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November 2021, Volume 40, Issue 7
- 1214-1229 Do local and global factors impact the emerging markets' sovereign yield curves? Evidence from a data‐rich environment
by Oguzhan Cepni & Ibrahim Ethem Guney & Doruk Kucuksarac & M. Hasan Yilmaz - 1230-1244 Convolution‐based filtering and forecasting: An application to WTI crude oil prices
by Christian Gourieroux & Joann Jasiak & Michelle Tong - 1245-1273 Agricultural commodity price dynamics and their determinants: A comprehensive econometric approach
by Jesus Crespo Cuaresma & Jaroslava Hlouskova & Michael Obersteiner - 1274-1290 Prediction of remaining time on site for e‐commerce users: A SOM and long short‐term memory study
by Ling‐Jing Kao & Chih‐Chou Chiu & Hung‐Jui Wang & Chang Yu Ko - 1291-1309 Cointegration, information transmission, and the lead‐lag effect between industry portfolios and the stock market
by Victor Troster & José Penalva & Abderrahim Taamouti & Dominik Wied - 1310-1324 The information content of uncertainty indices for natural gas futures volatility forecasting
by Chao Liang & Feng Ma & Lu Wang & Qing Zeng - 1325-1341 Human resources and corporate failure prediction modeling: Evidence from Belgium
by Xavier Brédart & Eric Séverin & David Veganzones - 1342-1375 Forecasting asset returns with network‐based metrics: A statistical and economic analysis
by Eduard Baitinger
September 2021, Volume 40, Issue 6
- 945-962 Shocks to the equity capital ratio of financial intermediaries and the predictability of stock return volatility
by Feng He & Libo Yin - 963-976 Forecasting US overseas travelling with univariate and multivariate models
by Apergis Nicholas - 977-999 Forecasting exchange rates for Central and Eastern European currencies using country‐specific factors
by Krystian Jaworski - 1000-1026 Recession probabilities for the Eurozone at the zero lower bound: Challenges to the term spread and rise of alternatives
by Ralf Fendel & Nicola Mai & Oliver Mohr - 1027-1053 Rationality and anchoring of inflation expectations: An assessment from survey‐based and market‐based measures
by Helder Ferreira de Mendonça & Pedro Mendes Garcia & José Valentim Machado Vicente - 1054-1069 Forecasting of intermittent demands under the risk of inventory obsolescence
by Kamal Sanguri & Kampan Mukherjee - 1070-1085 Global economic policy uncertainty and gold futures market volatility: Evidence from Markov regime‐switching GARCH‐MIDAS models
by Feng Ma & Xinjie Lu & Lu Wang & Julien Chevallier - 1086-1094 Testing bias in professional forecasts
by Philip Hans Franses - 1095-1117 Strategic bias and popularity effect in the prediction of economic surprises
by Luiz Félix & Roman Kräussl & Philip Stork - 1118-1130 Non‐linear mixed‐effects models for time series forecasting of smart meter demand
by Cameron Roach & Rob Hyndman & Souhaib Ben Taieb
August 2021, Volume 40, Issue 5
- 733-768 Forecasting US stock market volatility: How to use international volatility information
by Yaojie Zhang & Yudong Wang & Feng Ma - 769-791 Should crude oil price volatility receive more attention than the price of crude oil? An empirical investigation via a large‐scale out‐of‐sample forecast evaluation of US macroeconomic data
by Nima Nonejad - 792-816 An empirical study on the role of trading volume and data frequency in volatility forecasting
by Min Liu & Chien‐Chiang Lee & Wei‐Chong Choo - 817-833 The value added of the Bank of Japan's range forecasts
by Yoichi Tsuchiya - 834-848 Treating cross‐sectional and time series momentum returns as forecasts
by Oh Kang Kwon & Stephen Satchell - 849-860 Can night trading sessions improve forecasting performance of gold futures' volatility in China?
by Xuan Yao & Xiaofeng Hui & Kaican Kang - 861-882 Forecasting Baden‐Württemberg's GDP growth: MIDAS regressions versus dynamic mixed‐frequency factor models
by Konstantin Kuck & Karsten Schweikert - 883-910 Intraday conditional value at risk: A periodic mixed‐frequency generalized autoregressive score approach
by Tobias Eckernkemper & Bastian Gribisch - 911-920 Design of link prediction algorithm for complex network based on the comprehensive influence of predicting nodes and neighbor nodes
by Yang Wang & Jifa Wang - 921-941 Forecasting China's Crude Oil Futures Volatility: The Role of the Jump, Jumps Intensity, and Leverage Effect
by Jiqian Wang & Feng Ma & M.I.M. Wahab & Dengshi Huang
July 2021, Volume 40, Issue 4
- 577-602 Research constituents, intellectual structure, and collaboration pattern in the Journal of Forecasting: A bibliometric analysis
by H. Kent Baker & Satish Kumar & Debidutta Pattnaik - 603-625 A performance analysis of prediction intervals for count time series
by Annika Homburg & Christian H. Weiß & Layth C. Alwan & Gabriel Frahm & Rainer Göb - 626-635 Granger causality of bivariate stationary curve time series
by Han Lin Shang & Kaiying Ji & Ufuk Beyaztas - 636-652 The tensor auto‐regressive model
by Chelsey Hill & James Li & Matthew J. Schneider & Martin T. Wells - 653-666 Stock index forecasting: A new fuzzy time series forecasting method
by Hao Wu & Haiming Long & Yue Wang & Yanqi Wang - 667-685 Forecasting volatility with outliers in Realized GARCH models
by Guanghui Cai & Zhimin Wu & Lei Peng - 686-699 An approach to increasing forecast‐combination accuracy through VAR error modeling
by Till Weigt & Bernd Wilfling - 700-707 Point and density forecasting of macroeconomic and financial uncertainties of the USA
by Afees A. Salisu & Rangan Gupta & Ahamuefula E. Ogbonna - 708-729 Forecasting systemic risk in portfolio selection: The role of technical trading rules
by Noureddine Kouaissah & Amin Hocine
April 2021, Volume 40, Issue 3
- 367-386 Forecasting negative yield‐curve distributions
by Jae‐Yun Jun & Victor Lebreton & Yves Rakotondratsimba - 387-415 Is optimum always optimal? A revisit of the mean‐variance method under nonlinear measures of dependence and non‐normal liquidity constraints
by Mazin A.M. Al Janabi - 416-438 Predicting intraday jumps in stock prices using liquidity measures and technical indicators
by Ao Kong & Hongliang Zhu & Robert Azencott - 439-457 Forecasting financial vulnerability in the USA: A factor model approach
by Hyeongwoo Kim & Wen Shi - 458-480 Forecasting the production side of GDP
by Gregor Bäurle & Elizabeth Steiner & Gabriel Züllig - 481-499 Forecasting US inflation using Markov dimension switching
by Jan Prüser - 500-511 Dynamic VaR forecasts using conditional Pearson type IV distribution
by Wei Kuang - 512-527 Block bootstrap prediction intervals for parsimonious first‐order vector autoregression
by Jing Li - 528-546 Forecasting mortality rates with the adaptive spatial temporal autoregressive model
by Yanlin Shi - 547-574 State‐dependent evaluation of predictive ability
by Boriss Siliverstovs & Daniel S. Wochner
March 2021, Volume 40, Issue 2
- 189-212 Volatility specifications versus probability distributions in VaR forecasting
by Laura Garcia‐Jorcano & Alfonso Novales - 213-227 Forecasting real‐time economic activity using house prices and credit conditions
by Narayan Kundan Kishor - 228-242 A causal model for short‐term time series analysis to predict incoming Medicare workload
by Tasquia Mizan & Sharareh Taghipour - 243-268 Out‐of‐sample performance of bias‐corrected estimators for diffusion processes
by Zi‐Yi Guo - 269-278 Estimating the volatility of asset pricing factors
by Janis Becker & Christian Leschinski - 279-290 Estimation of healthcare expenditure per capita of Turkey using artificial intelligence techniques with genetic algorithm‐based feature selection
by Zeynep Ceylan & Abdulkadir Atalan - 291-300 Value‐at‐risk forecasting via dynamic asymmetric exponential power distributions
by Lu Ou & Zhibiao Zhao - 301-326 Forecast performance and bubble analysis in noncausal MAR(1, 1) processes
by Christian Gourieroux & Andrew Hencic & Joann Jasiak - 327-345 Stock‐induced Google trends and the predictability of sectoral stock returns
by Afees A. Salisu & Ahamuefula E. Ogbonna & Idris Adediran - 346-364 A new BISARMA time series model for forecasting mortality using weather and particulate matter data
by Víctor Leiva & Helton Saulo & Rubens Souza & Robert G. Aykroyd & Roberto Vila
January 2021, Volume 40, Issue 1
- 1-16 Market timing using combined forecasts and machine learning
by David A. Mascio & Frank J. Fabozzi & J. Kenton Zumwalt - 17-39 Are industry‐level indicators more helpful to forecast industrial stock volatility? Evidence from Chinese manufacturing purchasing managers index
by Yu Wei & Lan Bai & Kun Yang & Guiwu Wei - 40-61 Forecasting aggregate market volatility: The role of good and bad uncertainties
by Li Liu & Yudong Wang - 62-79 Neural network structure identification in inflation forecasting
by Tea Šestanović & Josip Arnerić - 80-93 Evaluating the OECD’s main economic indicators at anticipating recessions
by Máximo Camacho & Gonzalo Palmieri - 94-107 Directional news impact curve
by Stanislav Anatolyev - 108-131 What can we learn from the return predictability over the business cycle?
by Li Liu & Zhiyuan Pan & Yudong Wang - 132-143 A new insight into combining forecasts for elections: The role of social media
by Chih‐Yu Chin & Cheng‐Lung Wang - 144-161 Fiscal transparency, fiscal forecasting and budget credibility in developing countries
by Nada Azmy ElBerry & Stijn Goeminne - 162-186 Equity return predictability, its determinants, and profitable trading strategies
by Md Lutfur Rahman & Mahbub Khan & Samuel A. Vigne & Gazi Salah Uddin
December 2020, Volume 39, Issue 8
- 1179-1197 Modeling of frequency containment reserve prices with econometrics and artificial intelligence
by Emil Kraft & Dogan Keles & Wolf Fichtner - 1198-1212 Predictive models for influence of primary delays using high‐speed train operation records
by Zhongcan Li & Ping Huang & Chao Wen & Yixiong Tang & Xi Jiang - 1213-1228 Analysis of the relationship between LSTM network traffic flow prediction performance and statistical characteristics of standard and nonstandard data
by Erdem Doğan - 1229-1237 Stock index prediction based on wavelet transform and FCD‐MLGRU
by Xiaojun Li & Pan Tang - 1238-1252 Financial distress prediction model: The effects of corporate governance indicators
by Chih‐Chun Chen & Chun‐Da Chen & Donald Lien - 1253-1276 Is implied volatility more informative for forecasting realized volatility: An international perspective
by Chao Liang & Yu Wei & Yaojie Zhang - 1277-1290 Cryptocurrency volatility forecasting: A Markov regime‐switching MIDAS approach
by Feng Ma & Chao Liang & Yuanhui Ma & M.I.M. Wahab - 1291-1304 A large Bayesian VAR with a block‐specific shrinkage: A forecasting application for Italian industrial production
by Valentina Aprigliano - 1305-1323 Predictive modeling of consumer color preference: Using retail data and merchandise images
by Songtao Li & Ruoran Chen & Lijian Yang & Dinglong Huang & Simin Huang - 1324-1341 A hybrid model considering cointegration for interval‐valued pork price forecasting in China
by Dabin Zhang & Qian Li & Amin W. Mugera & Liwen Ling
November 2020, Volume 39, Issue 7
- 1001-1024 Professional forecasters' expectations, consistency, and international spillovers
by Joscha Beckmann & Robert L. Czudaj - 1025-1034 A comparison of conditional predictive ability of implied volatility and realized measures in forecasting volatility
by Yafeng Shi & Tingting Ying & Yanlong Shi & Chunrong Ai - 1035-1042 Moving average threshold heterogeneous autoregressive (MAT‐HAR) models
by Kaiji Motegi & Xiaojing Cai & Shigeyuki Hamori & Haifeng Xu - 1043-1056 Forecasting models in the manufacturing processes and operations management: Systematic literature review
by Icaro Romolo Sousa Agostino & Wesley Vieira da Silva & Claudimar Pereira da Veiga & Adriano Mendonça Souza - 1057-1080 Using the yield curve to forecast economic growth
by Parley Ruogu Yang - 1081-1097 On the forecasting of high‐frequency financial time series based on ARIMA model improved by deep learning
by Zhenwei Li & Jing Han & Yuping Song - 1098-1118 Forecasting Australia's real house price index: A comparison of time series and machine learning methods
by George Milunovich - 1119-1141 A detailed look at crude oil price volatility prediction using macroeconomic variables
by Nima Nonejad - 1142-1165 Sparse Bayesian vector autoregressions in huge dimensions
by Gregor Kastner & Florian Huber - 1166-1178 The industrial asymmetry of the stock price prediction with investor sentiment: Based on the comparison of predictive effects with SVR
by Zhenni Jin & Kun Guo & Yi Sun & Lin Lai & Zhewen Liao
September 2020, Volume 39, Issue 6
- 865-876 Cholesky–ANN models for predicting multivariate realized volatility
by Andrea Bucci - 877-886 Optimal forecast combination based on ensemble empirical mode decomposition for agricultural commodity futures prices
by Yongmei Fang & Bo Guan & Shangjuan Wu & Saeed Heravi - 887-910 Do credit booms predict US recessions?
by Marius M. Mihai - 911-926 A multi‐country dynamic factor model with stochastic volatility for euro area business cycle analysis
by Florian Huber & Michael Pfarrhofer & Philipp Piribauer - 927-933 Correcting the January optimism effect
by Philip Hans Franses - 934-943 Efficient selection of hyperparameters in large Bayesian VARs using automatic differentiation
by Joshua C. C. Chan & Liana Jacobi & Dan Zhu - 944-956 Assessment of agricultural energy consumption of Turkey by MLR and Bayesian optimized SVR and GPR models
by Zeynep Ceylan - 957-965 The predictability of stock market volatility in emerging economies: Relative roles of local, regional, and global business cycles
by Elie Bouri & Riza Demirer & Rangan Gupta & Xiaojin Sun - 966-985 Forecasting local currency bond risk premia of emerging markets: The role of cross‐country macrofinancial linkages
by Oguzhan Cepni & Rangan Gupta & I. Ethem Güney & M. Yilmaz - 986-999 A deep residual compensation extreme learning machine and applications
by Yinghao Chen & Xiaoliang Xie & Tianle Zhang & Jiaxian Bai & Muzhou Hou
August 2020, Volume 39, Issue 5
- 709-724 Forecasting with unbalanced panel data
by Badi H. Baltagi & Long Liu - 725-736 Shift‐contagion in energy markets and global crisis
by Mehdi Mili & Jean‐Michel Sahut & Frédéric Teulon - 737-756 A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting
by Guo‐Feng Fan & Yan‐Hui Guo & Jia‐Mei Zheng & Wei‐Chiang Hong - 757-768 Timescale classification in wind forecasting: A review of the state‐of‐the‐art
by Jannik Schütz Roungkvist & Peter Enevoldsen - 769-787 Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods
by Xiaobo Tang & Shixuan Li & Mingliang Tan & Wenxuan Shi - 788-796 Volatility impulse response analysis for DCC‐GARCH models: The role of volatility transmission mechanisms
by David Gabauer - 797-810 Forecasting stock volatility in the presence of extreme shocks: Short‐term and long‐term effects
by Lu Wang & Feng Ma & Guoshan Liu - 811-826 On long memory origins and forecast horizons
by J. Eduardo Vera‐Valdés - 827-840 Identifying US business cycle regimes using dynamic factors and neural network models
by Barış Soybilgen - 841-863 Model averaging estimation for conditional volatility models with an application to stock market volatility forecast
by Qingfeng Liu & Qingsong Yao & Guoqing Zhao
July 2020, Volume 39, Issue 4
- 569-579 Forecasting interest rates through Vasicek and CIR models: A partitioning approach
by Giuseppe Orlando & Rosa Maria Mininni & Michele Bufalo - 580-598 Forecasting under model uncertainty: Non‐homogeneous hidden Markov models with Pòlya‐Gamma data augmentation
by Constandina Koki & Loukia Meligkotsidou & Ioannis Vrontos - 599-614 On the predictability of crude oil market: A hybrid multiscale wavelet approach
by Stelios Bekiros & Jose Arreola Hernandez & Gazi Salah Uddin & Ahmed Taneem Muzaffar - 615-627 Spatial dependence model with feature difference
by Tommy K. Y. Cheung & Simon K. C. Cheung - 628-641 Combining multivariate volatility forecasts using weighted losses
by Adam Clements & Mark Bernard Doolan - 642-660 Short‐run wavelet‐based covariance regimes for applied portfolio management
by Theo Berger & Ramazan Gençay - 661-670 Diagnosis of diabetes mellitus using artificial neural network and classification and regression tree optimized with genetic algorithm
by Ebru Pekel Özmen & Tuncay Özcan - 671-686 Can online search data improve the forecast accuracy of pork price in China?
by Liwen Ling & Dabin Zhang & Shanying Chen & Amin W. Mugera - 687-706 Evaluation of the going‐concern status for companies: An ensemble framework‐based model
by Yu‐Feng Hsu & Wei‐Po Lee
April 2020, Volume 39, Issue 3
- 353-367 The wavelet scaling approach to forecasting: Verification on a large set of Noisy data
by Joanna Bruzda - 368-393 Do monetary policy transparency and central bank communication reduce interest rate disagreement?
by Ruttachai Seelajaroen & Pornanong Budsaratragoon & Boonlert Jitmaneeroj - 394-411 Short‐term forecasting of the US unemployment rate
by Benedikt Maas - 412-437 Revealing forecaster's preferences: A Bayesian multivariate loss function approach
by Emmanuel C. Mamatzakis & Mike G. Tsionas - 438-448 State‐space models for predicting IBNR reserve in row‐wise ordered runoff triangles: Calendar year IBNR reserves & tail effects
by Leonardo Costa & Adrian Pizzinga - 449-469 On the directional predictability of equity premium using machine learning techniques
by Jonathan Iworiso & Spyridon Vrontos - 470-488 A predictive model of train delays on a railway line
by Chao Wen & Weiwei Mou & Ping Huang & Zhongcan Li - 489-500 Regression tree model for prediction of demand with heterogeneity and censorship
by Evgeniy M. Ozhegov & Alina Ozhegova - 501-511 Real time prediction of irregular periodic time series data
by Kaimeng Zhang & Chi Tim Ng & Myung Hwan Na - 512-532 Forecasting of dependence, market, and investment risks of a global index portfolio
by Jose Arreola Hernandez & Mazin A.M. Al Janabi - 533-550 The impact of economic growth in mortality modelling for selected OECD countries
by Lydia Dutton & Athanasios A. Pantelous & Malgorzata Seklecka - 551-568 Gaussian processes for daily demand prediction in tourism planning
by Wai Kit Tsang & Dries F. Benoit
March 2020, Volume 39, Issue 2
- 117-125 Forecasting air pollution PM2.5 in Beijing using weather data and multiple kernel learning
by Xiang Xu - 126-142 Modeling and forecasting commodity market volatility with long‐term economic and financial variables
by Duc Khuong Nguyen & Thomas Walther - 143-154 Volatility forecasts using stochastic volatility models with nonlinear leverage effects
by Kenichiro McAlinn & Asahi Ushio & Teruo Nakatsuma - 155-167 Volatility forecasting with bivariate multifractal models
by Ruipeng Liu & Riza Demirer & Rangan Gupta & Mark Wohar - 168-186 Model instability in predictive exchange rate regressions
by Niko Hauzenberger & Florian Huber - 187-199 A simple parameter‐driven binary time series model
by Yang Lu - 200-219 Predictive ability and economic gains from volatility forecast combinations
by Stavroula P. Fameliti & Vasiliki D. Skintzi - 220-241 Financial market imperfections and profitability: New evidence from a large panel of US SME firms
by Nicholas Apergis - 242-259 Forecasting of electricity price through a functional prediction of sale and purchase curves
by Ismail Shah & Francesco Lisi - 260-280 Predicting loan default in peer‐to‐peer lending using narrative data
by Yufei Xia & Lingyun He & Yinguo Li & Nana Liu & Yanlin Ding - 281-295 Filtering and prediction of noisy and unstable signals: The case of Google Trends data
by Livio Fenga - 296-312 On the use of power transformations in CAViaR models
by Georgios Tsiotas - 313-333 Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts
by Frederik Kunze - 334-351 Evaluation of current research on stock return predictability
by Erhard Reschenhofer & Manveer Kaur Mangat & Christian Zwatz & Sándor Guzmics
January 2020, Volume 39, Issue 1
- 1-17 Evaluating early warning and coincident indicators of business cycles using smooth trends
by Marcos Bujosa & Antonio García‐Ferrer & Aránzazu de Juan & Antonio Martín‐Arroyo - 18-36 Forecasting and nowcasting emerging market GDP growth rates: The role of latent global economic policy uncertainty and macroeconomic data surprise factors
by Oguzhan Cepni & I. Ethem Guney & Norman R. Swanson - 37-46 Forecasting inflation using univariate continuous‐time stochastic models
by Kevin Fergusson - 47-55 A likelihood ratio and Markov chain‐based method to evaluate density forecasting
by Yushu Li & Jonas Andersson - 56-68 A novel forecasting model for the Baltic dry index utilizing optimal squeezing
by Spyros Makridakis & Andreas Merikas & Anna Merika & Mike G. Tsionas & Marwan Izzeldin - 69-83 A new approach to forecasting intermittent demand based on the mixed zero‐truncated Poisson model
by Aiping Jiang & Kwok Leung Tam & Xiaoyun Guo & Yufeng Zhang - 84-103 The dynamic effect of macroeconomic news on the euro/US dollar exchange rate
by Walid Ben Omrane & Robert Welch & Xinyao Zhou - 104-116 Using social media mining technology to improve stock price forecast accuracy
by Jia‐Yen Huang & Jin‐Hao Liu
December 2019, Volume 38, Issue 8
- 733-748 The role of forward‐ and backward‐looking information for inflation expectations formation
by Paul Hubert & Harun Mirza - 749-772 The effect of target function on the predictive performance of the two‐stage ridge estimator
by Selma Toker & Nimet Özbay - 773-787 Modeling and forecasting the oil volatility index
by João H. Gonçalves Mazzeu & Helena Veiga & Massimo B. Mariti - 788-802 A Bayesian structural model for predicting algal blooms
by Xinyu Sun & Tao Liu & Jiayin Wang - 803-819 Using accounting‐based information on young firms to predict bankruptcy
by Christian Lohmann & Thorsten Ohliger - 820-832 Challenging the spanning hypothesis at short horizons: Evidence from Norway
by Siri Valseth - 833-852 Constructing and evaluating core inflation measures from component‐level inflation data
by Edward N. Gamber & Julie K. Smith
November 2019, Volume 38, Issue 7
- 621-631 Forecasting with many predictors using Bayesian additive regression trees
by Jan Prüser - 632-648 Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria
by Umar Farooq & Muhammad Ali Jibran Qamar - 649-668 Can urban coffee consumption help predict US inflation?
by Afees A. Salisu & Raymond Swaray & Idris A. Adediran - 669-680 Out‐of‐sample volatility prediction: A new mixed‐frequency approach
by Yaojie Zhang & Feng Ma & Tianyi Wang & Li Liu - 681-698 A forecasting analysis of risk‐neutral equity and Treasury volatilities
by Ana González‐Urteaga & Belén Nieto & Gonzalo Rubio - 699-713 Do stock markets have predictive content for exchange rate movements?
by Shiu‐Sheng Chen & Cheng‐Che Hsu - 714-731 Why do EMD‐based methods improve prediction? A multiscale complexity perspective
by Jichang Dong & Wei Dai & Ling Tang & Lean Yu
September 2019, Volume 38, Issue 6
- 489-503 A note on the predictive power of survey data in nowcasting euro area GDP
by Jeong‐Ryeol Kurz‐Kim - 504-518 Forecasting economic indicators using a consumer sentiment index: Survey‐based versus text‐based data
by Minchae Song & Kyung‐shik Shin - 519-524 Information content of DSGE forecasts
by Ray C. Fair - 525-551 Predictive power of Markovian models: Evidence from US recession forecasting
by Ruilin Tian & Gang Shen - 552-563 WTI crude oil option implied VaR and CVaR: An empirical application
by Giovanni Barone‐Adesi & Marinela Adriana Finta & Chiara Legnazzi & Carlo Sala - 564-581 Oil financialization and volatility forecast: Evidence from multidimensional predictors
by Yan‐ran Ma & Qiang Ji & Jiaofeng Pan - 582-599 Trading volume and prediction of stock return reversals: Conditioning on investor types' trading
by Numan Ülkü & Olena Onishchenko - 600-619 An ensemble of LSTM neural networks for high‐frequency stock market classification
by Svetlana Borovkova & Ioannis Tsiamas
August 2019, Volume 38, Issue 5
- 375-389 The total cost of misclassification in credit scoring: A comparison of generalized linear models and generalized additive models
by Christian Lohmann & Thorsten Ohliger - 390-399 A modified sequential Monte Carlo procedure for the efficient recursive estimation of extreme quantiles
by Serdar Neslihanoglu & Paresh Date - 400-414 The role of jumps in the agricultural futures market on forecasting stock market volatility: New evidence
by Feng Ma & Yaojie Zhang & M. I. M. Wahab & Xiaodong Lai - 415-421 Combining expert‐adjusted forecasts
by Dick van Dijk & Philip Hans Franses - 422-439 Bayesian structure selection for vector autoregression model
by Chi‐Hsiang Chu & Mong‐Na Lo Huang & Shih‐Feng Huang & Ray‐Bing Chen - 440-458 Measuring large‐scale market responses and forecasting aggregated sales: Regression for sparse high‐dimensional data
by Nobuhiko Terui & Yinxing Li - 459-469 Intermittent demand forecasting in the Enterprise: Empirical verification
by Mariusz Doszyń - 470-487 Assessing time series models for forecasting international migration: Lessons from the United Kingdom
by Jakub Bijak & George Disney & Allan M. Findlay & Jonathan J. Forster & Peter W.F. Smith & Arkadiusz Wiśniowski
July 2019, Volume 38, Issue 4
- 257-276 Sentiment indices and their forecasting ability
by David A. Mascio & Frank J. Fabozzi - 277-292 Forecasting in long horizons using smoothed direct forecast
by Yaein Baek - 293-310 Point forecasting of intraday volume using Bayesian autoregressive conditional volume models
by Roman Huptas - 311-326 Does geographic location matter to stock return predictability?
by Sabri Boubaker & Imed Chkir & Lamia Chourou & Samir Saadi - 327-345 The impact of parameter uncertainty in insurance pricing and reserve with the temperature‐related mortality model
by Malgorzata Seklecka & Athanasios A. Pantelous & Colin O'Hare - 346-353 Forecasting the Dubai financial market with a combination of momentum effect with a deep belief network
by Andreas Karathanasopoulos & Mohammed Osman - 354-373 Forecasting price delay and future stock returns: The role of corporate social responsibility
by Yujing Gong & Kung‐Cheng Ho & Chia‐Chun Lo & Andreas Karathanasopoulos & I‐Ming Jiang
April 2019, Volume 38, Issue 3
- 155-174 Predicting opening spot prices using extended futures trading
by Janchung Wang & Sunwu Winfred Chen & Bo‐Ting Wang - 175-191 Real‐time inflation forecast combination for time‐varying coefficient models
by Bo Zhang - 192-206 Long‐term streamflow forecasting using artificial neural network based on preprocessing technique
by Fang‐Fang Li & Zhi‐Yu Wang & Jun Qiu - 207-221 Short‐term forecasts of economic activity: Are fortnightly factors useful?
by Libero Monteforte & Valentina Raponi - 222-235 Predictive likelihood for coherent forecasting of count time series
by Siuli Mukhopadhyay & Vurukonda Sathish - 236-255 Enhancing survey‐based investment forecasts
by Ciaran Driver & Nigel Meade
March 2019, Volume 38, Issue 2
- 73-80 Model‐based forecast adjustment: With an illustration to inflation
by Philip Hans Franses - 81-91 Forecasting private consumption with Google Trends data
by Jaemin Woo & Ann L. Owen - 92-105 Hybridizing kernel‐based fuzzy c‐means with hierarchical selective neural network ensemble model for business failure prediction
by Jiaming Liu & Chong Wu - 106-121 A class of periodic trend models for seasonal time series
by Tommaso Proietti & Martyna Marczak & Gianluigi Mazzi - 122-135 Learning dynamics in the formation of European inflation expectations
by Christina Bräuning & Carin van der Cruijsen