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

2022

• 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, Paradigm, 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 Staff Working Paper series, 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, Centre for Economic Policy Research, 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, International Journal of Energy Economics and Policy, 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, International Journal of Energy Economics and Policy, 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, International Journal of Energy Economics and Policy, 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, International Journal of Energy Economics and Policy, 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, International Journal of Energy Economics and Policy, 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.
• Plakandaras, Vasilios & Ji, Qiang, 2022, "Intrinsic decompositions in gold forecasting," Journal of Commodity Markets, Elsevier, volume 28, issue C, DOI: 10.1016/j.jcomm.2022.100245.
• Ghaemi Asl, Mahdi & Adekoya, Oluwasegun Babatunde & Rashidi, Muhammad Mahdi & Ghasemi Doudkanlou, Mohammad & Dolatabadi, Ali, 2022, "Forecast of Bayesian-based dynamic connectedness between oil market and Islamic stock indices of Islamic oil-exporting countries: Application of the cascade-forward backpropagation network," Resources Policy, Elsevier, volume 77, issue C, DOI: 10.1016/j.resourpol.2022.102778.
• Yao, Haixiang & Xia, Shenghao & Liu, Hao, 2022, "Six-factor asset pricing and portfolio investment via deep learning: Evidence from Chinese stock market," Pacific-Basin Finance Journal, Elsevier, volume 76, issue C, DOI: 10.1016/j.pacfin.2022.101886.
• Ouyang, Zisheng & Chen, Shili & Lai, Yongzeng & Yang, Xite, 2022, "The correlations among COVID-19, the effect of public opinion, and the systemic risks of China’s financial industries," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 600, issue C, DOI: 10.1016/j.physa.2022.127518.
• Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2022, "Urban economics in a historical perspective: Recovering data with machine learning," Regional Science and Urban Economics, Elsevier, volume 94, issue C, DOI: 10.1016/j.regsciurbeco.2021.103711.
• Kaczmarek, Tomasz & Będowska-Sójka, Barbara & Grobelny, Przemysław & Perez, Katarzyna, 2022, "False Safe Haven Assets: Evidence From the Target Volatility Strategy Based on Recurrent Neural Network," Research in International Business and Finance, Elsevier, volume 60, issue C, DOI: 10.1016/j.ribaf.2021.101610.
• Kristóf, Tamás & Virág, Miklós, 2022, "EU-27 bank failure prediction with C5.0 decision trees and deep learning neural networks," Research in International Business and Finance, Elsevier, volume 61, issue C, DOI: 10.1016/j.ribaf.2022.101644.
• Bernt P. Stigum, 2022, "Consumer Choice under Certainty and Uncertainty in Applied Econometrics," Journal of Economics and Econometrics, Economics and Econometrics Society, volume 65, issue 1, pages 1-27.
• Adriana Anamaria Davidescu & Răzvan Gabriel Hapau & Eduard Mihai Manta, 2022, "Impact of Crises on Capital Market Volatility: A Bibliometric Analysis," Contemporary Studies in Economic and Financial Analysis, Emerald Group Publishing Limited, "The New Digital Era: Other Emerging Risks and Opportunities", DOI: 10.1108/S1569-37592022000109B003.
• Lukas Janasek, 2022, "Acquisition of Costly Information in Data-Driven Decision Making," Working Papers IES, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, number 2022/10, May, revised May 2022.
• Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2022, "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper, Federal Reserve Bank of Atlanta, number 2022-16, Nov, DOI: 10.29338/wp2022-16.
• Nelson P. Rayl & Nitish R. Sinha, 2022, "Integrating Prediction and Attribution to Classify News," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2022-042, Jul, DOI: 10.17016/FEDS.2022.042.
• Aslı Seda KURT, 2022, "Forecasting Turkey’s Exports and Imports via Artificial Neural Network," Fiscaoeconomia, Tubitak Ulakbim JournalPark (Dergipark), issue 2, DOI: 10.25295/fsecon.1077683.
• Seyyide DOÄžAN & Hasan TÃœRE, 2022, "Country Risk Prediction with Machine Learning Techniques," Fiscaoeconomia, Tubitak Ulakbim JournalPark (Dergipark), issue 3, DOI: 10.25295/fsecon.1098493.
• Hector F. Calvo-Pardo & Tullio Mancini & Jose Olmo, 2022, "Machine Learning the Carbon Footprint of Bitcoin Mining," JRFM, MDPI, volume 15, issue 2, pages 1-30, February.
• Susan Ariel Aaronson, 2022, "Wicked Problems Might Inspire Greater Data Sharing," Working Papers, The George Washington University, Institute for International Economic Policy, number 2022-09, Sep.
• Mohamad Hassan Shahrour & Mostafa Dekmak, 2022, "Intelligent Stock Prediction: A Neural Network Approach," Post-Print, HAL, number hal-03884171, Jun, DOI: 10.1142/S2424786322500165.
• Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022, "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Post-Print, HAL, number halshs-03673240, May, DOI: 10.1016/j.regsciurbeco.2021.103711.
• Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022, "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," PSE-Ecole d'économie de Paris (Postprint), HAL, number halshs-03673240, May, DOI: 10.1016/j.regsciurbeco.2021.103711.
• Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2022, "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," Sciences Po Economics Publications (main), HAL, number halshs-03673240, May, DOI: 10.1016/j.regsciurbeco.2021.103711.
• Südbeck, Insa & Mindlina, Julia & Schnabel, André & Helber, Stefan, 2022, "Using Recurrent Neural Networks for the Performance Analysis and Optimization of Stochastic Milkrun-Supplied Flow Lines," Hannover Economic Papers (HEP), Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, number dp-703, Nov.
• Tea Baldigara, 2022, "Primjena Umjetnih Neuronskih Mreža U Modeliranju Domaće Turističke Potražnje," Ekonomski pregled, Hrvatsko društvo ekonomista (Croatian Society of Economists), volume 73, issue 3, pages 349-370, DOI: 10.32910/ep.73.3.1.
• Mühlbauer, Sabrina & Weber, Enzo, 2022, "Machine Learning for Labour Market Matching," IAB-Discussion Paper, Institut für Arbeitsmarkt- und Berufsforschung (IAB), Nürnberg [Institute for Employment Research, Nuremberg, Germany], number 202203, Feb, DOI: 10.48720/IAB.DP.2203.
• Montserrat Reyna Miranda & Ricardo Massa Roldán & Vicente Gómez Salcido, 2022, "Neuro-wavelet Model for price prediction in high-frequency data in the Mexican Stock market," Remef - Revista Mexicana de Economía y Finanzas Nueva Época REMEF (The Mexican Journal of Economics and Finance), Instituto Mexicano de Ejecutivos de Finanzas, IMEF, volume 17, issue 1, pages 1-23, Enero - M.
• Oscar Claveria & Enric Monte & Petar Soric & Salvador Torra, 2022, ""An application of deep learning for exchange rate forecasting"," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 202201, Jan, revised Jan 2022.
• Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022, ""Density forecasts of inflation using Gaussian process regression models"," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 202210, Jul, revised Jul 2022.
• Xenxo Vidal-Llana & Carlos Salort Sánchez & Vincenzo Coia & Montserrat Guillen, 2022, ""Non-Crossing Dual Neural Network: Joint Value at Risk and Conditional Tail Expectation estimations with non-crossing conditions"," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 202215, Oct, revised Oct 2022.
• Cochrane, William & Poot, Jacques & Roskruge, Matthew, 2022, "Urban Resilience and Social Security Uptake: New Zealand Evidence from the Global Financial Crisis and the COVID-19 Pandemic," IZA Discussion Papers, IZA Network @ LISER, number 15510, Aug.
• William Barnett & Kun He & Jingtong He, 2022, "Consumption Loan Augmented Divisia Monetary Index and China Monetary Aggregation," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202217, Nov.
• Giovanni Villani, 2022, "A Neural Network Approach to Value R&D Compound American Exchange Option," Computational Economics, Springer;Society for Computational Economics, volume 60, issue 1, pages 305-324, June, DOI: 10.1007/s10614-021-10150-5.
• Reza Bradrania & Davood Pirayesh Neghab & Mojtaba Shafizadeh, 2022, "State-dependent stock selection in index tracking: a machine learning approach," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, volume 36, issue 1, pages 1-28, March, DOI: 10.1007/s11408-021-00391-7.
• Alexander V. Giczy & Nicholas A. Pairolero & Andrew A. Toole, 2022, "Identifying artificial intelligence (AI) invention: a novel AI patent dataset," The Journal of Technology Transfer, Springer, volume 47, issue 2, pages 476-505, April, DOI: 10.1007/s10961-021-09900-2.
• Paolo Savona, 2022, "Prospects for Reforming the Money and Financial System," Open Economies Review, Springer, volume 33, issue 1, pages 187-195, February, DOI: 10.1007/s11079-021-09628-4.
• Chris Charalambous & Spiros H. Martzoukos & Zenon Taoushianis, 2022, "Estimating corporate bankruptcy forecasting models by maximizing discriminatory power," Review of Quantitative Finance and Accounting, Springer, volume 58, issue 1, pages 297-328, January, DOI: 10.1007/s11156-021-00995-0.
• Miriam Sosa & Christian Bucio & Edgar Ortiz Calisto, 2022, "Dynamic Stock Dependence and Monetary Variables in the United States (2000-2016) - A Copula and Neural Network Approach," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 96, pages 201-234, January-J, DOI: 10.17533/udea.le.n96a345321.
• Daniel Stempel & Johannes Zahner, 2022, "DSGE Models and Machine Learning: An Application to Monetary Policy in the Euro Area," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202232.
• Bryan T. Kelly & Semyon Malamud & Kangying Zhou, 2022, "The Virtue of Complexity in Return Prediction," NBER Working Papers, National Bureau of Economic Research, Inc, number 30217, Jul.
• Yacine Aït-Sahalia & Jianqing Fan & Lirong Xue & Yifeng Zhou, 2022, "How and When are High-Frequency Stock Returns Predictable?," NBER Working Papers, National Bureau of Economic Research, Inc, number 30366, Aug.
• Anton A. Gerunov, 2022, "Forecasting Customer Support Resolution Times Through Automated Machine Learning," Economics and Management, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, volume 19, issue 2, pages 1-11, DOI: 10.37708/em.swu.v19i2.1.
• Dimitris Korobilis & Kenichi Shimizu, 2022, "Bayesian Approaches to Shrinkage and Sparse Estimation," Foundations and Trends(R) in Econometrics, now publishers, volume 11, issue 4, pages 230-354, June, DOI: 10.1561/0800000041.
• Rodríguez Guevara, David Esteban & Rendón García, Juan Fernando & Trespalacios Carrasquilla, Alfredo & Jiménez Echeverri, Edwin Andrés, 2022, "Modelación de riesgo de crédito de personas naturales. Un caso aplicado a una caja de compensación familiar colombiana
  [Natural People Credit Risk Modeling. An applied case in a Colombian Family Benefit Fund]," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, volume 33, issue 1, pages 29-48, June, DOI: https://doi.org/10.46661/revmetodos.
• Díez, José Ignacio & Giannasi, Nadia & Scudelati, Mariela, 2022, "Redes, conocimientos y capacidades en el contexto de la industria del software. Análisis del caso Bahía Blanca
  [Network, knowledge and capabilities in the context of the software industry. Analysis of Bahía Blanca case]," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, volume 34, issue 1, pages 137-154, December, DOI: https://doi.org/10.46661/revmetodos.
• Beata Gavurova & Sylvia Jencova & Radovan Bacik & Marta Miskufova & Stanislav Letkovsky, 2022, "Artificial intelligence in predicting the bankruptcy of non-financial corporations," Oeconomia Copernicana, Institute of Economic Research, volume 13, issue 4, pages 1215-1251, December, DOI: 10.24136/oc.2022.035.
• Prisznyák, Alexandra, 2022, "Bankrobotics: Artificial Intelligence and Machine Learning Powered Banking Risk Management — Prevention of Money Laundering and Terrorist Financing," Public Finance Quarterly, Corvinus University of Budapest, volume 67, issue 2, pages 288-303, DOI: https://doi.org/10.35551/PFQ_2022_2.
• Liu, Mengxiao & Wang, Luhang & Yi, Yimin, 2022, "Quality Innovation, Cost Innovation, Export, and Firm Productivity Evolution: Evidence from the Chinese Electronics Industry," MPRA Paper, University Library of Munich, Germany, number 113270, Jul.
• Yang, Bill Huajian, 2022, "Modeling Path-Dependent State Transition by a Recurrent Neural Network," MPRA Paper, University Library of Munich, Germany, number 114188, Aug, revised 18 Jul 2022.
• Caio Almeida & Jianqing Fan & Gustavo Freire & Francesca Tang, 2022, "Can a Machine Correct Option Pricing Models?," Working Papers, Princeton University. Economics Department., number 2022-9, Jul.
• Damian Przekop, 2022, "Artificial Neural Networks vs Spatial Regression Approach in Property Valuation," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, volume 14, issue 2, pages 199-223, June.
• Aike Steentoft, Bu-Sung Lee, Markus Schl pfer, 2022, "Interpretable Prediction of Urban Mobility Flows with Deep Neural Networks as Gaussian Processes," Diskussionsschriften, Universitaet Bern, Departement Volkswirtschaft - CRED, number credresearchpaper36, May.
• Radu LUPU & Iulia LUPU & Tanase STAMULE & Mihai ROMAN, 2022, "Entropy as Leading Indicator for Extreme Systemic Risk Events," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 4, pages 58-73, December.
• Riccardo Di Francesco, 2022, "Aggregation Trees," CEIS Research Paper, Tor Vergata University, CEIS, number 546, Dec, revised 20 Nov 2023.
• Soham Banerjee & Diganta Mukherjee, 2022, "Short Term Stock Price Prediction in Indian Market: A Neural Network Perspective," Studies in Microeconomics, , volume 10, issue 1, pages 23-49, June, DOI: 10.1177/2321022220980537.
• Elda Xhumari & Suela Maxhelaku & Endrit Xhina, 2022, "A review of Knowledge Graph and Graph Neural Network application," Proceedings of Economics and Finance Conferences, International Institute of Social and Economic Sciences, number 13615626, Jul.
• Suela Maxhelaku & Elda Xhumari & Endrit Xhina, 2022, "Exploiting Knowledge Graphs in Healthcare," Proceedings of Economics and Finance Conferences, International Institute of Social and Economic Sciences, number 13615633, Jul.
• Vito Polito & Yunyi Zhang, 2022, "Tackling Large Outliers in Macroeconomic Data with Vector Artificial Neural Network Autoregression," Working Papers, The University of Sheffield, Department of Economics, number 2022004, Mar.
• Luca Grilli & Domenico Santoro, 2022, "Forecasting financial time series with Boltzmann entropy through neural networks," Computational Management Science, Springer, volume 19, issue 4, pages 665-681, October, DOI: 10.1007/s10287-022-00430-2.
• Charl Maree & Christian W. Omlin, 2022, "Reinforcement learning with intrinsic affinity for personalized prosperity management," Digital Finance, Springer, volume 4, issue 2, pages 241-262, September, DOI: 10.1007/s42521-022-00068-4.
• Sylwia Sysko-Romańczuk & Piotr Zaborek & Anna Wróblewska & Jacek Dąbrowski & Sergiy Tkachuk, 2022, "Data modalities, consumer attributes and recommendation performance in the fashion industry," Electronic Markets, Springer;IIM University of St. Gallen, volume 32, issue 3, pages 1279-1292, September, DOI: 10.1007/s12525-022-00579-3.
• Raisul Islam & Vladimir Volkov, 2022, "Contagion or interdependence? Comparing spillover indices," Empirical Economics, Springer, volume 63, issue 3, pages 1403-1455, September, DOI: 10.1007/s00181-021-02169-2.
• Simon Blöthner & Mario Larch, 2022, "Economic determinants of regional trade agreements revisited using machine learning," Empirical Economics, Springer, volume 63, issue 4, pages 1771-1807, October, DOI: 10.1007/s00181-022-02203-x.
• Adem Baltaci & Raif Cergibozan & Ali Ari, 2022, "Cultural values and the global financial crisis: a missing link?," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, volume 12, issue 3, pages 507-529, September, DOI: 10.1007/s40822-022-00208-6.
• Andreas Falke & Harald Hruschka, 2022, "Analyzing browsing across websites by machine learning methods," Journal of Business Economics, Springer, volume 92, issue 5, pages 829-852, July, DOI: 10.1007/s11573-021-01067-4.
• Cuiyuan Wang & Tao Wang & Changhe Yuan & Jane Yihua Rong, 2022, "Learning to trade on sentiment," Journal of Economics and Finance, Springer;Academy of Economics and Finance, volume 46, issue 2, pages 308-323, April, DOI: 10.1007/s12197-021-09565-5.
• Luis Gerardo Hernández García, 2022, "Transport equipment network analysis: the value-added contribution," Journal of Economic Structures, Springer;Pan-Pacific Association of Input-Output Studies (PAPAIOS), volume 11, issue 1, pages 1-25, December, DOI: 10.1186/s40008-022-00289-1.
• Yu-Min Lian & Jia-Ling Chen & Hsueh-Chien Cheng, 2022, "Predicting Bitcoin Prices via Machine Learning and Time Series Models," Journal of Applied Finance & Banking, SCIENPRESS Ltd, volume 12, issue 5, pages 1-2.
• Simona Hašková & Petr Šuleř & Tomáš Krulický, 2022, "Advantages of fuzzy approach compared to probabilistic approach in project evaluation," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 10, issue 1, pages 483-493, September, DOI: 10.9770/jesi.2022.10.1(27).
• Jaromír Vrbka & Jakub Horák & Tomáš Krulický, 2022, "The influence of world oil prices on the Chinese Yuan exchange rate," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 9, issue 4, pages 439-462, June, DOI: 10.9770/jesi2022.9.4(24).
• Antonio Rodríguez Andrés & Abraham Otero & Voxi Heinrich Amavilah, 2022, "Knowledge economy classification in African countries: A model-based clustering approach," Information Technology for Development, Taylor & Francis Journals, volume 28, issue 2, pages 372-396, April, DOI: 10.1080/02681102.2021.1950597.
• Matthias Lalisse, 2022, "Measuring the Impact of Campaign Finance on Congressional Voting: A Machine Learning Approach," Working Papers Series, Institute for New Economic Thinking, number inetwp178, Feb, DOI: 10.36687/inetwp178.
• Yuriy Kleban & Tetiana Stasiuk, 2022, "Crypto Currency Price Forecast: Neural Network Perspectives," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 254, pages 29-42, DOI: 10.26531/vnbu2022.254.03.
• Lee Changro, 2022, "Training and Interpreting Machine Learning Models: Application in Property Tax Assessment," Real Estate Management and Valuation, Paradigm, volume 30, issue 1, pages 13-22, March, DOI: 10.2478/remav-2022-0002.
• Sulejmani Artan & Tevdovski Dragan, 2022, "How the Contagion is Transmitted to the Macedonian Stock Market? an Analysis of Co-Exceedances," South East European Journal of Economics and Business, Paradigm, volume 17, issue 1, pages 1-13, June, DOI: 10.2478/jeb-2022-0001.
• Baiquan Ma & Robert Ślepaczuk, 2022, "The profitability of pairs trading strategies on Hong-Kong stock market: distance, cointegration, and correlation methods," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2022-02.
• Maciej Wysocki & Paweł Sakowski, 2022, "Investment Portfolio Optimization Based on Modern Portfolio Theory and Deep Learning Models," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2022-12.
• Illia Baranochnikov & Robert Ślepaczuk, 2022, "A comparison of LSTM and GRU architectures with novel walk-forward approach to algorithmic investment strategy," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2022-21.
• Katarzyna Kryńska & Robert Ślepaczuk, 2022, "Daily and intraday application of various architectures of the LSTM model in algorithmic investment strategies on Bitcoin and the S&P 500 Index," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2022-25.
• Thi Thu Giang Nguyen & Robert Ślepaczuk, 2022, "The efficiency of various types of input layers of LSTM model in investment strategies on S&P500 index," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2022-29.
• Mohammad Zoynul Abedin & M. Kabir Hassan & Imran Khan & Ivan F. Julio, 2022, "Feature Transformation for Corporate Tax Default Prediction: Application of Machine Learning Approaches," Asia-Pacific Journal of Operational Research (APJOR), World Scientific Publishing Co. Pte. Ltd., volume 39, issue 04, pages 1-26, August, DOI: 10.1142/S0217595921400170.
• Tayyab Raza Fraz & Samreen Fatima, 2022, "Modeling And Forecasting Volatility Of Stock Market Using Family Of Garch Models: Evidence From Cpec Linked Countries," Global Economy Journal (GEJ), World Scientific Publishing Co. Pte. Ltd., volume 22, issue 01, pages 1-15, March, DOI: 10.1142/S219456592250004X.
• Tingbin Bian & Jin Chen & Qu Feng & Jingyi Li, 2022, "Comparing Econometric Analyses With Machine Learning Approaches: A Study On Singapore Private Property Market," The Singapore Economic Review (SER), World Scientific Publishing Co. Pte. Ltd., volume 67, issue 06, pages 1787-1810, December, DOI: 10.1142/S0217590820500538.
• Molina, José Alberto & Iñíguez, David & Ruiz, Gonzalo & Tarancón, Alfonso, 2022, "Networks in Population Economics: production and collaborations," GLO Discussion Paper Series, Global Labor Organization (GLO), number 1051.
• Hoang, Daniel & Wiegratz, Kevin, 2022, "Machine learning methods in finance: Recent applications and prospects," Working Paper Series in Economics, Karlsruhe Institute of Technology (KIT), Department of Economics and Management, number 158.
• Zahner, Johannes & Baumgärtner, Martin, 2022, "Whatever it Takes to Understand a Central Banker – Embedding their Words Using Neural Networks," VfS Annual Conference 2022 (Basel): Big Data in Economics, Verein für Socialpolitik / German Economic Association, number 264019.

2021

• Daniel Borup & David E. Rapach & Erik Christian Montes Schütte, 2021, "Now- and Backcasting Initial Claims with High-Dimensional Daily Internet Search-Volume Data," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2021-02, Jan.
• Metin Aktas & Osman Taylan, 2021, "Determinants of Islamic Banks’ Profitability Using Panel Data Analysis and ANFIS Approaches in Saudi Arabia محددات ربحية المصارف الإسلامية باستخدام تحليل البيانات المَقْطعية وأساليب الاستدلال التَّكَيُّفي في المملكة العربية السعودية," Journal of King Abdulaziz University: Islamic Economics, King Abdulaziz University, Islamic Economics Institute., volume 34, issue 2, pages 19-40, July, DOI: 10.4197/Islec.34-2.2.
• Barış Aksoy, 2021, "Predicting Financial Statement Frauds Using Machine Learning Methods and Logistic Regression: The Case of Borsa Istanbul," Journal of Finance Letters (Maliye ve Finans Yazıları), Maliye ve Finans Yazıları Yayıncılık Ltd. Şti., volume 36, issue 115, pages 27-58, April, DOI: https://doi.org/10.33203/mfy.733855.
• Francesco Decarolis & Gabriele Rovigatti, 2021, "From Mad Men to Maths Men: Concentration and Buyer Power in Online Advertising," American Economic Review, American Economic Association, volume 111, issue 10, pages 3299-3327, October, DOI: 10.1257/aer.20190811.
• Christopher R. Knittel & Samuel Stolper, 2021, "Machine Learning about Treatment Effect Heterogeneity: The Case of Household Energy Use," AEA Papers and Proceedings, American Economic Association, volume 111, pages 440-444, May, DOI: 10.1257/pandp.20211090.
• Tetsuya Kaji & Elena Manresa & Guillaume A. Pouliot, 2021, "Adversarial Inference Is Efficient," AEA Papers and Proceedings, American Economic Association, volume 111, pages 621-625, May, DOI: 10.1257/pandp.20211037.
• Ramos & Pablo Negri & Martín Breitkopf & María Laura Ojeda, 2021, "From International to Regional Commodity Price Pass-through Using Self-Driven Recurrent Networks," Asociación Argentina de Economía Política: Working Papers, Asociación Argentina de Economía Política, number 4513, Nov.
• Adrian-Nicolae Buturache & Stelian Stancu, 2021, "Usage of Neural-Based Predictive Modeling and IIoT in Wind Energy Applications," The AMFITEATRU ECONOMIC journal, Academy of Economic Studies - Bucharest, Romania, volume 23, issue 57, pages 412-412.
• Kin G. Olivares & Cristian Challu & Grzegorz Marcjasz & Rafal Weron & Artur Dubrawski, 2021, "Neural basis expansion analysis with exogenous variables: Forecasting electricity prices with NBEATSx," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/21/07, Apr.
• Jesus Lago & Grzegorz Marcjasz & Bart De Schutter & Rafal Weron, 2021, "Erratum to 'Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark' [Appl. Energy 293 (2021) 116983]," WORking papers in Management Science (WORMS), Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology, number WORMS/21/12, Jul.
• Efe Arda & Güray Küçükkocaoğlu, 2021, "Yapay Zeka Yöntemleri İle Hisse Senedi Fiyat Öngörüleri," Journal of Research in Economics, Politics & Finance, Ersan ERSOY, volume 6, issue 2, pages 565-586, DOI: 10.30784/epfad.878664.
• Denuit, Michel & Charpentier , Arthur & Trufin, Julien, 2021, "Autocalibration and Tweedie-dominance for insurance pricing with machine learning," LIDAM Reprints ISBA, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), number 2021049, Nov, DOI: https://doi.org/10.1016/j.insmathec.
• Lachlan O'Neill & Simon D Angus & Satya Borgohain & Nader Chmait & David Dowe, 2021, "Creating Powerful and Interpretable Models with Regression Networks," SoDa Laboratories Working Paper Series, Monash University, number 2021-09, Sep.
• Lachlan O'Neill & Nandini Anantharama & Wray Buntine & Simon D Angus, 2021, "Quantitative Discourse Analysis at Scale - AI, NLP and the Transformer Revolution," SoDa Laboratories Working Paper Series, Monash University, number 2021-12, Dec.
• Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021, "Deep Structural Estimation: With an Application to Option Pricing," Papers, arXiv.org, number 2102.09209, Feb.
• Daniel Boller & Michael Lechner & Gabriel Okasa, 2021, "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Papers, arXiv.org, number 2104.04601, Apr.
• Thomas Conlon & John Cotter & Iason Kynigakis, 2021, "Machine Learning and Factor-Based Portfolio Optimization," Papers, arXiv.org, number 2107.13866, Jul.
• Dimitris Korobilis & Kenichi Shimizu, 2021, "Bayesian Approaches to Shrinkage and Sparse Estimation," Papers, arXiv.org, number 2112.11751, Dec.
• Sabyasachi Kar & Amaani Bashir & Mayank Jain, 2021, "New Approaches to Forecasting Growth and Inflation: Big Data and Machine Learning," IEG Working Papers, Institute of Economic Growth, number 446, Oct.
• Gavrilenko N. G., 2021, "The use of artificial neural networks when planning the target indicators for the truck haulage development in the Russian Federation," Russian Journal of Social Sciences and Humanities, Omsk Humanitarian Academy, volume 15, issue 2, pages 213-218, June, DOI: 10.17238/issn1998-5320.2021.15.2.26.
• Andrés Alonso & José Manuel Carbó, 2021, "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers, Banco de España, number 2105, Jan.
• Mathilde Gerardin & Martial Ranvier, 2021, "Enrichment of the Banque de France s monthly business survey: lessons from textual analysis of business leaders comments," Working papers, Banque de France, number 821.
• Andre Groeger & Jonathan Hersh & Andrea Matranga & Hannes Mueller & Joan Serrat, 2021, "Monitoring War Destruction from Space Using Machine Learning," Working Papers, Barcelona School of Economics, number 1257, May.
• Marlene Amstad & Leonardo Gambacorta & Chao He & Dora Xia, 2021, "Trade sentiment and the stock market: new evidence based on big data textual analysis of Chinese media," BIS Working Papers, Bank for International Settlements, number 917, Jan.
• Denis Shibitov & Mariam Mamedli, 2021, "Forecasting Russian Cpi With Data Vintages And Machine Learning Techniques," Bank of Russia Working Paper Series, Bank of Russia, number wps70, Apr.
• Andreas Joseph & Eleni Kalamara & George Kapetanios & Galina Potjagailo & Chiranjit Chakraborty, 2021, "Forecasting UK inflation bottom up," Bank of England Staff Working Paper series, Bank of England, number 915, Mar.
• Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021, "Comparing minds and machines: implications for financial stability," Bank of England Staff Working Paper series, Bank of England, number 937, Aug.
• Oren Barkan & Jonathan Benchimol & Itamar Caspi & Allon Hammer & Noam Koenigstein, 2021, "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Bank of Israel Working Papers, Bank of Israel, number 2021.06, Mar.
• Tripathi Manas & Kumar Saurabh & Inani Sarveshwar Kumar, 2021, "Exchange Rate Forecasting Using Ensemble Modeling for Better Policy Implications," Journal of Time Series Econometrics, De Gruyter, volume 13, issue 1, pages 43-71, January, DOI: 10.1515/jtse-2020-0013.
• Donfack Morvan Nongni & Dufays Arnaud, 2021, "Modeling time-varying parameters using artificial neural networks: a GARCH illustration," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, volume 25, issue 5, pages 311-343, December, DOI: 10.1515/snde-2019-0091.
• Ba Chu & Shafiullah Qureshi, 2021, "Comparing Out-of-Sample Performance of Machine Learning Methods to Forecast U.S. GDP Growth," Carleton Economic Papers, Carleton University, Department of Economics, number 21-12, Oct.
• Nenad Milojević & Srdjan Redzepagic, 2021, "Prospects of Artificial Intelligence and Machine Learning Application in Banking Risk Management," Journal of Central Banking Theory and Practice, Central bank of Montenegro, volume 10, issue 3, pages 41-57.
• Douglas Silveira & Silvinha Vasconcelos & Marcelo Resende & Daniel O. Cajueiro, 2021, "Won't Get Fooled Again: A Supervised Machine Learning Approach for Screening Gasoline Cartels," CESifo Working Paper Series, CESifo, number 8835.
• Mahdi Ebrahimi Kahou & Jesús Fernández-Villaverde & Jesse Perla & Arnav Sood, 2021, "Exploiting Symmetry in High-Dimensional Dynamic Programming," CESifo Working Paper Series, CESifo, number 9161.
• Simon Blöthner & Mario Larch, 2021, "Economic Determinants of Regional Trade Agreements Revisited Using Machine Learning," CESifo Working Paper Series, CESifo, number 9233.
• Rui (Aruhan) Shi, 2021, "Learning from Zero: How to Make Consumption-Saving Decisions in a Stochastic Environment with an AI Algorithm," CESifo Working Paper Series, CESifo, number 9255.
• Vito Polito & Yunyi Zhang, 2021, "Tackling Large Outliers in Macroeconomic Data with Vector Artificial Neural Network Autoregression," CESifo Working Paper Series, CESifo, number 9395.
• Adam N. Smith & Stephan Seiler & Ishant Aggarwal, 2021, "Optimal Price Targeting," CESifo Working Paper Series, CESifo, number 9439.
• Alexis Marchal, 2021, "Risk & Returns around Fomc Press Conferences: A Novel Perspective from Computer Vision," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-18, Mar.
• Michael Mayer & Steven C. Bourassa & Martin Hoesli & Donato Scognamiglio, 2021, "Structured Additive Regression and Tree Boosting," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-83, Sep.
• Blanka Horvath & Josef Teichmann & Zan Zuric, 2021, "Deep Hedging under Rough Volatility," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-88, Feb.
• Alexei Kireyev & Andrei Leonidov, 2021, "Twin trade shocks: Spillovers from US-China trade tensions," International Economics, CEPII research center, issue 167, pages 174-188.
• C Castro-Iragorri & J RamÔøΩrez, 2021, "Forecasting Dynamic Term Structure Models with Autoencoders," Documentos de Trabajo, Universidad del Rosario, number 19431, Jul.
• Luis Eduardo Penafiel Chang, 2021, "Panorama económico, político y sanitario de América Latina y el Caribe al comienzo de la pandemia del COVID-19," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue No. 95, pages 11-44.
• Magnolia Miriam Sosa Castro & Christian Bucio Pacheco & Edgar Ortiz Calisto, 2021, "Dynamic Stock Dependence and Monetary Variables in the United States (2000-2016): A Copula and Neural Network Approach," Revista Lecturas de Economía, Universidad de Antioquia, CIE, issue No. 96, pages 201-234.
• Rogelio Ladrón de Guevara Cortés & Salvador Torra Porras & Enric Monte Moreno, 2021, "Statistical and computational techniques for extraction of underlying systematic risk factors: a comparative study in the Mexican Stock Exchange," Revista Finanzas y Politica Economica, Universidad Católica de Colombia, volume 13, issue 2, pages 513-543.
• Amstad, Marlene & Gambacorta, Leonardo & He, Chao & Xia, Fan Dora, 2021, "Trade sentiment and the stock market: new evidence based on big data textual analysis of Chinese media," CEPR Discussion Papers, Centre for Economic Policy Research, number 15682, Jan.
• Smith, Adam & Seiler, Stephan & Aggarwal, Ishant, 2021, "Optimal Price Targeting," CEPR Discussion Papers, Centre for Economic Policy Research, number 16096, May.
• Calvo Pardo, Héctor & Olmo, Jose & Mancini, Tullio, 2021, "Machine Learning the Carbon Footprint of Bitcoin Mining," CEPR Discussion Papers, Centre for Economic Policy Research, number 16267, Jun.
• Ebrahimi Kahou, Mahdi & Fernández-Villaverde, Jesús & Perla, Jesse & Sood, Arnav, 2021, "Exploiting Symmetry in High-Dimensional Dynamic Programming," CEPR Discussion Papers, Centre for Economic Policy Research, number 16285, Jun.
• Ash, Elliott & Durante, Ruben & Grebenshchikova, Mariia & Schwarz, Carlo, 2021, "Visual Representation and Stereotypes in News Media," CEPR Discussion Papers, Centre for Economic Policy Research, number 16624, Oct.
• Margherita Comola & Carla Inguaggiato & Mariapia Mendola, 2021, "Learning about Farming: Innovation and Social Networks in a Resettled Community in Brazil," Development Working Papers, Centro Studi Luca d'Agliano, University of Milano, number 468, Feb.
• Saiz, Lorena & Ashwin, Julian & Kalamara, Eleni, 2021, "Nowcasting euro area GDP with news sentiment: a tale of two crises," Working Paper Series, European Central Bank, number 2616, Nov.
• Dimitrios Kartsonakis Mademlis & Nikolaos Dritsakis, 2021, "Volatility Forecasting using Hybrid GARCH Neural Network Models: The Case of the Italian Stock Market," International Journal of Economics and Financial Issues, International Journal of Economics and Financial Issues, volume 11, issue 1, pages 49-60.
• Pavel Baboshkin & Mafura Uandykova, 2021, "Multi-source Model of Heterogeneous Data Analysis for Oil Price Forecasting," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, volume 11, issue 2, pages 384-391.
• Felix Ghislain Yem Souhe & Camille Franklin Mbey & Alexandre Teplaira Boum & Pierre Ele, 2021, "Forecasting of Electrical Energy Consumption of Households in a Smart Grid," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, volume 11, issue 6, pages 221-233.
• Ouyang, Zi-sheng & Yang, Xi-te & Lai, Yongzeng, 2021, "Systemic financial risk early warning of financial market in China using Attention-LSTM model," The North American Journal of Economics and Finance, Elsevier, volume 56, issue C, DOI: 10.1016/j.najef.2021.101383.
• Fang, Ming & Taylor, Stephen, 2021, "A machine learning based asset pricing factor model comparison on anomaly portfolios," Economics Letters, Elsevier, volume 204, issue C, DOI: 10.1016/j.econlet.2021.109919.
• Su, Jiun-Hua, 2021, "Model selection in utility-maximizing binary prediction," Journal of Econometrics, Elsevier, volume 223, issue 1, pages 96-124, DOI: 10.1016/j.jeconom.2020.07.052.
• Elek, Péter & Bíró, Anikó, 2021, "Regional differences in diabetes across Europe – regression and causal forest analyses," Economics & Human Biology, Elsevier, volume 40, issue C, DOI: 10.1016/j.ehb.2020.100948.
• Xie, Wen-Jie & Li, Mu-Yao & Zhou, Wei-Xing, 2021, "Learning representation of stock traders and immediate price impacts," Emerging Markets Review, Elsevier, volume 48, issue C, DOI: 10.1016/j.ememar.2020.100791.
• DIMA, Bogdan & DIMA, Ştefana Maria & IOAN, Roxana, 2021, "Remarks on the behaviour of financial market efficiency during the COVID-19 pandemic. The case of VIX," Finance Research Letters, Elsevier, volume 43, issue C, DOI: 10.1016/j.frl.2021.101967.
• Delong, Łukasz & Lindholm, Mathias & Wüthrich, Mario V., 2021, "Gamma Mixture Density Networks and their application to modelling insurance claim amounts," Insurance: Mathematics and Economics, Elsevier, volume 101, issue PB, pages 240-261, DOI: 10.1016/j.insmatheco.2021.08.003.
• Denuit, Michel & Charpentier, Arthur & Trufin, Julien, 2021, "Autocalibration and Tweedie-dominance for insurance pricing with machine learning," Insurance: Mathematics and Economics, Elsevier, volume 101, issue PB, pages 485-497, DOI: 10.1016/j.insmatheco.2021.09.001.
• Carbonneau, Alexandre, 2021, "Deep hedging of long-term financial derivatives," Insurance: Mathematics and Economics, Elsevier, volume 99, issue C, pages 327-340, DOI: 10.1016/j.insmatheco.2021.03.017.
• Kireyev, Alexei & Leonidov, Andrei, 2021, "Twin trade shocks: Spillovers from US-China trade tensions," International Economics, Elsevier, volume 167, issue C, pages 174-188, DOI: 10.1016/j.inteco.2021.05.007.
• Imhof, David & Wallimann, Hannes, 2021, "Detecting bid-rigging coalitions in different countries and auction formats," International Review of Law and Economics, Elsevier, volume 68, issue C, DOI: 10.1016/j.irle.2021.106016.