Research classified by Journal of Economic Literature (JEL) codes

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Top JEL
/ C: Mathematical and Quantitative Methods
/ / C4: Econometric and Statistical Methods: Special Topics
/ / / C45: Neural Networks and Related Topics

2023

• Chuliá, Helena & Estévez, Marc & Uribe, Jorge M., 2023, "Systemic political risk," Economic Modelling, Elsevier, volume 125, issue C, DOI: 10.1016/j.econmod.2023.106375.
• Li, Houjian & Zhou, Deheng & Hu, Jiayu & Li, Junwen & Su, Mengying & Guo, Lili, 2023, "Forecasting the realized volatility of Energy Stock Market: A multimodel comparison," The North American Journal of Economics and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.najef.2023.101895.
• Amendolagine, Vito & von Jacobi, Nadia, 2023, "Symbiotic relationships among formal and informal institutions: Comparing five Brazilian cultural ecosystems," Economic Systems, Elsevier, volume 47, issue 3, DOI: 10.1016/j.ecosys.2023.101092.
• Conti, Antonio M. & Nobili, Andrea & Signoretti, Federico M., 2023, "Bank capital requirement shocks: A narrative perspective," European Economic Review, Elsevier, volume 151, issue C, DOI: 10.1016/j.euroecorev.2022.104254.
• Saâdaoui, Foued & Ben Jabeur, Sami, 2023, "Analyzing the influence of geopolitical risks on European power prices using a multiresolution causal neural network," Energy Economics, Elsevier, volume 124, issue C, DOI: 10.1016/j.eneco.2023.106793.
• Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023, "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, volume 125, issue C, DOI: 10.1016/j.eneco.2023.106843.
• Zhao, Yinglan & Feng, Chen & Xu, Nuo & Peng, Song & Liu, Chang, 2023, "Early warning of exchange rate risk based on structural shocks in international oil prices using the LSTM neural network model," Energy Economics, Elsevier, volume 126, issue C, DOI: 10.1016/j.eneco.2023.106921.
• Bennedsen, Mikkel & Hillebrand, Eric & Jensen, Sebastian, 2023, "A neural network approach to the environmental Kuznets curve," Energy Economics, Elsevier, volume 126, issue C, DOI: 10.1016/j.eneco.2023.106985.
• Wongsinhirun, Nopparat & Chatjuthamard, Pattanaporn & Jiraporn, Pornsit, 2023, "Corporate culture and board gender diversity: Evidence from textual analysis," International Review of Financial Analysis, Elsevier, volume 86, issue C, DOI: 10.1016/j.irfa.2023.102534.
• Lee, Kyungsub, 2023, "Recurrent neural network based parameter estimation of Hawkes model on high-frequency financial data," Finance Research Letters, Elsevier, volume 55, issue PA, DOI: 10.1016/j.frl.2023.103922.
• Gao, Wei & Ju, Ming & Yang, Tongyang, 2023, "Severe weather and peer-to-peer farmers’ loan default predictions: Evidence from machine learning analysis," Finance Research Letters, Elsevier, volume 58, issue PA, DOI: 10.1016/j.frl.2023.104287.
• Barua, Ronil & Sharma, Anil K., 2023, "Using fear, greed and machine learning for optimizing global portfolios: A Black-Litterman approach," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104515.
• Coita, Ioana-Florina & Belbe, Stefana (Ștefana) & Mare, Codruta (Codruța) & Osterrieder, Joerg & Hopp, Christian, 2023, "Modelling taxpayers’ behaviour based on prediction of trust using sentiment analysis," Finance Research Letters, Elsevier, volume 58, issue PC, DOI: 10.1016/j.frl.2023.104549.
• Galil, Koresh & Hauptman, Ami & Rosenboim, Rosit Levy, 2023, "Prediction of corporate credit ratings with machine learning: Simple interpretative models," Finance Research Letters, Elsevier, volume 58, issue PD, DOI: 10.1016/j.frl.2023.104648.
• Huber, Martin & Imhof, David, 2023, "Flagging cartel participants with deep learning based on convolutional neural networks," International Journal of Industrial Organization, Elsevier, volume 89, issue C, DOI: 10.1016/j.ijindorg.2023.102946.
• Decarolis, Francesco & Li, Muxin, 2023, "Regulating online search in the EU: From the android case to the digital markets act and digital services act," International Journal of Industrial Organization, Elsevier, volume 90, issue C, DOI: 10.1016/j.ijindorg.2023.102983.
• Fissler, Tobias & Merz, Michael & Wüthrich, Mario V., 2023, "Deep quantile and deep composite triplet regression," Insurance: Mathematics and Economics, Elsevier, volume 109, issue C, pages 94-112, DOI: 10.1016/j.insmatheco.2023.01.001.
• Olivares, Kin G. & Challu, Cristian & Marcjasz, Grzegorz & Weron, Rafał & Dubrawski, Artur, 2023, "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," International Journal of Forecasting, Elsevier, volume 39, issue 2, pages 884-900, DOI: 10.1016/j.ijforecast.2022.03.001.
• Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023, "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," International Journal of Forecasting, Elsevier, volume 39, issue 3, pages 1145-1162, DOI: 10.1016/j.ijforecast.2022.04.009.
• Kaniel, Ron & Lin, Zihan & Pelger, Markus & Van Nieuwerburgh, Stijn, 2023, "Machine-learning the skill of mutual fund managers," Journal of Financial Economics, Elsevier, volume 150, issue 1, pages 94-138, DOI: 10.1016/j.jfineco.2023.07.004.
• Schade, Philipp & Schuhmacher, Monika C., 2023, "Predicting entrepreneurial activity using machine learning," Journal of Business Venturing Insights, Elsevier, volume 19, issue C, DOI: 10.1016/j.jbvi.2022.e00357.
• Liu, Zhenya & Teka, Hanen & You, Rongyu, 2023, "Conditional autoencoder pricing model for energy commodities," Resources Policy, Elsevier, volume 86, issue PA, DOI: 10.1016/j.resourpol.2023.104060.
• Teplova, Tamara & Sokolova, Tatiana & Kissa, David, 2023, "Revealing stock liquidity determinants by means of explainable AI: The role of ESG before and during the COVID-19 pandemic," Resources Policy, Elsevier, volume 86, issue PB, DOI: 10.1016/j.resourpol.2023.104253.
• Pirayesh Neghab, Davood & Bradrania, Reza & Elliott, Robert, 2023, "Deliberate premarket underpricing: New evidence on IPO pricing using machine learning," International Review of Economics & Finance, Elsevier, volume 88, issue C, pages 902-927, DOI: 10.1016/j.iref.2023.07.008.
• Bouteska, Ahmed & Hajek, Petr & Fisher, Ben & Abedin, Mohammad Zoynul, 2023, "Nonlinearity in forecasting energy commodity prices: Evidence from a focused time-delayed neural network," Research in International Business and Finance, Elsevier, volume 64, issue C, DOI: 10.1016/j.ribaf.2022.101863.
• Wang, Dan & Chen, Zhi & Florescu, Ionuţ & Wen, Bingyang, 2023, "A sparsity algorithm for finding optimal counterfactual explanations: Application to corporate credit rating," Research in International Business and Finance, Elsevier, volume 64, issue C, DOI: 10.1016/j.ribaf.2022.101869.
• Ballester, Laura & López, Jesúa & Pavía, Jose M., 2023, "European systemic credit risk transmission using Bayesian networks," Research in International Business and Finance, Elsevier, volume 65, issue C, DOI: 10.1016/j.ribaf.2023.101914.
• Mtiraoui, Amine & Boubaker, Heni & BelKacem, Lotfi, 2023, "A hybrid approach for forecasting bitcoin series," Research in International Business and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.ribaf.2023.102011.
• Duan, Kun & Wang, Rui & Chen, Shun & Ge, Lei, 2023, "Exploring the predictability of attention mechanism with LSTM: Evidence from EU carbon futures prices," Research in International Business and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.ribaf.2023.102020.
• Bouteska, Ahmed & Harasheh, Murad & Abedin, Mohammad Zoynul, 2023, "Revisiting overconfidence in investment decision-making: Further evidence from the U.S. market," Research in International Business and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.ribaf.2023.102028.
• Grudniewicz, Jan & Ślepaczuk, Robert, 2023, "Application of machine learning in algorithmic investment strategies on global stock markets," Research in International Business and Finance, Elsevier, volume 66, issue C, DOI: 10.1016/j.ribaf.2023.102052.
• Motohashi, Kazuyuki & Zhu, Chen, 2023, "Identifying technology opportunity using dual-attention model and technology-market concordance matrix," Technological Forecasting and Social Change, Elsevier, volume 197, issue C, DOI: 10.1016/j.techfore.2023.122916.
• Cesar Ramos, 2023, "Machine Learning Insights into Bolivia’s Economic Downturns," Cuadernos de Investigación Económica Boliviana, Ministerio de Economía y Finanzas Públicas de Bolivia, volume 6, issue 2, pages 5-33, December.
• Driss El Kadiri Boutchich, 2023, "Painting art and sustainability: relationship from composite indices and a neural network," International Journal of Social Economics, Emerald Group Publishing Limited, volume 51, issue 1, pages 46-61, July, DOI: 10.1108/IJSE-01-2023-0006.
• Marko Kureljusic & Jonas Metz, 2023, "The applicability of machine learning algorithms in accounts receivables management," Journal of Applied Accounting Research, Emerald Group Publishing Limited, volume 24, issue 4, pages 769-786, February, DOI: 10.1108/JAAR-05-2022-0116.
• Marko Kureljusic & Erik Karger, 2023, "Forecasting in financial accounting with artificial intelligence – A systematic literature review and future research agenda," Journal of Applied Accounting Research, Emerald Group Publishing Limited, volume 25, issue 1, pages 81-104, May, DOI: 10.1108/JAAR-06-2022-0146.
• Mohd Nayyer Rahman & Badar Alam Iqbal & Nida Rahman, 2023, "Impact of US-China trade war on Asian economies: neural network multilayer perceptron approach," Journal of Chinese Economic and Foreign Trade Studies, Emerald Group Publishing Limited, volume 16, issue 2, pages 172-189, March, DOI: 10.1108/JCEFTS-08-2022-0056.
• Özgür İcan & Taha Buğra Çelik, 2023, "Weak-form market efficiency and corruption: a cross-country comparative analysis," Journal of Capital Markets Studies, Emerald Group Publishing Limited, volume 7, issue 1, pages 72-90, April, DOI: 10.1108/JCMS-12-2022-0046.
• Chronopoulos, Ilias & Raftapostolos, Aristeidis & Kapetanios, George, 2023, "Forecasting Value-at-Risk using deep neural network quantile regression," Essex Finance Centre Working Papers, University of Essex, Essex Business School, number 34837, Feb.
• Kazuyuki MOTOHASHI, 2023, "Identifying Technology Opportunity Using a Dual-attention Model and a Technology-market Concordance Matrix," Discussion papers, Research Institute of Economy, Trade and Industry (RIETI), number 23024, Mar.
• Ilias Chronopoulos & Katerina Chrysikou & George Kapetanios & James Mitchell & Aristeidis Raftapostolos, 2023, "Deep Neural Network Estimation in Panel Data Models," Working Papers, Federal Reserve Bank of Cleveland, number 23-15, Jul, DOI: 10.26509/frbc-wp-202315.
• Thomas R. Cook & Nathan M. Palmer, 2023, "Understanding Models and Model Bias with Gaussian Processes," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 23-07, Jun, DOI: 10.18651/RWP2023-07.
• Thomas R. Cook & Sophia Kazinnik & Anne Lundgaard Hansen & Peter McAdam, 2023, "Evaluating Local Language Models: An Application to Bank Earnings Calls," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 23-12, Nov.
• Maximilian Ahrens & Deniz Erdemlioglu & Michael McMahon & Christopher J. Neely & Xiye Yang, 2023, "Mind Your Language: Market Responses to Central Bank Speeches," Working Papers, Federal Reserve Bank of St. Louis, number 2023-013, May, revised 28 Sep 2024, DOI: 10.20955/wp.2023.013.
• Miguel Faria-e-Castro & Fernando Leibovici, 2023, "Artificial Intelligence and Inflation Forecasts," Working Papers, Federal Reserve Bank of St. Louis, number 2023-015, Jul, revised 26 Feb 2024, DOI: 10.20955/wp.2023.015.
• Anastasia D. Petaykina, 2023, "Predicting Changes in Household Consumption Using Neural Networks
  [Прогнозирование Изменений Потребления Домашних Хозяйств С Использованием Нейронных Сетей]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 7, pages 42-53, July.
• Yury A. Pleskachyev & Yury Yu. Ponomarev & Matvey A. Saprykin, 2023, "Territorial Planning and Forecasting of Economic Indicators by Machine Learning Methods
  [Территориальное Планирование И Прогнозирование Экономических Показателей Методами Машинного Обучения]," Russian Economic Development, Gaidar Institute for Economic Policy, issue 9, pages 46-57, September.
• Anastasia D. Petaykina, 2023, "Прогнозирование Изменений Потребления Домашних Хозяйств С Использованием Нейронных Сетей," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 7, pages 42-53, July.
• Yury A. Pleskachyev & Yury Yu. Ponomarev & Matvey A. Saprykin, 2023, "Территориальное Планирование И Прогнозирование Экономических Показателей Методами Машинного Обучения," Russian Economic Development (in Russian), Gaidar Institute for Economic Policy, issue 9, pages 46-57, September.
• Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2023, "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," Post-Print, HAL, number emse-04624940, Jul, DOI: 10.1016/j.ijforecast.2022.04.009.
• Sahed Abdelkader & Kahoui Hacene, 2023, "Electricity Consumption Forecasting in Algeria using ARIMA and Long Short-Term Memory Neural Network," Post-Print, HAL, number hal-04183403, Jun.
• Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023, "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print, HAL, number hal-04223161, DOI: 10.1007/s11156-023-01192-x.
• Celso Brunetti & Marc Joëts & Valérie Mignon, 2023, "Reasons Behind Words: OPEC Narratives and the Oil Market," Working Papers, HAL, number hal-04196053.
• Vitor Joao Pereira Domingues MARTINHO, 2023, "Energy Crops: Assessments In The European Union Agricultural Regions Through Machine Learning Approaches," Regional Science Inquiry, Hellenic Association of Regional Scientists, volume 0, issue 1, pages 29-42, June.
• Alejandro Vargas Sánchez & André Nicolas Monje Prudencio, , "Optimización de Carteras de Renta Variable con Aprendizaje Automático," Investigación & Desarrollo, Universidad Privada Boliviana, number 0223, DOI: 10.23881/idupbo.023.2-2e.
• Oleksandr Lutsii & Oleksandr Helevei, 2023, "Formation of Components of the Marketing Information System for Agricultural Products Using Big Data Methods," Oblik i finansi, Institute of Accounting and Finance, issue 3, pages 145-150, September, DOI: 10.33146/2307-9878-2023-3(101)-145-.
• Saurabh Ghosh & Abhishek Ranjan, 2023, "A Machine Learning Approach to GDP Nowcasting: An Emerging Market Experience," Bulletin of Monetary Economics and Banking, Bank Indonesia, volume 26, issue Special I, pages 33-54, February, DOI: https://doi.org/10.59091/1410-8046..
• Marcus Buckmann & Andreas Joseph, 2023, "An Interpretable Machine Learning Workflow with an Application to Economic Forecasting," International Journal of Central Banking, International Journal of Central Banking, volume 19, issue 4, pages 449-522, October.
• Naudé, Wim, 2023, "Artificial Intelligence and the Economics of Decision-Making," IZA Discussion Papers, IZA Network @ LISER, number 16000, Mar.
• Osman Semi Ceylan, 2023, "Implementation of Quantum Weyl Transformations on Cirq," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 1-5, October, DOI: doi.org/10.5281/zenodo.10102956.
• Sevdanur GENÇ, 2023, "The Impact of Quantum Technology on the Metaverse: Future Possibilities and Challenges," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 17-27, October, DOI: doi.org/10.5281/zenodo.10102956.
• Ercan ÇaÄŸlar, 2023, "Introduction to Quantum Data Science: Grover Search Algorithm," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 29-34, October, DOI: doi.org/10.5281/zenodo.10102956.
• Bayram Köse, 2023, "Data, Informatics, Artificial Intelligence and Optimization," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 35-40, October, DOI: doi.org/10.5281/zenodo.10102956.
• Sinem Kalkan, 2023, "Magnetized Strange Quark Matter in Lyra Theory," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 41-44, October, DOI: doi.org/10.5281/zenodo.10102956.
• Hüseyin Türker, 2023, "Exploring Quantum Annealing: A Pathway to Quantum Computing," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 45-54, October, DOI: doi.org/10.5281/zenodo.10102956.
• Dila BaÅŸpınar & Ela BAÅžPINAR & Umut AldoÄŸan, 2023, "Derivative and partial integral methods and Gauss integral's indefinite integral solution and its use in wave function in quantum mechanics and exact solutions of the wave function depending on position and time," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 55-63, October, DOI: doi.org/10.5281/zenodo.10102956.
• Davut Emre Tasar & Kutan Koruyan & Ceren Öcal CoÅŸar, 2023, "Machine Learning in the Quantum Age: Quantum vs. Classical Support Vector Machines," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 65-72, October, DOI: doi.org/10.5281/zenodo.10102956.
• Sevdanur GENÇ, 2023, "Hamiltonian Analysis of Amino Acid Sequences by Quantum Computing," Journal of Quantum Technologies and Informatics Research, Holistence Publications, volume 1, issue 1, pages 7-16, October, DOI: doi.org/10.5281/zenodo.10102956.
• Paolo Massimo Buscema & Francesca Della Torre & Giulia Massini & Guido Ferilli & Pier Luigi Sacco, 2023, "Reconstructing the Emergent Organization of Information Flows in International Stock Markets: A Computational Complex Systems Approach," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 1, pages 49-89, June, DOI: 10.1007/s10614-022-10267-1.
• Ba Chu & Shafiullah Qureshi, 2023, "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 4, pages 1567-1609, December, DOI: 10.1007/s10614-022-10312-z.
• Hannes Wallimann & David Imhof & Martin Huber, 2023, "A Machine Learning Approach for Flagging Incomplete Bid-Rigging Cartels," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 4, pages 1669-1720, December, DOI: 10.1007/s10614-022-10315-w.
• Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2023, "A Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 4, pages 1801-1843, December, DOI: 10.1007/s10614-022-10320-z.
• Bhaskar Tripathi & Rakesh Kumar Sharma, 2023, "Modeling Bitcoin Prices using Signal Processing Methods, Bayesian Optimization, and Deep Neural Networks," Computational Economics, Springer;Society for Computational Economics, volume 62, issue 4, pages 1919-1945, December, DOI: 10.1007/s10614-022-10325-8.
• Indu Khurana & Daniel J. Lee, 2023, "Gender bias in high stakes pitching: an NLP approach," Small Business Economics, Springer, volume 60, issue 2, pages 485-502, February, DOI: 10.1007/s11187-021-00598-y.
• Botond Benedek & Balint Zsolt Nagy, 2023, "Traditional versus AI-Based Fraud Detection: Cost Efficiency in the Field of Automobile Insurance," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), volume 22, issue 2, pages 77-98.
• Jonathan Proctor & Tamma Carleton & Sandy Sum, 2023, "Parameter Recovery Using Remotely Sensed Variables," NBER Working Papers, National Bureau of Economic Research, Inc, number 30861, Jan.
• Bryan T. Kelly & Dacheng Xiu, 2023, "Financial Machine Learning," NBER Working Papers, National Bureau of Economic Research, Inc, number 31502, Jul.
• Michael G. Mueller-Smith & Benjamin Pyle & Caroline Walker, 2023, "Estimating the Impact of the Age of Criminal Majority: Decomposing Multiple Treatments in a Regression Discontinuity Framework," NBER Working Papers, National Bureau of Economic Research, Inc, number 31523, Aug.
• Turan G. Bali & Bryan T. Kelly & Mathis Mörke & Jamil Rahman, 2023, "Machine Forecast Disagreement," NBER Working Papers, National Bureau of Economic Research, Inc, number 31583, Aug.
• D. Babet & Q. Deltour & T. Faria & S. Himpens, 2023, "Les reseaux de neurones appliques a la statistique publique : methodes et cas d’usages," Documents de Travail de l'Insee - INSEE Working Papers, Institut National de la Statistique et des Etudes Economiques, number m2023-01.
• Leonidas Aristodemou & Fernando Galindo-Rueda & Kuniko Matsumoto & Akiyoshi Murakami, 2023, "Measuring governments’ R&D funding response to COVID-19: An application of the OECD Fundstat infrastructure to the analysis of R&D directionality," OECD Science, Technology and Industry Working Papers, OECD Publishing, number 2023/06, Oct, DOI: 10.1787/4889f5f2-en.
• Dillon Huddleston & Fred Liu & Lars Stentoft, 2023, "Intraday Market Predictability: A Machine Learning Approach," Journal of Financial Econometrics, Oxford University Press, volume 21, issue 2, pages 485-527.
• Jan-Peter Kucklick & Jennifer Priefer & Daniel Beverungen & Oliver Müller, 2023, "Elucidating the Predictive Power of Search and Experience Qualities for Pricing of Complex Goods – A Machine Learning-based Study on Real Estate Appraisal," Working Papers Dissertations, Paderborn University, Faculty of Business Administration and Economics, number 112, Jun.
• Benedek Nagy & Manuela Rozalia Gabor & Ioan Bogdan Baco? & Moaaz Kabil & Kai Zhu & Lóránt Dénes Dávid, 2023, "Google and apple mobility data as predictors for European tourism during the COVID-19 pandemic: A neural network approach," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, volume 18, issue 2, pages 419-459, June, DOI: 10.24136/eq.2023.013.
• Vancsura, László & Bareith, Tibor, 2023, "Analysis of the performance of predictive models during Covid-19 and the Russian-Ukrainian war," Public Finance Quarterly, Corvinus University of Budapest, volume 69, issue 2, pages 118-132, DOI: https://doi.org/10.35551/PFQ_2023_2.
• Gutierrez-Lythgoe, Antonio, 2023, "Movilidad urbana sostenible: Predicción de demanda con Inteligencia Artificial
  [Sustainable Urban Mobility: Demand Prediction with Artificial Intelligence]," MPRA Paper, University Library of Munich, Germany, number 117103, Apr.
• Gutierrez-Lythgoe, Antonio, 2023, "Autoempleo y Machine Learning: Una aplicación para España
  [Self-employment and Machine Learning: An application for Spain]," MPRA Paper, University Library of Munich, Germany, number 117275, May.
• Temel, Tugrul & Phumpiu, Paul, 2023, "Policy Design from a Network Perspective: Targeting a Sector, Cascade of Links, Network Resilience," MPRA Paper, University Library of Munich, Germany, number 118389, Aug.
• Temel, Tugrul & Phumpiu, Paul, 2023, "Policy Design from a Network Perspective: Targeting a Sector, Cascade of Links, Network Resilience," MPRA Paper, University Library of Munich, Germany, number 118466, Sep.
• Kitova, Olga & Dyakonova, Ludmila & Savinova, Victoria & Fomin, Kiril, 2023, "Forecasting the main economic indicators for industry in the analytical system "Horizon"," MPRA Paper, University Library of Munich, Germany, number 118887, Oct.
• Chen, Ying & Grith, Maria & Lai, Hannah L. H., 2023, "Neural Tangent Kernel in Implied Volatility Forecasting: A Nonlinear Functional Autoregression Approach," MPRA Paper, University Library of Munich, Germany, number 119022, Oct.
• Mestiri, Sami, 2023, "How to use machine learning in finance," MPRA Paper, University Library of Munich, Germany, number 120045, Oct.
• Pal, Hemendra, 2023, "The Impact of Russia-Ukraine conflict on Global Commodity Brent Crude Prices," MPRA Paper, University Library of Munich, Germany, number 124770, Aug, revised 02 Oct 2024.
• Jiří Witzany & Milan Fičura, 2023, "Machine Learning Applications to Valuation of Options on Non-liquid Markets," FFA Working Papers, Prague University of Economics and Business, number 5.001, Jan, revised 24 Jan 2023.
• Jiří Witzany & Milan Fičura, 2023, "A Comparison of Neural Networks and Bayesian MCMC for the Heston Model Estimation (Forget Statistics - Machine Learning is Sufficient!)," FFA Working Papers, Prague University of Economics and Business, number 5.007, Jul, revised 11 Jul 2023.
• Callan Windsor & Max Zang, 2023, "Firms' Price-setting Behaviour: Insights from Earnings Calls," RBA Research Discussion Papers, Reserve Bank of Australia, number rdp2023-06, Sep, DOI: 10.47688/rdp2023-06.
• Roy, Sumitra & Gupta, Vishnu & Ray, Samrat, 2023, "Adoption of AI chat bot like Chat GPT in higher education in India: a sem analysis approach," Economic environment, Russian Presidential Academy of National Economy and Public Administration, issue 4, pages 130-149, DOI: 10.36683/2306-1758/2023-4-46/130-14.
• Guodong Guo & Brad R. Humphreys & Qiangchang Wang & Yang Zhou, 2023, "Attractive or Aggressive? A Face Recognition and Machine Learning Approach for Estimating Returns to Visual Appearance," Journal of Sports Economics, , volume 24, issue 6, pages 737-758, August, DOI: 10.1177/15270025231160769.
• Abdullah Mohammad Ghazi Al khatib & Bayan Mohamad Alshaib & Ali Mohamad Kanaan, 2023, "The Interaction Between Financial Development and Economic Growth: A Novel Application of Transfer Entropy and Nonlinear Approach in Algeria," SAGE Open, , volume 13, issue 4, pages 21582440231, December, DOI: 10.1177/21582440231217871.
• Joao Vitor Matos Goncalves & Michel Alexandre & Gilberto Tadeu Lima, 2023, "ARIMA and LSTM: A Comparative Analysis of Financial Time Series Forecasting," Working Papers, Department of Economics, University of São Paulo (FEA-USP), number 2023_13, Nov.
• Gianluca Anese & Marco Corazza & Michele Costola & Loriana Pelizzon, 2023, "Impact of public news sentiment on stock market index return and volatility," Computational Management Science, Springer, volume 20, issue 1, pages 1-36, December, DOI: 10.1007/s10287-023-00454-2.
• Georgios Fatouros & Georgios Makridis & Dimitrios Kotios & John Soldatos & Michael Filippakis & Dimosthenis Kyriazis, 2023, "DeepVaR: a framework for portfolio risk assessment leveraging probabilistic deep neural networks," Digital Finance, Springer, volume 5, issue 1, pages 29-56, March, DOI: 10.1007/s42521-022-00050-0.
• Huei-Wen Teng & Yu-Hsien Li, 2023, "Can deep neural networks outperform Fama-MacBeth regression and other supervised learning approaches in stock returns prediction with asset-pricing factors?," Digital Finance, Springer, volume 5, issue 1, pages 149-182, March, DOI: 10.1007/s42521-023-00076-y.
• Ioannis Chalkiadakis & Gareth W. Peters & Matthew Ames, 2023, "Hybrid ARDL-MIDAS-Transformer time-series regressions for multi-topic crypto market sentiment driven by price and technology factors," Digital Finance, Springer, volume 5, issue 2, pages 295-365, June, DOI: 10.1007/s42521-023-00079-9.
• Tiago E. Pratas & Filipe R. Ramos & Lihki Rubio, 2023, "Forecasting bitcoin volatility: exploring the potential of deep learning," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, volume 13, issue 2, pages 285-305, June, DOI: 10.1007/s40822-023-00232-0.
• Francisco J. Delgado & Elena Fernández-Rodríguez & Roberto García-Fernández & Manuel Landajo & Antonio Martínez-Arias, 2023, "Tax avoidance and earnings management: a neural network approach for the largest European economies," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 9, issue 1, pages 1-25, December, DOI: 10.1186/s40854-022-00424-8.
• Esra Alp Coşkun & Hakan Kahyaoglu & Chi Keung Marco Lau, 2023, "Which return regime induces overconfidence behavior? Artificial intelligence and a nonlinear approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 9, issue 1, pages 1-34, December, DOI: 10.1186/s40854-022-00446-2.
• Ihab K. A. Hamdan & Wulamu Aziguli & Dezheng Zhang & Eli Sumarliah, 2023, "Machine learning in supply chain: prediction of real-time e-order arrivals using ANFIS," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, volume 14, issue 1, pages 549-568, March, DOI: 10.1007/s13198-022-01851-7.
• Tobias Götze & Marc Gürtler & Eileen Witowski, 2023, "Forecasting accuracy of machine learning and linear regression: evidence from the secondary CAT bond market," Journal of Business Economics, Springer, volume 93, issue 9, pages 1629-1660, November, DOI: 10.1007/s11573-023-01138-8.
• K. Kumaraswamy & N. Ch. Bhatracharyulu, 2023, "Statistical brand switching model: an Hidden Markov approach," OPSEARCH, Springer;Operational Research Society of India, volume 60, issue 2, pages 942-950, June, DOI: 10.1007/s12597-022-00613-0.
• Indra Budi & Yaniasih Yaniasih, 2023, "Understanding the meanings of citations using sentiment, role, and citation function classifications," Scientometrics, Springer;Akadémiai Kiadó, volume 128, issue 1, pages 735-759, January, DOI: 10.1007/s11192-022-04567-4.
• Mei-Mei Lin & Fu-Hsiang Kuo, 2023, "A Principal Component Analysis of Digital Banking Development in Taiwan," Journal of Applied Finance & Banking, SCIENPRESS Ltd, volume 13, issue 4, pages 1-2.
• Yelyzaveta Apanovych & Yelyzaveta Apanovych & Stanislav Prágr, 2023, "Determination of iron procurement strategy for manufacturing companies," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 11, issue 2, pages 331-348, December, DOI: 10.9770/jesi.2023.11.2(23).
• Caio Almeida & Jianqing Fan & Gustavo Freire & Francesca Tang, 2023, "Can a Machine Correct Option Pricing Models?," Journal of Business & Economic Statistics, Taylor & Francis Journals, volume 41, issue 3, pages 995-1009, July, DOI: 10.1080/07350015.2022.2099871.
• Santiago Picasso, 2023, "Crecimiento y convergencia: un análisis desde la teoría de grafos," Documentos de Trabajo (working papers), Instituto de Economía - IECON, number 23-15.
• Diana Barro & Luca Barzanti & Marco Corazza & Martina Nardon, 2023, "Machine Learning and Fundraising: Applications of Artificial Neural Networks," Working Papers, Department of Economics, University of Venice "Ca' Foscari", number 2023: 33.
• Adelaide Baronchelli & Roberto Ricciuti & Mattia Viale, 2023, "Elite persistence in medieval Venice after the Black Death," Working Papers, University of Verona, Department of Economics, number 01/2023, Jan.
• Sabek Amine, 2023, "Unveiling the diverse efficacy of artificial neural networks and logistic regression: A comparative analysis in predicting financial distress," Croatian Review of Economic, Business and Social Statistics, Sciendo, volume 9, issue 1, pages 16-32, July, DOI: 10.2478/crebss-2023-0002.
• Souto Hugo Gobato & Moradi Amir, 2023, "Forecasting realized volatility through financial turbulence and neural networks," Economics and Business Review, Sciendo, volume 9, issue 2, pages 133-159, April, DOI: 10.18559/ebr.2023.2.737.
• Kaczmarek Tomasz & Grobelny Przemysław, 2023, "How to fly to safety without overpaying for the ticket," Economics and Business Review, Sciendo, volume 9, issue 2, pages 160-183, April, DOI: 10.18559/ebr.2023.2.738.
• Węcel Krzysztof & Sawiński Marcin & Stróżyna Milena & Lewoniewski Włodzimierz & Księżniak Ewelina & Stolarski Piotr & Abramowicz Witold, 2023, "Artificial intelligence—friend or foe in fake news campaigns," Economics and Business Review, Sciendo, volume 9, issue 2, pages 41-70, April, DOI: 10.18559/ebr.2023.2.736.
• Manta Eduard Mihai & Bogoevici Flavia, 2023, "Clustering the AI Landscape: Navigating Global Insights from Leading AI Indexes," Journal of Social and Economic Statistics, Sciendo, volume 12, issue 2, pages 88-108, December, DOI: 10.2478/jses-2023-0011.
• Maudud Hassan Uzzal & Robert Ślepaczuk, 2023, "The performance of time series forecasting based on classical and machine learning methods for S&P 500 index," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-05.
• Karol Chojnacki & Robert Ślepaczuk, 2023, "This study compares well-known tools of technical analysis (Moving Average Crossover MAC) with Machine Learning based strategies (LSTM and XGBoost) and Ensembled Machine Learning Strategies (LSTM ensembled with XGBoost and MAC). All models were compa," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-15.
• Damian Ślusarczyk & Robert Ślepaczuk, 2023, "Optimal Markowitz Portfolio Using Returns Forecasted with Time Series and Machine Learning Models," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-17.
• Paweł Jakubowski & Robert Ślepaczuk & Franciszek Windorbski, 2023, "REnsembling ARIMAX Model in Algorithmic Investment Strategies on Commodities Market," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-20.
• Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023, "Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-23.
• Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023, "Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-25.
• Sahil Teymurzade & Robert Ślepaczuk, 2023, "Predicting DJIA, NASDAQ and NYSE index prices using ARIMA and VAR models," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-27.
• Jesús Fernández‐Villaverde & Samuel Hurtado & Galo Nuño, 2023, "Financial Frictions and the Wealth Distribution," Econometrica, Econometric Society, volume 91, issue 3, pages 869-901, May, DOI: 10.3982/ECTA18180.
• Mohamad Hassan Shahrour & Mostafa Dekmak, 2023, "Intelligent stock prediction: A neural network approach," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., volume 10, issue 01, pages 1-14, March, DOI: 10.1142/S2424786322500165.
• Simon, Frederik & Weibels, Sebastian & Zimmermann, Tom, 2025, "Deep parametric portfolio policies," CFR Working Papers, University of Cologne, Centre for Financial Research (CFR), number 23-01, revised 2025.
• Barkan, Oren & Benchimol, Jonathan & Caspi, Itamar & Cohen, Eliya & Hammer, Allon & Koenigstein, Noam, 2023, "Forecasting CPI inflation components with Hierarchical Recurrent Neural Networks," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, volume 39, issue 3, pages 1145-1162, DOI: 10.1016/j.ijforecast.2022.04.009.
• Stempel, Daniel & Zahner, Johannes, 2023, "Whose inflation rates matter most? A DSGE model and machine learning approach to monetary policy in the Euro area," IMFS Working Paper Series, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), number 188.
• Baumgärtner, Martin & Zahner, Johannes, 2023, "Whatever it takes to understand a central banker: Embedding their words using neural networks," IMFS Working Paper Series, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), number 194.
• Büchel, Jan & Engler, Jan & Mertens, Armin, 2023, "Gesuchte Datenkompetenzen in Deutschland
  [The demand for data skills in Germany]," IW-Trends – Vierteljahresschrift zur empirischen Wirtschaftsforschung, Institut der deutschen Wirtschaft (IW) / German Economic Institute, volume 50, issue 2, pages 3-17, DOI: 10.2373/1864-810X.23-02-01.
• Frank, Johannes, 2023, "Forecasting realized volatility in turbulent times using temporal fusion transformers," FAU Discussion Papers in Economics, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, number 03/2023.
• Singla, Shikhar, 2023, "Regulatory costs and market power," LawFin Working Paper Series, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin), number 47.
• Foltas, Alexander, 2023, "Quantifying priorities in business cycle reports: Analysis of recurring textual patterns around peaks and troughs," Working Papers, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin, number 44, DOI: 10.18452/27015.
• Stempel, Daniel & Zahner, Johannes, 2023, "Whose Inflation Rates Matter Most? A DSGE Model and Machine Learning Approach to Monetary Policy in the Euro Area," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage", Verein für Socialpolitik / German Economic Association, number 277627.
• Holtemöller, Oliver & Kozyrev, Boris, 2023, "Forecasting Economic Activity with a Neural Network in Uncertain Times: Monte Carlo Evidence and Application to German GDP," VfS Annual Conference 2023 (Regensburg): Growth and the "sociale Frage", Verein für Socialpolitik / German Economic Association, number 277688.

2022

• Mikkel Bennedsen & Eric Hillebrand & Sebastian Jensen, 2022, "A Neural Network Approach to the Environmental Kuznets Curve," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2022-09, May.
• Francesco Bianchi & Sydney C. Ludvigson & Sai Ma, 2022, "Belief Distortions and Macroeconomic Fluctuations," American Economic Review, American Economic Association, volume 112, issue 7, pages 2269-2315, July, DOI: 10.1257/aer.20201713.
• Michael Bailey & Drew Johnston & Theresa Kuchler & Johannes Stroebel & Arlene Wong, 2022, "Peer Effects in Product Adoption," American Economic Journal: Applied Economics, American Economic Association, volume 14, issue 3, pages 488-526, July, DOI: 10.1257/app.20200367.
• Ignacia Mercadal, 2022, "Dynamic Competition and Arbitrage in Electricity Markets: The Role of Financial Players," American Economic Journal: Microeconomics, American Economic Association, volume 14, issue 3, pages 665-699, August, DOI: 10.1257/mic.20190276.
• Arman Khachiyan & Anthony Thomas & Huye Zhou & Gordon Hanson & Alex Cloninger & Tajana Rosing & Amit K. Khandelwal, 2022, "Using Neural Networks to Predict Microspatial Economic Growth," American Economic Review: Insights, American Economic Association, volume 4, issue 4, pages 491-506, December, DOI: 10.1257/aeri.20210422.
• Rossella Calvi & Jacob Penglase & Denni Tommasi, 2022, "Measuring Women's Empowerment in Collective Households," AEA Papers and Proceedings, American Economic Association, volume 112, pages 556-560, May, DOI: 10.1257/pandp.20221054.
• Laurence Kotlikoff, 2022, "Does Prediction Machines Predict Our AI Future? A Review," Journal of Economic Literature, American Economic Association, volume 60, issue 3, pages 1052-1057, September, DOI: 10.1257/jel.20191519.
• Mariam Dundua & Otar Gorgodze, 2022, "Application of Artificial Intelligence for Monetary Policy-Making," NBG Working Papers, National Bank of Georgia, number 02/2022, Nov.
• ȘTefan Bolotä‚ & Mircea Asandului, 2022, "Using Machine Learning In Detecting Fake News," Review of Economic and Business Studies, Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, issue 30, pages 53-66, December, DOI: 10.47743/rebs-2022-2-0004.
• Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022, "“An application of deep learning for exchange rate forecasting”," AQR Working Papers, University of Barcelona, Regional Quantitative Analysis Group, number 202201, Jan, revised Jan 2022.
• Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022, "“Density forecasts of inflation using Gaussian process regression models”," AQR Working Papers, University of Barcelona, Regional Quantitative Analysis Group, number 202207, Jul, revised Jul 2022.
• Grzegorz Marcjasz & Micha{l} Narajewski & Rafa{l} Weron & Florian Ziel, 2022, "Distributional neural networks for electricity price forecasting," Papers, arXiv.org, number 2207.02832, Jul, revised Dec 2022.
• Ramis Khabibullin & Sergei Seleznev, 2022, "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Papers, arXiv.org, number 2210.07154, Oct.
• Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2022, "Bayesian Neural Networks for Macroeconomic Analysis," Papers, arXiv.org, number 2211.04752, Nov, revised Apr 2024.
• Anton A. Gerunov, 2022, "Performance of 109 Machine Learning Algorithms across Five Forecasting Tasks: Employee Behavior Modeling, Online Communication, House Pricing, IT Support and Demand Planning," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 15-43.
• Andrés Alonso-Robisco & José Manuel Carbó, 2022, "Inteligencia artificial y finanzas: una alianza estratégica," Occasional Papers, Banco de España, number 2222, Oct.
• José Manuel Carbó & Sergio Gorjón, 2022, "Application of machine learning models and interpretability techniques to identify the determinants of the price of bitcoin," Working Papers, Banco de España, number 2215, Apr.
• Carlos Moreno Pérez & Marco Minozzo, 2022, "Natural Language Processing and Financial Markets: Semi-supervised Modelling of Coronavirus and Economic News," Working Papers, Banco de España, number 2228, Aug.
• Carlos Moreno Pérez & Marco Minozzo, 2022, "Monetary Policy Uncertainty in Mexico: An Unsupervised Approach," Working Papers, Banco de España, number 2229, Aug.
• Carlos Moreno Pérez & Marco Minozzo, 2022, "“Making Text Talk”: The Minutes of the Central Bank of Brazil and the Real Economy," Working Papers, Banco de España, number 2240, Nov, DOI: https://doi.org/10.53479/23646.
• Valerio Astuti & Alessio Ciarlone & Alberto Coco, 2022, "The role of central bank communication in inflation-targeting Eastern European emerging economies," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1381, Oct.
• Torre Leonardo & González Eva & Casillas Ramón & Alvarado Jorge, 2022, "Sentiment Indexes and Economic Activity Indicators in Mexico 2016-2021," Working Papers, Banco de México, number 2022-18, Dec.
• Luis Gerardo Gage & Raúl Morales-Resendiz & John Arroyo & Jeniffer Rubio & Paolo Barucca, 2022, "Classifying payment patterns with artificial neural networks: an autoencoder approach," IFC Bulletins chapters, Bank for International Settlements, in: Bank for International Settlements, "Machine learning in central banking".
• Jošić Hrvoje & Žmuk Berislav, 2022, "A Machine Learning Approach to Forecast International Trade: The Case of Croatia," Business Systems Research, Sciendo, volume 13, issue 3, pages 144-160, October, DOI: 10.2478/bsrj-2022-0030.
• Ramis Khabibullin & Sergei Seleznev, 2022, "Fast Estimation of Bayesian State Space Models Using Amortized Simulation-Based Inference," Bank of Russia Working Paper Series, Bank of Russia, number wps104, Dec.
• Martin Huber & David Imhof & Rieko Ishii, 2022, "Transnational machine learning with screens for flagging bid‐rigging cartels," Journal of the Royal Statistical Society Series A, Royal Statistical Society, volume 185, issue 3, pages 1074-1114, July, DOI: 10.1111/rssa.12811.
• Marcus Buckmann & Andreas Joseph, 2022, "An interpretable machine learning workflow with an application to economic forecasting," Bank of England working papers, Bank of England, number 984, Jun.
• Elliott Ash & Ruben Durante & Maria Grebenshchikova & Carlo Schwarz, 2022, "Visual Representation and Stereotypes in News Media," CESifo Working Paper Series, CESifo, number 9686.
• David Anderson & Urban Ulrych, 2022, "Accelerated American Option Pricing with Deep Neural Networks," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 22-03, Jan.
• Dongshuai Zhao & Zhongli Wang & Florian Schweizer-Gamborino & Didier Sornette, 2022, "Polytope Fraud Theory," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 22-41, May.
• Oksana Bashchenko, 2022, "Bitcoin Price Factors: Natural Language Processing Approach," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 22-48, May.
• Fredy Cepeda-Lopez & Fredy Gamboa-Estrada & Carlos Leon-Rinc�n & Hern�n Rincon-Castro, 2022, "Colombian Liberalization and Integration into World Trade Markets: Much Ado about Nothing," Revista de Economía del Rosario, Universidad del Rosario, volume 25, issue 2, pages 1-44.
• Kase, Hanno & Melosi, Leonardo & Rottner, Matthias, 2022, "Estimating Nonlinear Heterogeneous Agents Models with Neural Networks," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 17391, Jun.
• Katumullage, Duwani & Yang, Chenyu & Barth, Jackson & Cao, Jing, 2022, "Using Neural Network Models for Wine Review Classification," Journal of Wine Economics, Cambridge University Press, volume 17, issue 1, pages 27-41, February.
• Yang, Chenyu & Barth, Jackson & Katumullage, Duwani & Cao, Jing, 2022, "Wine Review Descriptors as Quality Predictors: Evidence from Language Processing Techniques," Journal of Wine Economics, Cambridge University Press, volume 17, issue 1, pages 64-80, February.
• Festus Victor Bekun, 2022, "Mitigating Emissions in India: Accounting for the Role of Real Income, Renewable Energy Consumption and Investment in Energy," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 1, pages 188-192.
• Christian Manuel Moreno Rocha & Jose Ricardo Nunez Alvarez & Daniel A. Diaz Castillo & Esnaider D. Florian Domingue & Juan Camilo Barrera Hernandez, 2022, "Implementation of the Hierarchical Analytical Process in the Selection of the Best Source of Renewable Energy in the Colombian Caribbean Region," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 2, pages 111-119, March.
• Adrian-Nicolae Buturache & Stelian Stancu, 2022, "Building Energy Consumption Prediction Using Neural-Based Models," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 2, pages 30-38, March.
• Christian Manuel Moreno Rocha & Esnaider D. Florian Domingue & Daniel A. Diaz Castillo & Kevin Logreira Vargas & Andres Alfredo Medina Guzman, 2022, "Evaluation of Energy Alternatives through FAHP for the Energization of Colombian Insular Areas," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 4, pages 87-98, July.
• Christian Manuel Moreno Rocha & David Fernandez Perez & Jesus Rodriguez Retamoza & Jorge Silva Ortega & Denis Brieva Bohorquez & Luis Taborda Catalan, 2022, "Evaluation, Hierarchy and Selection of the best Source of Energy by using AHP, as a Proposed Solution to an Energy and Socio-economic Problem, in the case of Colombia s Pacific Zone," International Journal of Energy Economics and Policy, Econjournals, volume 12, issue 5, pages 409-419, September.
• Davidescu, Adriana AnaMaria & Petcu, Monica Aureliana & Curea, Stefania Cristina & Manta, Eduard Mihai, 2022, "Two faces of the same coin: Exploring the multilateral perspective of informality in relation to Sustainable Development Goals based on bibliometric analysis," Economic Analysis and Policy, Elsevier, volume 73, issue C, pages 683-705, DOI: 10.1016/j.eap.2021.12.016.
• Colak, Gonul & Fu, Mengchuan & Hasan, Iftekhar, 2022, "On modeling IPO failure risk," Economic Modelling, Elsevier, volume 109, issue C, DOI: 10.1016/j.econmod.2022.105790.
• Sadorsky, Perry, 2022, "Forecasting solar stock prices using tree-based machine learning classification: How important are silver prices?," The North American Journal of Economics and Finance, Elsevier, volume 61, issue C, DOI: 10.1016/j.najef.2022.101705.
• Silveira, Douglas & Vasconcelos, Silvinha & Resende, Marcelo & Cajueiro, Daniel O., 2022, "Won’t Get Fooled Again: A supervised machine learning approach for screening gasoline cartels," Energy Economics, Elsevier, volume 105, issue C, DOI: 10.1016/j.eneco.2021.105711.
• Lei, Heng & Xue, Minggao & Liu, Huiling, 2022, "Probability distribution forecasting of carbon allowance prices: A hybrid model considering multiple influencing factors," Energy Economics, Elsevier, volume 113, issue C, DOI: 10.1016/j.eneco.2022.106189.
• Herrera, Gabriel Paes & Constantino, Michel & Su, Jen-Je & Naranpanawa, Athula, 2022, "Renewable energy stocks forecast using Twitter investor sentiment and deep learning," Energy Economics, Elsevier, volume 114, issue C, DOI: 10.1016/j.eneco.2022.106285.
• Ghabri, Yosra & Ben Rhouma, Oussama & Gana, Marjène & Guesmi, Khaled & Benkraiem, Ramzi, 2022, "Information transmission among energy markets, cryptocurrencies, and stablecoins under pandemic conditions," International Review of Financial Analysis, Elsevier, volume 82, issue C, DOI: 10.1016/j.irfa.2022.102197.
• Alonso-Robisco, Andrés & Carbó, José Manuel, 2022, "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, volume 84, issue C, DOI: 10.1016/j.irfa.2022.102372.
• Kutuk, Yasin & Barokas, Lina, 2022, "Multivariate CDS risk premium prediction with SOTA RNNs on MI[N]T countries," Finance Research Letters, Elsevier, volume 45, issue C, DOI: 10.1016/j.frl.2021.102198.
• García-Céspedes, Rubén & Moreno, Manuel, 2022, "The generalized Vasicek credit risk model: A Machine Learning approach," Finance Research Letters, Elsevier, volume 47, issue PA, DOI: 10.1016/j.frl.2021.102669.
• Hanauer, Matthias X. & Kononova, Marina & Rapp, Marc Steffen, 2022, "Boosting agnostic fundamental analysis: Using machine learning to identify mispricing in European stock markets," Finance Research Letters, Elsevier, volume 48, issue C, DOI: 10.1016/j.frl.2022.102856.
• Barua, Ronil & Sharma, Anil K., 2022, "Dynamic Black Litterman portfolios with views derived via CNN-BiLSTM predictions," Finance Research Letters, Elsevier, volume 49, issue C, DOI: 10.1016/j.frl.2022.103111.
• Al-Mudafer, Muhammed Taher & Avanzi, Benjamin & Taylor, Greg & Wong, Bernard, 2022, "Stochastic loss reserving with mixture density neural networks," Insurance: Mathematics and Economics, Elsevier, volume 105, issue C, pages 144-174, DOI: 10.1016/j.insmatheco.2022.03.010.
• Xu, Shuzhe & Zhang, Chuanlong & Hong, Don, 2022, "BERT-based NLP techniques for classification and severity modeling in basic warranty data study," Insurance: Mathematics and Economics, Elsevier, volume 107, issue C, pages 57-67, DOI: 10.1016/j.insmatheco.2022.07.013.
• Caporin, Massimiliano & Costola, Michele & Garibal, Jean-Charles & Maillet, Bertrand, 2022, "Systemic risk and severe economic downturns: A targeted and sparse analysis," Journal of Banking & Finance, Elsevier, volume 134, issue C, DOI: 10.1016/j.jbankfin.2021.106339.