Journal of Forecasting最新文献_第5页

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

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