducted in Hubei and Beijing confirmed the model's robustness and generalization capabilities, providing valuable evidence of its effectiveness and reliability across varying market contexts. This study presents a novel predictive model for carbon trading prices, with a unique capability to harness data at differing frequencies. The MFF-CPPM not only enhances forecasting accuracy but also offers an innovative approach to effectively incorporate multifrequency information. This advancement paves the way for flexible forecasting models in any scenario where data arrive at differing frequencies.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"436-458"},"PeriodicalIF":3.4,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143110981","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

Predicting Equity Premium: A New Momentum Indicator Selection Strategy With Machine Learning 预测股票溢价：一种基于机器学习的动量指标选择策略

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-10-02 DOI: 10.1002/for.3200

Yong Qu, Ying Yuan

引用次数: 0

Forecasting Expected Shortfall and Value-at-Risk With Cross-Sectional Aggregation 用横断面聚集法预测预期不足和风险价值

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-10-01 DOI: 10.1002/for.3195

Jie Wang, Yongqiao Wang

引用次数: 0

Interval Forecasting of Carbon Price With a Novel Hybrid Multiscale Decomposition and Bootstrap Approach 基于混合多尺度分解和自举法的碳价格区间预测

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-09-24 DOI: 10.1002/for.3199

Bangzhu Zhu, Chunzhuo Wan, Ping Wang, Julien Chevallier

引用次数: 0

Affinities and Complementarities of Methods and Information Sets in the Estimation of Prices in Real Estate Markets 房地产市场价格估计中方法和信息集的亲和性和互补性

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-09-20 DOI: 10.1002/for.3202

Mirko S. Bozanic-Leal, Marcel Goic, Charles Thraves

{"title":"Affinities and Complementarities of Methods and Information Sets in the Estimation of Prices in Real Estate Markets","authors":"Mirko S. Bozanic-Leal, Marcel Goic, Charles Thraves","doi":"10.1002/for.3202","DOIUrl":"https://doi.org/10.1002/for.3202","url":null,"abstract":"<div>\u0000 \u0000 <p>In this article, we evaluate the predictive power of multiple machine learning methods using different sets of information, such as location, amenities, socioeconomic characteristics, and available infrastructure nearby, in both residential and commercial real estate markets. This analysis allows us to understand what type of information is the most relevant for each market, which methods are best suited for certain explanatory variables, and the degree of complementarity among different covariates. Our results indicate that the combination of multiple data sources consistently leads to better forecasting and that flexible machine learning models outperform linear regression or spatial methods by taking advantage of the complex interactions between explanatory variables of different sources. From a substantive point of view, we found that residential sale markets have a higher prediction error compared with their rent counterparts, with house sales being the market with the largest estimation error. In terms of the explanatory power of different information sets in different markets, we observe that socioeconomic and location variables have the highest impact on the prediction for sale markets and that, in relative terms, amenities and proximity to places of interest are more important for rental than sale residential markets.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"356-375"},"PeriodicalIF":3.4,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143117813","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 of S&P 500 ESG Index by Using CEEMDAN and LSTM Approach 基于CEEMDAN和LSTM方法的标普500 ESG指数预测

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-09-20 DOI: 10.1002/for.3201

Divya Aggarwal, Sougata Banerjee

引用次数: 0