Journal of Forecasting最新文献_第3页

Exploring Multisource High-Dimensional Mixed-Frequency Risks in the Stock Market: A Group Penalized Reverse Unrestricted Mixed Data Sampling Approach

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

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

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

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

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

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

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

Could Diffusion Indexes Have Forecasted the Great Depression?

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-09-15 DOI: 10.1002/for.3196

Gabriel Mathy, Yongchen Zhao

引用次数: 0

Stock Price Limit and Its Predictability in the Chinese Stock Market

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-09-12 DOI: 10.1002/for.3197

Haohui Liang, Yujia Hu

引用次数: 0

Deep Dive Into Churn Prediction in the Banking Sector: The Challenge of Hyperparameter Selection and Imbalanced Learning 深入研究银行业的客户流失预测：超参数选择和不平衡学习的挑战

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-09-05 DOI: 10.1002/for.3194

Vasileios Gkonis, Ioannis Tsakalos

{"title":"Deep Dive Into Churn Prediction in the Banking Sector: The Challenge of Hyperparameter Selection and Imbalanced Learning","authors":"Vasileios Gkonis, Ioannis Tsakalos","doi":"10.1002/for.3194","DOIUrl":"10.1002/for.3194","url":null,"abstract":"<p>Forecasting customer churn has long been a major issue in the banking sector because the early identification of customer exit is crucial for the sustainability of banks. However, modeling customer churn is hampered by imbalanced data between classification classes, where the churn class is typically significantly smaller than the no-churn class. In this study, we examine the performance of deep neural networks for predicting customer churn in the banking sector, while incorporating various resampling techniques to overcome the challenges posed by imbalanced datasets. In this work we propose the utilization of the APTx activation function to enhance our model’s forecasting ability. In addition, we compare the effectiveness of different combinations of activation functions, optimizers, and resampling techniques to identify configurations that yield promising results for predicting customer churn. Our results offer dual insights, enriching the existing literature in the field of hyperparameter selection, imbalanced learning, and churn prediction, while also revealing that APTx can be a promising component in the field of neural networks.</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"44 2","pages":"281-296"},"PeriodicalIF":3.4,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3194","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142191687","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