Journal of Forecasting最新文献

{"title":"The Impact of News Sentiment on the Bitcoin Price via Machine Learning and Deep Learning-Based NLP Models","authors":"Yunus Emre Gür,&nbsp;Emre Ünal","doi":"10.1002/for.70068","DOIUrl":"https://doi.org/10.1002/for.70068","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper employs deep learning and machine learning-based NLP models to investigate the impact of the news sentiment on the Bitcoin price. The lagged Bitcoin variables, news indicators, macroeconomic, and financial factors were taken into account to explain the importance of news sentiment on the Bitcoin price. Moreover, FinBERT-based sentiment scores and semantic features extracted from over 650,000 financial news headlines were integrated with financial and macroeconomic variables. The importance scores of the investigation showed that Bitcoin was largely explained by its lagged price movements, which suggests the speculative nature of the cryptocurrency. However, the investigation also revealed that Bitcoin was significantly influenced by the news sentiment score. In other words, the paper indicates that the movements in the Bitcoin price can be predominantly explained by the news sentiment. Advanced hybrid models (all ML and DL models with the addition of variables obtained with the FinBERT model) were optimized using Optuna and RandomizedSearchCV. The FinBERT-LSTM model achieved the best prediction accuracy. Nevertheless, the main findings indicated that the response of the Bitcoin price to negative news was much stronger than to positive and neutral news. This finding suggests that the asymmetric relationship between the Bitcoin price and news sentiment was evident. GARCH-based volatility and what-if scenario analyses further demonstrated that negative sentiment leads to sharper fluctuations in the Bitcoin price. The paper provides important implications for policymakers, portfolio managers, investors, and academics.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 3","pages":"895-923"},"PeriodicalIF":2.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147567728","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}

{"title":"A Dynamic Cost-Adjusted AdaCost Model for Credit Prediction of Smallholder Farmers","authors":"XianZhu Shao,&nbsp;YongQiang Du,&nbsp;LuoFei Liang,&nbsp;Xue Xu,&nbsp;Zhiyi Lu","doi":"10.1002/for.70069","DOIUrl":"https://doi.org/10.1002/for.70069","url":null,"abstract":"<div>\u0000 \u0000 <p>Data imbalances constrain credit prediction models. This study proposes a dynamic cost-adjusted AdaCost credit prediction model to improve prediction accuracy. Our model enriches existing research methods on the credit problems of smallholder farmers and opens new avenues for establishing credit prediction models for smallholder farmers. The present study is characterized by two distinctive research innovations: Firstly, it introduces a variable cost-sensitive function that enables adaptively adjusted misclassification costs for each sub-model generated by the AdaCost framework. This advancement effectively addresses the inherent limitation of static cost-sensitive function values in conventional AdaCost models. Secondly, the dynamic nature of the proposed cost-sensitive function induces corresponding variations in both sample weights and model weights within interconnected subsequent sub-models. The mechanism fundamentally resolves the critical oversight in traditional AdaCost methodologies that failed to account for the dynamic interdependencies between cost-sensitive functions and weight adaptation processes. Using data on agricultural loans from a commercial bank in China as empirical data and comparing them with seven baseline models, including AdaBoost, AdaCost, Cost RF, Cost XGBoost, Cost SVM, Cost GBDT, and Cost DT, we found that the proposed dynamic cost-adjusted AdaCost model outperformed the other models. Robustness tests were conducted using two publicly available loan datasets from UCI. They showed that the dynamic cost-adjusted AdaCost model performed better than the AdaBoost and AdaCost models.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 3","pages":"997-1019"},"PeriodicalIF":2.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562322","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}

{"title":"DKformer: A Novel Transformer-Based Model for Interval-Valued Crude Oil Price Forecasting","authors":"Chuanmiao Yan,&nbsp;Xinyu Zhang,&nbsp;Ruhong Cui,&nbsp;Yuying Sun,&nbsp;Shouyang Wang","doi":"10.1002/for.70070","DOIUrl":"https://doi.org/10.1002/for.70070","url":null,"abstract":"<div>\u0000 \u0000 <p>Most deep learning techniques are designed for point-valued data, while interval-valued data contain richer information over the same time period, especially during extreme events. To leverage this, we propose DKformer, a novel transformer architecture that models interval data as an inseparable set using the \u0000<span></span><math>\u0000 <msub>\u0000 <mrow>\u0000 <mi>D</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>K</mi>\u0000 </mrow>\u0000 </msub></math> loss function. By combining transformers and \u0000<span></span><math>\u0000 <msub>\u0000 <mrow>\u0000 <mi>D</mi>\u0000 </mrow>\u0000 <mrow>\u0000 <mi>K</mi>\u0000 </mrow>\u0000 </msub></math> loss, DKformer learns complex nonstationary and nonlinear patterns in interval data while accounting for uncertainties and dependencies. Compared to existing interval and point-based benchmark models, DKformer significantly enhances crude oil price forecast accuracy. Remarkably, it demonstrates robust out-of-sample prediction performance across various forecast horizons. Furthermore, the effectiveness of DKformer is preserved even under extreme conditions, including Black Swan events. Our work highlights the potential for DKformer to enhance decision-making in energy and finance, where robustness to uncertainty and the ability to handle extreme events hold paramount importance.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 3","pages":"1020-1035"},"PeriodicalIF":2.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147562324","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}

{"title":"Mortality Forecasting Using Variational Inference","authors":"Patrik Andersson,&nbsp;Mathias Lindholm","doi":"10.1002/for.70078","DOIUrl":"https://doi.org/10.1002/for.70078","url":null,"abstract":"<p>This paper considers the problem of forecasting mortality rates. A large number of models have already been proposed for this task, but they generally have the disadvantage of either estimating the model in a two-step process, possibly losing efficiency, or relying on methods that are cumbersome for the practitioner to use. We instead propose using variational inference and the probabilistic programming library Pyro for estimating the model. This allows for flexibility in modelling assumptions while still being able to estimate the full model in one step. The models are fitted on Swedish mortality data, and we find that the in-sample fit is good and that the forecasting performance is better than other popular models. Code is available online (https://github.com/LPAndersson/VImortality).</p>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 3","pages":"1069-1076"},"PeriodicalIF":2.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/for.70078","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147563108","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}

{"title":"Are the Bank of Korea's Inflation Forecasts Biased Toward the Target?","authors":"Eunkyu Seong,&nbsp;Seojeong Lee","doi":"10.1002/for.70076","DOIUrl":"https://doi.org/10.1002/for.70076","url":null,"abstract":"<div>\u0000 \u0000 <p>The Bank of Korea (BoK) regularly publishes the <i>Economic Outlook</i>, offering forecasts for key macroeconomic variables such as GDP growth, inflation, and unemployment rates. This study examines whether the BoK's inflation forecasts exhibit bias, specifically a tendency to align with its inflation target. We extend the Holden and Peel (1990) test to incorporate state dependency, defining the state of the economy based on whether realized inflation falls below the target at the time of the forecast. Our analysis reveals that the BoK's inflation forecasts are biased under this state-dependent framework. Furthermore, we examine a range of bias correction strategies based on AR(1) and mean error models, including their state-dependent variants. These strategies generally improve forecast accuracy. Among them, the AR(1)-based correction exhibits relatively stable performance, consistently reducing the root mean square error.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 3","pages":"1092-1109"},"PeriodicalIF":2.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564547","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}

{"title":"Obtaining Conservative Assessments of Profitability for Current Period Based on Target-Adjusted Achievable Capacity Index With SARIMA Prediction","authors":"Rung-Hung Su,&nbsp;Yi-Hung Kung,&nbsp;Yi-Hung Lee","doi":"10.1002/for.70085","DOIUrl":"https://doi.org/10.1002/for.70085","url":null,"abstract":"<div>\u0000 \u0000 <p>The achievable capacity index (ACI) is an effective tool for measuring and estimating the profitability of a single-period product, where profitability is the probability of achieving a target profit under optimal ordering quantity. Setting a reasonable target profit (or corresponding target demand) is crucial, as it can impact profitability measurements and result in misguided decision-making for the next period, especially when demand fluctuates over time. This study applies the seasonal autoregressive integrated moving average (SARIMA) to time-series data to account for time dependencies in predicting target demand for the next period. We then developed a new ACI, referred to as t-ACI, which incorporates the predicted target demand. In estimating the t-ACI, we may encounter the risk of overestimation due to sampling error. Therefore, we derived the lower confidence bound for the t-ACI (LCBtA) to provide a conservative assessment of profitability. Finally, we explore the conservative profitability evaluation using LCBtA to identify profitable products. Some generic tables and procedures of decision-making for evaluation are provided. Regarding insights and implications, variations in Type-I errors for prediction and estimation significantly influence the direction of decision-making in evaluations. For instance, increasing the Type-I error for estimation or decreasing it for prediction can positively steer the evaluation.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 3","pages":"1145-1157"},"PeriodicalIF":2.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147565917","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}

{"title":"Exploiting Functional Time Series Prediction for PM2.5 Based on Multivariate Variational Mode Decomposition and Anomaly Detection","authors":"Zhifu Tao,&nbsp;Weiying Liu,&nbsp;Qin Xu,&nbsp;Piao Wang","doi":"10.1002/for.70075","DOIUrl":"https://doi.org/10.1002/for.70075","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents a novel approach to high-frequency time series forecasting in the context of functional time series, addressing challenges such as data complexity and outliers. The proposed hybrid model integrates outlier detection, multivariate variational mode decomposition (MVMD), and model pooling to enhance forecasting accuracy. Initially, outliers are identified using the isolation forest technique and subsequently replaced with smoothed values via a sliding window moving average. MVMD is then employed to decompose the time series into high-, mid-, and low-frequency components, based on sample entropy. Discrete daily observations are transformed into functional data using Fourier basis functions, and functional principal component analysis (FPCA) is applied for dimensionality reduction, generating principal component scores and functions. Forecasting is carried out through model pooling, which combines statistical, machine learning, and deep learning techniques to predict the principal component scores. The final prediction is obtained by aggregating the forecasts of the predicted scores and their corresponding principal component functions. Empirical results, based on PM2.5 forecasting, demonstrate that the proposed approach significantly outperforms alternative models, offering valuable contributions to air quality monitoring and informed decision-making.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 3","pages":"1077-1091"},"PeriodicalIF":2.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147564118","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}

{"title":"A Comparative Forecasting Framework for Turkey–Germany Trade: Evidence From Time Series and Artificial Neural Networks Models","authors":"Seyma Nur Unal,&nbsp;Huseyin Karamelikli","doi":"10.1002/for.70084","DOIUrl":"https://doi.org/10.1002/for.70084","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper examines the trade relationship between Turkey and Germany by generating strategically consistent forecasts for import and export flows using time series models and artificial neural networks (ANNs). Utilizing monthly trade data from 2002 to 2021, the study compares traditional time series approaches with nonlinear autoregressive exogenous (NARX) ANNs. The results show that a single-hidden-layer NARX model with four lags provides the most accurate forecasts across commodity sections, outperforming alternative specifications. Although the models effectively capture overall trade dynamics, the analysis indicates that forecast performance varies across disaggregated sectors. The study demonstrates the usefulness of ANN-based forecasting for short-term trade planning while also noting limitations related to data length and model generalizability. The findings offer policy-relevant insights for improving trade strategy, enhancing early-warning mechanisms, and supporting data-driven decision-making in bilateral trade management.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 3","pages":"1177-1187"},"PeriodicalIF":2.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566070","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}

{"title":"A Rich-Spatial and Multiscaled Transformer-Based Approach for Long-Term Multivariate Time-Series Forecasting Problem","authors":"Linh Nguyen Thi My,&nbsp;Vu Nguyen,&nbsp;Tham Vo","doi":"10.1002/for.70066","DOIUrl":"https://doi.org/10.1002/for.70066","url":null,"abstract":"<div>\u0000 \u0000 <p>In the era of big data and the Internet of Things (IoT), the rapid growth of online data presents both challenges in management and opportunities for knowledge discovery. Among various data structures, time-series data is widely used across domains such as finance, healthcare, and environmental monitoring. Within this field, multivariate time-series forecasting is crucial for predicting complex, interdependent temporal patterns, making it a key focus for applications like stock market analysis, air quality monitoring, and energy forecasting. Generally speaking, the long-sequential multivariate time-series forecasting task is always considered as challenging as it requires the in-depth capability of sufficiently preserving the joined intradependence and interdependence between the variables within the time-series data in forms of a multichannel learning approach. The long sequence length of multivariate input and predicted data poses a significant challenge for time-series forecasting models, making it difficult to effectively learn temporal and dynamic patterns from historical observations while ensuring accurate long-term predictions. Even with advancements in deep learning (DL), including state-of-the-art transformer-based architecture, the application of multichannel learning for complex multivariate time-series data remains an open research problem. To address this limitation, we propose MCTMF, a novel multichannel transformer-based forecasting technique. Our proposed MCTMF model can assist in extending the series-aware time-series learning framework by incorporating a CNN-based multichannel learning mechanism within the temporal feature encoding process. This enhancement allows MCTMF to effectively capture and model intricate rich-spatial dependencies across multiple variables, significantly improving forecasting accuracy for complex multivariate time-series data. The extensive experiments within real-world multivariate time-series datasets have validated the outperformance of our proposed MCTMF model against the contemporary state-of-the-art transformer-based forecasting models.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 3","pages":"964-976"},"PeriodicalIF":2.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147570003","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}

{"title":"Exploring the Forecasting of Crude Oil, Gold, and Euro Currency Implied Volatility Indices: Insights From the Decomposed Stock Market Volatility","authors":"Gongyue Jiang,&nbsp;Gaoxiu Qiao,&nbsp;Shiyuan Huang","doi":"10.1002/for.70087","DOIUrl":"https://doi.org/10.1002/for.70087","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper explores whether the information from the stock market can provide positive contents for the implied volatility prediction in the crude oil market, gold market, and foreign exchange market. Specifically, we investigate the predictive effects of realized continuous volatility, realized jump volatility, positive and negative realized semi-variations, and signed jumps from the S&amp;P 500 index on three implied volatility indices, OVX (Crude Oil Volatility Index), GVZ (Gold Volatility Index), and EVZ (Euro Volatility Index). We construct a hybrid method by combining parametric models with machine learning to explore the market spillover effects of stock market information on three markets. The empirical results show that realized measures in the stock market can provide incremental information for the prediction of the implied volatility indices, the positive and negative semi-variations of stock index showing better performance than that of jump volatility. The method of combining FNN with the parametric model shows better performance compared to SVR. The superiority of this hybrid approach is further verified based on the Model Confidence Set test. Furthermore, an economic significance evaluation confirms that the enhanced predictive accuracy translates into significant economic value.</p>\u0000 </div>","PeriodicalId":47835,"journal":{"name":"Journal of Forecasting","volume":"45 3","pages":"1203-1224"},"PeriodicalIF":2.7,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147566322","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}