Journal of Forecasting最新文献_第4页

Splitting long-term and short-term financial ratios for improved financial distress prediction: Evidence from Taiwanese public companies 拆分长期和短期财务比率，改进财务困境预测：来自台湾上市公司的证据

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-27 DOI: 10.1002/for.3143

Asyrofa Rahmi, Chia-chi Lu, Deron Liang, Ayu Nur Fadilah

{"title":"Splitting long-term and short-term financial ratios for improved financial distress prediction: Evidence from Taiwanese public companies","authors":"Asyrofa Rahmi, Chia-chi Lu, Deron Liang, Ayu Nur Fadilah","doi":"10.1002/for.3143","DOIUrl":"https://doi.org/10.1002/for.3143","url":null,"abstract":"Financial distress occurs when a company cannot meet its financial obligations within a specified timeframe, often owing to prolonged poor operational performance. While studies on financial distress prediction (FDP) use financial ratios (FRs) to forecast distress, they neglect to differentiate long-term (LT) attributes from FRs. To address this gap, our study introduces a novel model that distinguishes between LT and short-term (ST) accounting attributes in FRs. Using data from Taiwanese public companies (1991–2018), our proposed model employs a stacking ensemble classifier to split LT and ST Altman's ratios. This study addresses three key questions: (1) Do models involving split of LT and ST ratios outperform those that combine them? (2) How reliable and robust are these proposed models? (3) What is the proposed model's impact on distress prediction? The results show a significant outperformance of the existing solution, with higher accuracy, lower Type I and Type II errors, and reduced misclassification costs. These models are reliable in handling imbalanced data, proving suitable for real-market investigations. Diverse FR contexts from previous Taiwanese studies validate the distinction between LT and ST features, representing robust performance. This model identifies characteristics of correctly and incorrectly predicted distress in companies, providing nuanced insights into complex distress attributes. This study introduces a pioneering model demonstrating superior predictive accuracy, reliability, and robustness by considering the split between LT and ST accounting attributes. It lays a foundation for future studies to extend and refine the proposed model, offering valuable insights into the complex dynamics of FDP.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142435855","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 the direction of the Fed's monetary policy decisions using random forest 利用随机森林预测美联储货币政策决策的方向

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-23 DOI: 10.1002/for.3144

Jungyeon Yoon, Juanjuan Fan

引用次数: 0

Regime-dependent commodity price dynamics: A predictive analysis 与时间相关的商品价格动态：预测分析

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-20 DOI: 10.1002/for.3152

Jesus Crespo Cuaresma, Ines Fortin, Jaroslava Hlouskova, Michael Obersteiner

{"title":"Regime-dependent commodity price dynamics: A predictive analysis","authors":"Jesus Crespo Cuaresma, Ines Fortin, Jaroslava Hlouskova, Michael Obersteiner","doi":"10.1002/for.3152","DOIUrl":"10.1002/for.3152","url":null,"abstract":"We develop an econometric modelling framework to forecast commodity prices taking into account potentially different dynamics and linkages existing at different states of the world and using different performance measures to validate the predictions. We assess the extent to which the quality of the forecasts can be improved by entertaining different regime-dependent threshold models considering different threshold variables. We evaluate prediction quality using both loss minimization and profit maximization measures based on directional accuracy, directional value, the ability to predict turning points, and the returns implied by a simple trading strategy. Our analysis provides overwhelming evidence that allowing for regime-dependent dynamics leads to improvements in predictive ability for the Goldman Sachs Commodity Index, as well as for its five sub-indices (energy, industrial metals, precious metals, agriculture, and livestock). Our results suggest the existence of a trade-off between predictive ability based on loss and profit measures, which implies that the particular aim of the prediction exercise carried out plays a very important role in terms of defining which set of models is the best to use.","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":null,"pages":null},"PeriodicalIF":3.4,"publicationDate":"2024-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.3152","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141122071","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

Seeing is believing: Forecasting crude oil price trend from the perspective of images 眼见为实：从图像角度预测原油价格走势

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-19 DOI: 10.1002/for.3149

Xiaohang Ren, Wenting Jiang, Qiang Ji, Pengxiang Zhai

引用次数: 0

Portfolio management based on a reinforcement learning framework 基于强化学习框架的投资组合管理

IF 3.4 3区经济学

Journal of Forecasting Pub Date : 2024-05-19 DOI: 10.1002/for.3155

Wu Junfeng, Li Yaoming, Tan Wenqing, Chen Yun

引用次数: 0

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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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