Journal of Forecasting最新文献_第9页

Traffic flow prediction: A 3D adaptive multi-module joint modeling approach integrating spatial-temporal patterns to capture global features 交通流量预测：三维自适应多模块联合建模方法：整合时空模式，捕捉全局特征

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-18 DOI: 10.1002/for.3147

Zain Ul Abideen, Xiaodong Sun, Chao Sun

{"title":"Traffic flow prediction: A 3D adaptive multi-module joint modeling approach integrating spatial-temporal patterns to capture global features","authors":"Zain Ul Abideen, Xiaodong Sun, Chao Sun","doi":"10.1002/for.3147","DOIUrl":"10.1002/for.3147","url":null,"abstract":"The challenges in citywide traffic flow are intricate, encompassing various factors like temporal and spatial dependencies, holidays, and weather. Despite the complexity, there are still research gaps in effectively incorporating these spatio-temporal relations through deep learning. Addressing these gaps is crucial for tackling issues such as traffic congestion, public safety, and efficient traffic management within cities. This paper underscores notable research gaps, including the development of models capable of handling both local and global traffic flow patterns, integrating multi-modal data sources, and effectively managing spatio-temporal dependencies. In this paper, we proposed a novel model named 3D spatial–temporal-based adaptive modeling graph convolutional network (3D(STAMGCN)) that addresses for traffic flow data in better periodicity modeling. In contrast to earlier studies, 3D(STAMGCN) approaches the task of traffic flow prediction as a periodic residual learning problem. This is achieved by capturing the input variation between historical time segments and the anticipated output for future time segments. Forecasting traffic flow, as opposed to a direct approach, is significantly simpler when focusing on learning more stationary deviations. This, in turn, aids in the training of the model. Nevertheless, the networks enable residual generation at each time interval through learned variations between future conditions and their corresponding weekly observations. Consequently, this significantly contributes to achieving more accurate forecasts for multiple steps ahead. We executed extensive experiments on two real-world datasets and compared the performance of our model to state-of-the-art (SOTA) techniques.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2766-2791"},"PeriodicalIF":3.4,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063335","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 tail risk of skewed financial returns having exponential-polynomial tails 预测具有指数-多项式尾部的倾斜金融收益的尾部风险

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-15 DOI: 10.1002/for.3154

Albert Antwi, Emmanuel N. Gyamfi, Anokye M. Adam

{"title":"Forecasting tail risk of skewed financial returns having exponential-polynomial tails","authors":"Albert Antwi, Emmanuel N. Gyamfi, Anokye M. Adam","doi":"10.1002/for.3154","DOIUrl":"10.1002/for.3154","url":null,"abstract":"Aggregated long and short trading risk positions of speculative assets over time are likely to be unequal. This may be because of irrational decisions of traders and investors as well as catastrophic events that lead to pronounce or salient market crashes. Returns of such assets are therefore more likely to have one polynomial tail and one exponential tail. The generalized hyperbolic (GH) skewed Student-t distribution is known to handle such situations quite well. In this paper, we use generalized autoregressive conditional heteroscedasticity (GARCH) models to empirically show the superiority of the GH skewed Student-t distribution in forecasting the extreme tail risks of cryptocurrency returns in the presence of substantial skewness in comparison with some competing distributions. Furthermore, we show the practical significance of the GH skewed Student-t distribution-based risk forecasts in computing daily capital requirements. Evidence from the study suggests that the GH skewed Student-t distribution model tends to be superior in forecasting volatility and expected shortfall (ES) but not value-at-risk. In addition, the distribution yields higher value-at-risk (VaR) exceptions but surprisingly avoids the red zone of the Basel II accord penalty zones and produces lower but optimal daily capital requirements. Therefore, in the presence of substantially skewed returns having exponential-polynomial tails, we recommend the use of the GH skewed Student-t distribution for parametric GARCH models in forecasting extreme tail risk.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2731-2748"},"PeriodicalIF":3.4,"publicationDate":"2024-05-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3154","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140976844","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

Volatility forecasting incorporating intraday positive and negative jumps based on deep learning model 基于深度学习模型的包含日内正负跳变的波动率预测

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-15 DOI: 10.1002/for.3146

Yilun Zhang, Yuping Song, Ying Peng, Hanchao Wang

引用次数: 0

A deep learning-based multivariate decomposition and ensemble framework for container throughput forecasting 基于深度学习的集装箱吞吐量预测多变量分解和集合框架

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-14 DOI: 10.1002/for.3151

Anurag Kulshrestha, Abhishek Yadav, Himanshu Sharma, Shikha Suman

{"title":"A deep learning-based multivariate decomposition and ensemble framework for container throughput forecasting","authors":"Anurag Kulshrestha, Abhishek Yadav, Himanshu Sharma, Shikha Suman","doi":"10.1002/for.3151","DOIUrl":"10.1002/for.3151","url":null,"abstract":"Traditional linear models struggle to capture the intricate relationship between dynamic container throughput and its complex interplay with economic fluctuations. This study introduces a novel, deep learning-based multivariate framework for precision in demanding landscapes. The framework consistently outperforms eight established benchmark models by employing vital economic indicators like GDP and port tonnage, identified through rigorous predictor importance analysis of an initial set of four variables, including imports and exports. Statistical significance is demonstrably achieved through the Diebold–Mariano and Wilcoxon rank-sum tests. Utilizing the Port of Singapore as a case study, the framework offers agile adaptability for the ever-evolving global supply chain. Comprehensive analyses ensure robustness, decoding intricate throughput dynamics. Incorporating noise-assisted multivariate empirical mode decomposition (NA-MEMD) for nonlinear decomposition and bidirectional long short-term memory (BiLSTM) for time series dependencies, this innovative approach holds promise for revolutionizing container throughput forecasting and enhancing competitiveness in the global market through optimized resource allocation and streamlined operations.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2685-2704"},"PeriodicalIF":3.4,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140978994","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 stock returns with industry volatility concentration 利用行业波动集中度预测股票回报率

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-14 DOI: 10.1002/for.3150

Yaojie Zhang, Mengxi He, Zhikai Zhang

引用次数: 0

Time-varying risk preference and equity risk premium forecasting: The role of the disposition effect 时变风险偏好与股票风险溢价预测：处置效应的作用

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-10 DOI: 10.1002/for.3145

Kenan Qiao, Haibin Xie

{"title":"Time-varying risk preference and equity risk premium forecasting: The role of the disposition effect","authors":"Kenan Qiao, Haibin Xie","doi":"10.1002/for.3145","DOIUrl":"10.1002/for.3145","url":null,"abstract":"This study examines whether the disposition effect can explain time-varying risk preference and predict the equity risk premium. To do so, we propose an augmented general autoregressive conditional heteroskedasticity (GARCH)-in-Mean model unraveling the complex relationship between unrealized gains/losses, realized returns, and the equity risk premium. In our model, the risk aversion coefficient varies with the market state of unrealized gains/losses. Using data from the US stock markets, we show strong evidence that the disposition effect drives time-varying risk preference: The risk aversion coefficient is significantly positive during periods of unrealized gains, but insignificant during periods of unrealized losses. These findings reconcile the conflicting results of the risk-return trade-off in existing literature. Moreover, our model shows significant predictability of the equity risk premium, both in-sample and out-of-sample. Incorporating our model's predictions can yield substantial utility gains for a mean-variance investor. Our results indicate that the disposition effect leads to time-varying risk preference and thus induces equity risk premium predictability.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2659-2674"},"PeriodicalIF":3.4,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937267","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 study and development of high-order fuzzy time series forecasting methods for air quality index forecasting 用于空气质量指数预报的高阶模糊时间序列预报方法的研究与开发

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-10 DOI: 10.1002/for.3153

Sushree Subhaprada Pradhan, Sibarama Panigrahi

{"title":"A study and development of high-order fuzzy time series forecasting methods for air quality index forecasting","authors":"Sushree Subhaprada Pradhan, Sibarama Panigrahi","doi":"10.1002/for.3153","DOIUrl":"10.1002/for.3153","url":null,"abstract":"The endless adverse effects of air pollution incidents have raised significant public concerns in the past few decades. The measure of air pollution, that is, the air quality index (AQI), is highly volatile and associated with different kinds of uncertainties. Following this, the study and development of accurate fuzzy time series forecasting (TSF) methods for predicting the AQI have a significant role in air pollution control and management. Motivated by this, in this paper, a systematic study is made to evaluate the true potential of fuzzy TSF methods employing traditional fuzzy set (TFS), intuitionistic fuzzy set (IFS), hesitant fuzzy set (HFS), and neutrosophic fuzzy set (NFS) in forecasting the AQI. Two novel high-order fuzzy TSF methods, TFS-multilayer perceptron (MLP) and HFS-MLP, are proposed employing TFS and HFS in which ratio trend variation of AQI data is used instead of original AQI, MLP is used to model the fuzzy logical relationships (FLRs), and none/mean of aggregated membership values are used while modeling the FLRs using MLP. The results from the proposed fuzzy TSF methods are compared with recently proposed fuzzy TSF methods employing TFS, IFS, and NFS and six popular machine learning models, including MLP, support vector regression (SVR), Bagging Regressors, XGBoost, long-short term memory (LSTM), and convolutional neural network (CNN). The “Wilcoxon Signed-Rank test” and “Friedman and Nemenyi hypothesis test” are applied to the results obtained by employing different ratios in the train-validation-test to draw decisive conclusions reliably. The simulation results show the statistical dominance of the proposed TFS-MLP method over all other crisp and fuzzy TSF methods employed in this paper.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2635-2658"},"PeriodicalIF":3.4,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140936965","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 multistage forecasting model for green bond cost optimization with dynamic corporate risk constraints 具有动态企业风险约束的绿色债券成本优化多阶段预测模型

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-10 DOI: 10.1002/for.3142

Zinan Hu, Ruicheng Yang, Sumuya Borjigin

{"title":"A multistage forecasting model for green bond cost optimization with dynamic corporate risk constraints","authors":"Zinan Hu, Ruicheng Yang, Sumuya Borjigin","doi":"10.1002/for.3142","DOIUrl":"10.1002/for.3142","url":null,"abstract":"This study develops a multi-stage stochastic model to forecast the issuance of green bonds using the Filtered Historical Simulation (FHS) method to identify the most cost-effective conditions for issuing these bonds amid various risk factors. Drawing on historical yield data and financial metrics of corporate green bonds from December 2014 to June 2023, the model considers fluctuating elements such as risk probabilities, financial risks in worst-case scenarios, and liquidity risks at upcoming issuance moments. Our findings reveal the model's effectiveness in pinpointing the lowest possible costs of issuing new green bond portfolios in the future, while also addressing expected financial risk, risk occurrence probability, and liquidity issues. The results provide issuers with the insights needed to accurately time the market, tailor bond maturities according to a corporation's future risk profile, and enhance liquidity management. Notably, our model indicates that refining the estimated probability of future risk occurrences can lead to significant savings in green bond issuance costs. This approach allows for adaptable bond issuance strategies, addresses inherent debt, and enables detailed risk management, offering substantial benefits for green enterprises navigating the complexities of future financial landscapes.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2607-2634"},"PeriodicalIF":3.4,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140936875","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

Twitter policy uncertainty and stock returns in South Africa: Evidence from time-varying Granger causality 南非 Twitter 政策的不确定性与股票回报：来自时变格兰杰因果关系的证据

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-10 DOI: 10.1002/for.3148

Kingstone Nyakurukwa, Yudhvir Seetharam

引用次数: 0

Takeover in Europe: Target characteristics and acquisition likelihood 欧洲的收购：目标特征与收购可能性

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-09 DOI: 10.1002/for.3135

Hicham Meghouar

{"title":"Takeover in Europe: Target characteristics and acquisition likelihood","authors":"Hicham Meghouar","doi":"10.1002/for.3135","DOIUrl":"10.1002/for.3135","url":null,"abstract":"This article analyzes characteristics of takeover targets in the European market—relatively less studied compared with US and UK markets—to develop a takeover prediction model. Our sample includes 320 European companies with 140 targets and 180 non-targets over the period 1994–2007, covering two M&A waves. In this study, we test the discriminating power of many relevant variables including new one that could have a discriminating power in potentially determining (value creation). Our results show that European targets are characterized by a growth-resource imbalance, are less rich in FCF, have growth opportunities, have a higher level of transaction volume of shares prior to the bid, achieve lower economic performance, and destroy value. Furthermore, we develop several predictive models using targets' financial data from 1 year, 2 years, and 3 years before takeover, along with the 3-year average. The correct classification power in the original sample is 70% (in-sample). As for predictive ability, the correct classification power in a control sample is 79.4% (out-of-sample). We also noted that predictive models using data from 1 or 2 years before the bid appear to display more significant predictive ability.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"43 7","pages":"2588-2606"},"PeriodicalIF":3.4,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140937046","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