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

2021

• Martin Baumgaertner & Johannes Zahner, 2021, "Whatever it takes to understand a central banker - Embedding their words using neural networks," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202130.
• Erik Heilmann & Janosch Henze & Heike Wetzel, 2021, "Machine learning in energy forecasts with an application to high frequency electricity consumption data," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202135.
• Sylwia Radomska, 2021, "Prognozowanie indeksu WIG20 za pomocą sieci neuronowych NARX i metody SVM," Bank i Kredyt, Narodowy Bank Polski, volume 52, issue 5, pages 457-472.
• Isil Erel & Léa H Stern & Chenhao Tan & Michael S Weisbach, 2021, "Selecting Directors Using Machine Learning," NBER Chapters, National Bureau of Economic Research, Inc, "Big Data: Long-Term Implications for Financial Markets and Firms".
• Kai Li & Feng Mai & Rui Shen & Xinyan Yan, 2021, "Measuring Corporate Culture Using Machine Learning," NBER Chapters, National Bureau of Economic Research, Inc, "Big Data: Long-Term Implications for Financial Markets and Firms".
• Julie Lassébie & Luca Marcolin & Marieke Vandeweyer & Benjamin Vignal, 2021, "Speaking the same language: A machine learning approach to classify skills in Burning Glass Technologies data," OECD Social, Employment and Migration Working Papers, OECD Publishing, number 263, Nov, DOI: 10.1787/adb03746-en.
• Sergiu Mihai Haţegan, 2021, "A Mapping Of The Literature On Econophysics," Annals of Faculty of Economics, University of Oradea, Faculty of Economics, volume 1, issue 1, pages 92-100, July.
• Apaar Sadhwani & Kay Giesecke & Justin Sirignano, 2021, "Deep Learning for Mortgage Risk
  [The Subprime Virus]," Journal of Financial Econometrics, Oxford University Press, volume 19, issue 2, pages 313-368.
• Marcus Buckmann & Andy Haldane & Anne-Caroline Hüser, 2021, "Comparing minds and machines: implications for financial stability," Oxford Review of Economic Policy, Oxford University Press and Oxford Review of Economic Policy Limited, volume 37, issue 3, pages 479-508.
• Daniele Bianchi & Matthias Büchner & Andrea Tamoni, 2021, "Bond Risk Premiums with Machine Learning
  [Quadratic term structure models: Theory and evidence]," The Review of Financial Studies, Society for Financial Studies, volume 34, issue 2, pages 1046-1089.
• Daniele Bianchi & Matthias Büchner & Tobias Hoogteijling & Andrea Tamoni, 2021, "Corrigendum: Bond Risk Premiums with Machine Learning
  [Bond risk premiums with machine learning]," The Review of Financial Studies, Society for Financial Studies, volume 34, issue 2, pages 1090-1103.
• Isil Erel & Léa H Stern & Chenhao Tan & Michael S Weisbach, 2021, "Selecting Directors Using Machine Learning
  [The role of boards of directors in corporate governance: A conceptual framework and survey]," The Review of Financial Studies, Society for Financial Studies, volume 34, issue 7, pages 3226-3264.
• Kai Li & Feng Mai & Rui Shen & Xinyan Yan, 2021, "Measuring Corporate Culture Using Machine Learning
  [Machine learning methods that economists should know about]," The Review of Financial Studies, Society for Financial Studies, volume 34, issue 7, pages 3265-3315.
• Constantin Ilie & Andreea-Daniela Moraru, 2021, "Management Based on Data Analysis. Part Two. Artificial Intelligence Data Modeling," Ovidius University Annals, Economic Sciences Series, Ovidius University of Constantza, Faculty of Economic Sciences, volume 0, issue 2, pages 743-748, December.
• Andrea Kolková & Aleksandr Kljuènikov, 2021, "Demand forecasting: an alternative approach based on technical indicator Pbands," Oeconomia Copernicana, Institute of Economic Research, volume 12, issue 4, pages 1063-1094, December, DOI: 10.24136/oc.2021.035.
• Katsafados, Apostolos G. & Leledakis, George N. & Pyrgiotakis, Emmanouil G. & Androutsopoulos, Ion & Fergadiotis, Manos, 2021, "Machine Learning in U.S. Bank Merger Prediction: A Text-Based Approach," MPRA Paper, University Library of Munich, Germany, number 108272, Jun.
• Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021, "Using Deep Learning Neural Networks to Predict the Knowledge Economy Index for Developing and Emerging Economies," MPRA Paper, University Library of Munich, Germany, number 109137, Apr.
• Andres, Antonio Rodriguez & Otero, Abraham & Amavilah, Voxi Heinrich, 2021, "Evaluation of technology clubs by clustering: A cautionary note," MPRA Paper, University Library of Munich, Germany, number 109138, May.
• Amavilah, Voxi Heinrich & Otero, Abraham & Andres, Antonio Rodriguez, 2021, "Knowledge Economy Classification in African Countries: A Model-Based Clustering Approach," MPRA Paper, University Library of Munich, Germany, number 109188, Mar.
• Kitova, Olga & Savinova, Victoria, 2021, "Development of an Ensemble of Models for Predicting Socio-Economic Indicators of the Russian Federation using IRT-Theory and Bagging Methods," MPRA Paper, University Library of Munich, Germany, number 110824, Nov.
• Medel-Ramírez, Carlos & Medel-López, Hilario & Lara-Mérida, Jennifer, 2021, "(SARS-CoV-2) COVID 19: Vigilancia genómica y evaluación del impacto en la población hablante de lengua indígena en México
  [(SARS-CoV-2) COVID 19: Genomic surveillance and impact assessment on the indigenous language-speaking population in Mexico]," MPRA Paper, University Library of Munich, Germany, number 110858, Nov.
• Korobilis, Dimitris & Shimizu, Kenichi, 2021, "Bayesian Approaches to Shrinkage and Sparse Estimation," MPRA Paper, University Library of Munich, Germany, number 111631, Dec.
• Baris Aksoy, 2021, "Predicting Direction of Stock Price Using Machine Learning Techniques: The Sample of Borsa Istanbul (Pay Senedi Fiyat Yönünün Makine Öğrenmesi Yöntemleri ile Tahmini: Borsa İstanbul Örneği)," Business and Economics Research Journal, Bursa Uludag University, Faculty of Economics and Administrative Sciences, volume 12, issue 1, pages 89-110.
• Athanasia Dimitriadou & Anna Agrapetidou & Periklis Gogas & Theophilos Papadimitriou, 2021, "Credit Rating Agencies: Evolution or Extinction?," DUTH Research Papers in Economics, Democritus University of Thrace, Department of Economics, number 9-2021, Oct.
• Juan de Lucio, 2021, "Estimación adelantada del crecimiento regional mediante redes neuronales LSTM," INVESTIGACIONES REGIONALES - Journal of REGIONAL RESEARCH, Asociación Española de Ciencia Regional, issue 49, pages 45-64.
• Christopher Holland & Anil Kavuri, 2021, "Artificial Intelligence and Digital Transformation of Insurance Markets," Journal of Financial Transformation, Capco Institute, volume 54, pages 104-115.
• Daehyeon PARK & Doojin RYU, 2021, "Forecasting Stock Market Dynamics using Bidirectional Long Short-Term Memory," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 2, pages 22-34, June.
• López Malpica, Gustavo & Hoyos Reyes, Luis Fernando & Rodríguez Benavides, Domingo & Mora Gutiérrez, Roman Anselmo, 2021, "Técnicas metaheurísticas para pronosticar el tipo de cambio del dólar de Estados Unidos con respecto al peso mexicano / Adaptation of Metaheuristic Techniques to Forecast the USD Dollar-MXN Peso Exchange Rate," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, volume 11, issue 2, pages 147-172, julio-dic.
• Paolo Angelis & Roberto Marchis & Mario Marino & Antonio Luciano Martire & Immacolata Oliva, 2021, "Betting on bitcoin: a profitable trading between directional and shielding strategies," Decisions in Economics and Finance, Springer;Associazione per la Matematica, volume 44, issue 2, pages 883-903, December, DOI: 10.1007/s10203-021-00324-z.
• Fred Espen Benth & Nils Detering & Silvia Lavagnini, 2021, "Accuracy of deep learning in calibrating HJM forward curves," Digital Finance, Springer, volume 3, issue 3, pages 209-248, December, DOI: 10.1007/s42521-021-00030-w.
• Akash Malhotra, 2021, "A hybrid econometric–machine learning approach for relative importance analysis: prioritizing food policy," Eurasian Economic Review, Springer;Eurasia Business and Economics Society, volume 11, issue 3, pages 549-581, September, DOI: 10.1007/s40822-021-00170-9.
• José Américo Pereira Antunes, 2021, "To supervise or to self-supervise: a machine learning based comparison on credit supervision," Financial Innovation, Springer;Southwestern University of Finance and Economics, volume 7, issue 1, pages 1-21, December, DOI: 10.1186/s40854-021-00242-4.
• Ali Habibnia & Esfandiar Maasoumi, 2021, "Forecasting in Big Data Environments: An Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), volume 19, issue 1, pages 363-381, December, DOI: 10.1007/s40953-021-00275-7.
• Harald Hruschka, 2021, "Comparing unsupervised probabilistic machine learning methods for market basket analysis," Review of Managerial Science, Springer, volume 15, issue 2, pages 497-527, February, DOI: 10.1007/s11846-019-00349-0.
• Rosina O. Weber & Kedma B. Duarte, 2021, "Data-driven artificial intelligence to automate researcher assessment," Scientometrics, Springer;Akadémiai Kiadó, volume 126, issue 4, pages 3265-3281, April, DOI: 10.1007/s11192-020-03859-x.
• Judith J. Castro Pérez & José E. Medina Reyes & Agustín I. Cabrera Llanos, 2021, "Forecasting the Effects of the COVID-19 Crisis on Economic Growth and the Microfinance Sector in Latin America: An Approach with Fuzzy Neural Networks," Springer Books, Springer, in: Griselda Dávila-Aragón & Salvador Rivas-Aceves, "The Future of Companies in the Face of a New Reality", DOI: 10.1007/978-981-16-2613-5_5.
• Yu-Min Lian & Chia-Hsuan Li & Yi-Hsuan Wei, 2021, "Machine Learning and Time Series Models for VNQ Market Predictions," Journal of Applied Finance & Banking, SCIENPRESS Ltd, volume 11, issue 5, pages 1-2.
• Ieva Meidutė-Kavaliauskienė & Gitana Dudzevičiūtė & Agnė Šimelytė & Nijolė Maknickienė, 2021, "Sustainability and regional security in the context of Lithuania," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 8, issue 3, pages 248-266, March, DOI: 10.9770/jesi.2021.8.3(14).
• Simona Hašková & Petr Šuleř & Tomáš Krulický, 2021, "Advantages of fuzzy approach compared to probabilistic approach in project evaluation," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 9, issue 2, pages 446-456, December, DOI: 10.9770/jesi.2021.9.2(29).
• Hanjo Odendaal, 2021, "A machine learning approach to domain specific dictionary generation. An economic time series framework," Working Papers, Stellenbosch University, Department of Economics, number 06/2021.
• Antonio Rodríguez Andrés & Voxi Heinrich S. Amavilah & Abraham Otero, 2021, "Evaluation of technology clubs by clustering: a cautionary note," Applied Economics, Taylor & Francis Journals, volume 53, issue 52, pages 5989-6001, November, DOI: 10.1080/00036846.2021.1934393.
• Thomas Conlon & John Cotter & Iason Kynigakis, 2021, "Machine Learning and Factor-Based Portfolio Optimization," Working Papers, Geary Institute, University College Dublin, number 202111, Mar.
• Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021, "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," Economics Working Paper Series, University of St. Gallen, School of Economics and Political Science, number 2104, Apr.
• Wei-Bin Zhang, 2021, "Economic Growth And Human Networking," Business & Management Compass, University of Economics Varna, issue 1, pages 5-25.
• Perez Katarzyna & Szczyt Małgorzata, 2021, "Classification of Open-End Investment Funds Using Artificial Neural Networks. The Case of Polish Equity Funds," Central European Economic Journal, Sciendo, volume 8, issue 55, pages 269-284, January, DOI: 10.2478/ceej-2021-0020.
• Dzik-Walczak Aneta & Odziemczyk Maciej, 2021, "Modelling cross-sectional tabular data using convolutional neural networks: Prediction of corporate bankruptcy in Poland," Central European Economic Journal, Sciendo, volume 8, issue 55, pages 352-377, January, DOI: 10.2478/ceej-2021-0024.
• Chi Yeong Nain & Chi Orson, 2021, "Modeling and Forecasting of Monthly Global Price of Bananas Using Seasonal Arima and Multilayer Perceptron Neural Network," Econometrics. Advances in Applied Data Analysis, Sciendo, volume 25, issue 3, pages 21-41, September, DOI: 10.15611/eada.2021.3.02.
• Młodzianowski Piotr & Valencia Hernandez Jose Aldo, 2021, "Evaluation of Cluster Management Quality Based on Consumer Opinion Sentiment Analysis," Foundations of Management, Sciendo, volume 13, issue 1, pages 219-228, January, DOI: 10.2478/fman-2021-0017.
• Gosztonyi Márton, 2021, "Comparative Research of Central and Eastern European Startup Researches Based on Artificial Intelligence-Based Natural Language Processing," Journal of Intercultural Management, Sciendo, volume 13, issue 4, pages 4-33, December, DOI: 10.2478/joim-2021-0070.
• Dawid Siwicki, 2021, "The Application of Machine Learning Algorithms for Spatial Analysis: Predicting of Real Estate Prices in Warsaw," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-05.
• Piotr Borowski & Marcin Chlebus, 2021, "Machine learning in the prediction of flat horse racing results in Poland," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-13.
• Kamil Korzeń & Robert Ślepaczuk, 2021, "Enhanced Index Replication Based on Smart Beta and Tail-Risk Asset Allocation," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-18.
• Jan Grudniewicz & Robert Ślepaczuk, 2021, "Application of machine learning in quantitative investment strategies on global stock markets," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-23.
• Nguyen Vo & Robert Ślepaczuk, 2021, "Applying Hybrid ARIMA-SGARCH in Algorithmic Investment Strategies on S&P500 Index," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-25.
• Sergio Castellano Gómez & Robert Ślepaczuk, 2021, "Robust optimisation in algorithmic investment strategies," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-27.
• Garg, Karan, 2021, "Machines and Markets : Assessing the Impact of Algorithmic Trading on Financial Market Efficiency," Warwick-Monash Economics Student Papers, Warwick Monash Economics Student Papers, number 11.
• Hinterlang, Natascha & Tänzer, Alina, 2021, "Optimal monetary policy using reinforcement learning," Discussion Papers, Deutsche Bundesbank, number 51/2021.
• Chen, Shi & Härdle, Wolfgang & Schienle, Melanie, 2021, "High-dimensional statistical learning techniques for time-varying limit order book networks," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2021-015.
• Luo, Jiawen & Klein, Tony & Walther, Thomas & Ji, Qiang, 2021, "Forecasting Realized Volatility of Crude Oil Futures Prices based on Machine Learning," QBS Working Paper Series, Queen's University Belfast, Queen's Business School, number 2021/04, DOI: 10.2139/ssrn.3701000.
• Anese, Gianluca & Corazza, Marco & Costola, Michele & Pelizzon, Loriana, 2021, "Impact of public news sentiment on stock market index return and volatility," SAFE Working Paper Series, Leibniz Institute for Financial Research SAFE, number 322.
• Dörr, Julian Oliver & Kinne, Jan & Lenz, David & Licht, Georg & Winker, Peter, 2021, "An integrated data framework for policy guidance in times of dynamic economic shocks," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 21-062.

2020

• Bo Cowgill & Fabrizio Dell'Acqua & Sandra Matz, 2020, "The Managerial Effects of Algorithmic Fairness Activism," AEA Papers and Proceedings, American Economic Association, volume 110, pages 85-90, May, DOI: 10.1257/pandp.20201035.
• Ashesh Rambachan & Jon Kleinberg & Jens Ludwig & Sendhil Mullainathan, 2020, "An Economic Perspective on Algorithmic Fairness," AEA Papers and Proceedings, American Economic Association, volume 110, pages 91-95, May, DOI: 10.1257/pandp.20201036.
• Bo Cowgill & Megan T. Stevenson, 2020, "Algorithmic Social Engineering," AEA Papers and Proceedings, American Economic Association, volume 110, pages 96-100, May, DOI: 10.1257/pandp.20201037.
• Martha J. Bailey & Connor Cole & Morgan Henderson & Catherine Massey, 2020, "How Well Do Automated Linking Methods Perform? Lessons from US Historical Data," Journal of Economic Literature, American Economic Association, volume 58, issue 4, pages 997-1044, December, DOI: 10.1257/jel.20191526.
• Ержан И.С. // Erzhan I.S., 2020, "Использование моделей machine learning при прогнозировании инфляции // Using machine learning models in inflation forecasting," Economic Review(National Bank of Kazakhstan), National Bank of Kazakhstan, issue 1, pages 39-48.
• Adolfo Rodríguez-Vargas, 2020, "Forecasting Costa Rican Inflation with Machine Learning Methods," Documentos de Trabajo, Banco Central de Costa Rica, number 2002, Mar.
• Hannes Wallimann & David Imhof & Martin Huber, 2020, "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," Papers, arXiv.org, number 2004.05629, Apr.
• Mykola Babiak & Jozef Barunik, 2020, "Deep Learning, Predictability, and Optimal Portfolio Returns," Papers, arXiv.org, number 2009.03394, Sep, revised Feb 2026.
• Xinwen Ni & Wolfgang Karl Hardle & Taojun Xie, 2020, "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," Papers, arXiv.org, number 2009.12121, Sep, revised Aug 2021.
• Oren Barkan & Jonathan Benchimol & Itamar Caspi & Eliya Cohen & Allon Hammer & Noam Koenigstein, 2020, "Forecasting CPI Inflation Components with Hierarchical Recurrent Neural Networks," Papers, arXiv.org, number 2011.07920, Nov, revised Feb 2022.
• Bo Cowgill & Fabrizio Dell'Acqua & Sandra Matz, 2020, "The Managerial Effects of Algorithmic Fairness Activism," Papers, arXiv.org, number 2012.02393, Dec.
• Anton Gerunov, 2020, "Classification algorithms for modeling economic choice," Economic Thought journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 2, pages 45-67.
• Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2020, "Financial frictions and the wealth distribution," Working Papers, Banco de España, number 2013, Jun.
• Carlos León & Paolo Barucca & Oscar Acero & Gerardo Gage & Fabio Ortega, 2020, "Pattern recognition of financial institutions’ payment behavior," Borradores de Economia, Banco de la Republica de Colombia, number 1130, Sep, DOI: https://doi.org/10.32468/be.1130.
• Tetsuya Kaji & Elena Manresa & Guillaume Pouliot, 2020, "An Adversarial Approach to Structural Estimation," Working Papers, Becker Friedman Institute for Research In Economics, number 2020-144.
• Michael Creel, 2020, "Inference Using Simulated Neural Moments," Working Papers, Barcelona School of Economics, number 1182, Jun.
• Salim Sercan SARI & Þule Yüksel YÝÐÝTER, 2020, "Borsa Istanbul Hisse Senedi Getirilerinin ANFIS Aracýlýðýyla Tahmin Edilmesi," Bingol University Journal of Economics and Administrative Sciences, Bingol University, Faculty of Economics and Administrative Sciences, volume 4, issue 1, pages 171-193, August, DOI: https://doi.org/10.33399/biibfad.74.
• Ferencek Aljaž & Kofjač Davorin & Škraba Andrej & Sašek Blaž & Borštnar Mirjana Kljajić, 2020, "Deep Learning Predictive Models for Terminal Call Rate Prediction during the Warranty Period," Business Systems Research, Sciendo, volume 11, issue 2, pages 36-50, October, DOI: 10.2478/bsrj-2020-0014.
• Ramis Khbaibullin & Sergei Seleznev, 2020, "Stochastic Gradient Variational Bayes and Normalizing Flows for Estimating Macroeconomic Models," Bank of Russia Working Paper Series, Bank of Russia, number wps61, Oct.
• Jonnathan R. Cáceres Santos, 2020, "Modelos de Machine Learning para el análisis y pronóstico de la situación financiera de bancos – Caso boliviano," Revista de Análisis del BCB, Banco Central de Bolivia, volume 33, issue 1, pages 69-91, July - De.
• Soohyon Kim, 2020, "Macroeconomic and Financial Market Analyses and Predictions through Deep Learning," Working Papers, Economic Research Institute, Bank of Korea, number 2020-18, Sep.
• Mykola Babiak & Jozef Barunik, 2020, "Deep Learning, Predictability, and Optimal Portfolio Returns," CERGE-EI Working Papers, The Center for Economic Research and Graduate Education - Economics Institute, Prague, number wp677, Dec.
• Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2020, "Financial Frictions and the Wealth Distribution," CESifo Working Paper Series, CESifo, number 8482.
• Oksana Bashchenko & Alexis Marchal, 2020, "Deep Learning for Asset Bubbles Detection," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-08, Mar.
• Oksana Bashchenko & Alexis Marchal, 2020, "Deep Learning, Jumps, and Volatility Bursts," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-10, Mar.
• Roberto Molinari & Gaetan Bakalli & Stéphane Guerrier & Cesare Miglioli & Samuel Orso & O. Scaillet, 2020, "Swag: A Wrapper Method for Sparse Learning," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 20-49, Jun.
• Luis Jorge Garay & Eduardo Salcedo-Albar�n & Daphne �lvarez, 2020, "Macro-Corrupción y Cooptación Institucional en el departamento de Córdoba, Colombia," Informes de Investigación, Fedesarrollo, number 18137, Apr.
• Giavazzi, Francesco & Lemoli, Giacomo & Rubera, Gaia & Iglhaut, Felix, 2020, "Terrorist Attacks, Cultural Incidents and the Vote for Radical Parties: Analyzing Text from Twitter," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14455, Feb.
• Adams-Prassl, Abigail & Balgova, Maria & Qian, Matthias, 2020, "Flexible Work Arrangements in Low Wage Jobs: Evidence from Job Vacancy Data," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15263, Sep.
• Taylor, Mark & Filippou, Ilias & Rapach, David & Zhou, Guofu, 2020, "Exchange Rate Prediction with Machine Learning and a Smart Carry Trade Portfolio," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15305, Sep.
• Gobillon, Laurent & Combes, Pierre-Philippe & Zylberberg, Yanos, 2020, "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15308, Sep.
• Niklas, Britta & Rinke, Wolfram, 2020, "Pricing Models for German Wine: Hedonic Regression vs. Machine Learning," Journal of Wine Economics, Cambridge University Press, volume 15, issue 3, pages 284-311, August.
• Ангелин Лалев & Александрина Александрова, 2020, "Използване На Дълбоки Невронни Мрежи За Откриване На Измами С Кредитни Карти," Scientific Research Almanac, D. A. Tsenov Academy of Economics, Svishtov, Bulgaria, volume 28, issue 1 Year 20, pages 39-62.
• Leonard Sabetti & Ronald Heijmans, 2020, "Shallow or deep? Detecting anomalous flows in the Canadian Automated Clearing and Settlement System using an autoencoder," Working Papers, DNB, number 681, Apr.
• Azqueta-Gavaldon, Andres & Hirschbühl, Dominik & Onorante, Luca & Saiz, Lorena, 2020, "Nowcasting business cycle turning points with stock networks and machine learning," Working Paper Series, European Central Bank, number 2494, Nov.
• Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2020, "The relationship between air pollution and COVID-19-related deaths: An application to three French cities," Applied Energy, Elsevier, volume 279, issue C, DOI: 10.1016/j.apenergy.2020.115835.
• Thomas, Sheetal & Goel, Mridula & Agrawal, Dipak, 2020, "A framework for analyzing financial behavior using machine learning classification of personality through handwriting analysis," Journal of Behavioral and Experimental Finance, Elsevier, volume 26, issue C, DOI: 10.1016/j.jbef.2020.100315.
• Jahn, Malte, 2020, "Artificial neural network regression models in a panel setting: Predicting economic growth," Economic Modelling, Elsevier, volume 91, issue C, pages 148-154, DOI: 10.1016/j.econmod.2020.06.008.
• Philip, R., 2020, "Estimating permanent price impact via machine learning," Journal of Econometrics, Elsevier, volume 215, issue 2, pages 414-449, DOI: 10.1016/j.jeconom.2019.10.002.
• Jasiński, Tomasz, 2020, "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, volume 213, issue C, DOI: 10.1016/j.energy.2020.118784.
• Tölö, Eero, 2020, "Predicting systemic financial crises with recurrent neural networks," Journal of Financial Stability, Elsevier, volume 49, issue C, DOI: 10.1016/j.jfs.2020.100746.
• Marcjasz, Grzegorz & Uniejewski, Bartosz & Weron, Rafał, 2020, "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," International Journal of Forecasting, Elsevier, volume 36, issue 2, pages 466-479, DOI: 10.1016/j.ijforecast.2019.07.002.
• Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2020, "Nonlinear forecast combinations: An example using euro-area real GDP growth," Journal of Economic Behavior & Organization, Elsevier, volume 180, issue C, pages 579-589, DOI: 10.1016/j.jebo.2018.09.021.
• Maehashi, Kohei & Shintani, Mototsugu, 2020, "Macroeconomic forecasting using factor models and machine learning: an application to Japan," Journal of the Japanese and International Economies, Elsevier, volume 58, issue C, DOI: 10.1016/j.jjie.2020.101104.
• Kanazawa, Nobuyuki, 2020, "Radial basis functions neural networks for nonlinear time series analysis and time-varying effects of supply shocks," Journal of Macroeconomics, Elsevier, volume 64, issue C, DOI: 10.1016/j.jmacro.2020.103210.
• León, Carlos & Barucca, Paolo & Acero, Oscar & Gage, Gerardo & Ortega, Fabio, 2020, "Pattern recognition of financial institutions’ payment behavior," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 1, issue 1, DOI: 10.1016/j.latcb.2020.100011.
• Rodríguez-Vargas, Adolfo, 2020, "Forecasting Costa Rican inflation with machine learning methods," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 1, issue 1, DOI: 10.1016/j.latcb.2020.100012.
• Rubio, Jeniffer & Barucca, Paolo & Gage, Gerardo & Arroyo, John & Morales-Resendiz, Raúl, 2020, "Classifying payment patterns with artificial neural networks: An autoencoder approach," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 1, issue 1, DOI: 10.1016/j.latcb.2020.100013.
• Dungey, Mardi & Islam, Raisul & Volkov, Vladimir, 2020, "Crisis transmission: Visualizing vulnerability," Pacific-Basin Finance Journal, Elsevier, volume 59, issue C, DOI: 10.1016/j.pacfin.2019.101255.
• Jiang, Minqi & Liu, Jiapeng & Zhang, Lu & Liu, Chunyu, 2020, "An improved Stacking framework for stock index prediction by leveraging tree-based ensemble models and deep learning algorithms," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 541, issue C, DOI: 10.1016/j.physa.2019.122272.
• Brida, Juan Gabriel & Carrera, Edgar J. Sanchez & Segarra, Verónica, 2020, "Clustering and regime dynamics for economic growth and income inequality," Structural Change and Economic Dynamics, Elsevier, volume 52, issue C, pages 99-108, DOI: 10.1016/j.strueco.2019.09.010.
• Baris Yalin Uzunlu & Syed Muzammil Hussain, 2020, "Employing Machine Learning Algorithms to build Trading Strategies with higher than Risk-Free Returns," International Econometric Review (IER), Economic Research Association, volume 12, issue 2, pages 112-138, September.
• Michael Puglia & Adam Tucker, 2020, "Machine Learning, the Treasury Yield Curve and Recession Forecasting," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2020-038, May, DOI: 10.17016/FEDS.2020.038.
• Wallimann, Hannes & Imhof, David & Huber, Martin, 2020, "A Machine Learning Approach for Flagging Incomplete Bid-rigging Cartels," FSES Working Papers, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland, number 513, Apr.
• Huber, Martin & Imhof, David, 2020, "Transnational machine learning with screens for flagging bid-rigging cartels," FSES Working Papers, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland, number 519, Oct.
• Raquel M. Gaspar & Sara D. Lopes & Bernardo Sequeira, 2020, "Neural Network Pricing of American Put Options," Risks, MDPI, volume 8, issue 3, pages 1-24, July.
• Gusarov, N. & Talebijmalabad, A. & Joly, I., 2020, "Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness," Working Papers, Grenoble Applied Economics Laboratory (GAEL), number 2020-12.
• Nikita Gusarov & Amirreza Talebijamalabad & Iragaël Joly, 2020, "Exploration of model performances in the presence of heterogeneous preferences and random effects utilities awareness," Working Papers, HAL, number hal-03019739, Oct.
• Péter Elek & Anikó Bíró, 2020, "Regional differences in diabetes across Europe –regression and causal forest analyses," KRTK-KTI WORKING PAPERS, Institute of Economics, Centre for Economic and Regional Studies, number 2027, Jun.
• Steffen Q. Mueller & Patrick Ring & Maria Fischer, 2020, "Excited and aroused: The predictive importance of simple choice process metrics," Working Papers, Chair for Economic Policy, University of Hamburg, number 067, Dec.
• Breuer, Wolfgang & Steininger, Bertram, 2020, "Recent Trends in Real Estate Research: A Comparison of Recent Working Papers and Publications using Machine Learning Algorithms," Working Paper Series, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance, number 20/15, Dec.
• Pablo Rocha Portugal & Horacio Vera Cossio & Fernanda Wanderley, 2020, "Redes, características locales y flujos migratorios - Un estudio de la migración interna desde el análisis de redes sociales para impulsar el desarrollo local," SDSN Bolivia, Universidad Privada Boliviana, number 07-20, Aug.
• Raquel M. Gaspar & Sara D. Lopes & Bernardo Sequeira, 2020, "Neural Network pricing of American put options," Working Papers REM, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa, number 2020/0122, Apr.
• Shahid Ali & Junrui Zhang & Aamir Azeem & Asif Mahmood, 2020, "Impact Of Electricity Consumption On Economic Growth: An Application Of Vector Error Correction Model and Artificial Neural Networks," Journal of Developing Areas, Tennessee State University, College of Business, volume 54, issue 4, pages 89-104, October-D.
• Özlem DENÄ°Z BAÅžAR & Elif GÃœNEREN GENÇ, 2020, "A Comparison Of Logistic Regression, Artificial Neural Networks And Moora Methods In Estimation Of The Safety Of Countries," JOURNAL OF LIFE ECONOMICS, Holistence Publications, volume 7, issue 2, pages .123-134, April, DOI: 10.15637/jlecon.7.008.
• Kubilay ERÄ°SLÄ°K & Özlem DENÄ°Z BAÅžAR, 2020, "Estimation Of The Sectors Of The Investments Made On Venture Capital Companies With Artificial Neural Networks And Multiple Logistic Regression Analysis," JOURNAL OF LIFE ECONOMICS, Holistence Publications, volume 7, issue 4, pages 297-308, October, DOI: 10.15637/jlecon.7.022.
• Jikhan Jeong, 2020, "Identifying Consumer Preferences from User- and Crowd-Generated Digital Footprints on Amazon.com by Leveraging Machine Learning and Natural Language Processing," 2020 Papers, Job Market Papers, number pje208, Nov.
• Zongwu Cai & Xiyuan Liu, 2020, "A Functional-Coefficient VAR Model for Dynamic Quantiles with Constructing Financial Network," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202017, Oct, revised Oct 2020.
• Zongwu Cai & Xiyuan Liu, 2020, "A Nonparametric Dynamic Network via Multivariate Quantile Autoregressions," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202209, Oct, revised Mar 2022.
• Zongwu Cai & Xiyuan Liu & Liangjun Su, 2020, "A Functional-Coefficient VAR Model for Dynamic Quantiles and Its Application to Constructing Nonparametric Financial Network," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202406, Oct, revised Jan 2024.
• Jermain C. Kaminski & Christian Hopp, 2020, "Predicting outcomes in crowdfunding campaigns with textual, visual, and linguistic signals," Small Business Economics, Springer, volume 55, issue 3, pages 627-649, October, DOI: 10.1007/s11187-019-00218-w.
• Daniel Wochner, 2020, "Dynamic Factor Trees and Forests – A Theory-led Machine Learning Framework for Non-Linear and State-Dependent Short-Term U.S. GDP Growth Predictions," KOF Working papers, KOF Swiss Economic Institute, ETH Zurich, number 20-472, May, DOI: 10.3929/ethz-b-000399304.
• Boriss Siliverstovs & Daniel Wochner, 2020, "Recessions as Breadwinner for Forecasters State-Dependent Evaluation of Predictive Ability: Evidence from Big Macroeconomic US Data," Working Papers, Latvijas Banka, number 2020/02, Feb.
• Francesco Giavazzi & Felix Iglhaut & Giacomo Lemoli & Gaia Rubera, 2020, "Terrorist Attacks, Cultural Incidents and the Vote for Radical Parties: Analyzing Text from Twitter," NBER Working Papers, National Bureau of Economic Research, Inc, number 26825, Mar.
• Munisamy Gopinath & Feras A. Batarseh & Jayson Beckman, 2020, "Machine Learning in Gravity Models: An Application to Agricultural Trade," NBER Working Papers, National Bureau of Economic Research, Inc, number 27151, May.
• Marshall Burke & Anne Driscoll & David Lobell & Stefano Ermon, 2020, "Using Satellite Imagery to Understand and Promote Sustainable Development," NBER Working Papers, National Bureau of Economic Research, Inc, number 27879, Oct.
• Jyldyz Djumalieva & Stef Garasto & Cath Sleeman, 2020, "Evaluating a new earnings indicator. Can we improve the timeliness of existing statistics on earnings by using salary information from online job adverts?," Economic Statistics Centre of Excellence (ESCoE) Discussion Papers, Economic Statistics Centre of Excellence (ESCoE), number ESCoE DP-2020-19, Dec.
• Nicolas Woloszko, 2020, "Adaptive Trees: a new approach to economic forecasting," OECD Economics Department Working Papers, OECD Publishing, number 1593, Jan, DOI: 10.1787/5569a0aa-en.
• Nicolas Woloszko, 2020, "Tracking activity in real time with Google Trends," OECD Economics Department Working Papers, OECD Publishing, number 1634, Dec, DOI: 10.1787/6b9c7518-en.
• Tobias Götze & Marc Gürtler & Eileen Witowski, 2020, "Improving CAT bond pricing models via machine learning," Journal of Asset Management, Palgrave Macmillan, volume 21, issue 5, pages 428-446, September, DOI: 10.1057/s41260-020-00167-0.
• Jaromir Vrbka, 2020, "The use of neural networks to determine value based drivers for SMEs operating in the rural areas of the Czech Republic," Oeconomia Copernicana, Institute of Economic Research, volume 11, issue 2, pages 325-346, June, DOI: 10.24136/oc.2020.014.
• Grilli, Luca & Santoro, Domenico, 2020, "How Boltzmann Entropy Improves Prediction with LSTM," MPRA Paper, University Library of Munich, Germany, number 100578, May.
• Gomez-Ruano, Gerardo, 2020, "Data Science: A Primer for Economists," MPRA Paper, University Library of Munich, Germany, number 102928.
• Diunugala, Hemantha Premakumara & Mombeuil, Claudel, 2020, "Modeling and predicting foreign tourist arrivals to Sri Lanka: A comparison of three different methods," MPRA Paper, University Library of Munich, Germany, number 103779, Oct.
• Kitova, Olga & Dyakonova, Ludmila & Savinova, Victoria, 2020, "Prediction of Socio-Economic Indicators of the Megapolis Development on the Basis of the Intellectual Forecasting Information System “SHM Horizon”," MPRA Paper, University Library of Munich, Germany, number 104234, Jul, revised 19 Nov 2020.
• Fajar, Muhammad & Hartini, Sri, 2020, "Comparison of ARIMA, SSA, and ARIMA – SSA hybrid model performance in Indonesian economic growth forecasting," MPRA Paper, University Library of Munich, Germany, number 105045, Jun, revised 16 Jun 2020.
• Grilli, Luca & Santoro, Domenico, 2020, "Generative Adversarial Network for Market Hourly Discrimination," MPRA Paper, University Library of Munich, Germany, number 99846, Apr.
• Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2020, "Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Working Papers, University of Pretoria, Department of Economics, number 202056, Jun.
• Avela, Aleksi & Lehmus, Markku, 2020, "It’s in the News: Developing a Real Time Index for Economic Uncertainty Based on Finnish News Titles," ETLA Working Papers, The Research Institute of the Finnish Economy, number 84, Dec.
• Fela Ozbey & Semin Paksoy, 2020, "Estimation of the XU100 Index Return Volatility with the Integration of GARCH Family Models and ANN," Business and Economics Research Journal, Bursa Uludag University, Faculty of Economics and Administrative Sciences, volume 11, issue 2, pages 385-396.
• Kriti Mahajan & Anand Srinivasan, 2020, "Inflation Forecasting In Emerging Markets: A Machine Learning Approach," Working Papers, Centre for Advanced Financial Research and Learning (CAFRAL), number 022296, Feb.
• Andreas Psimopoulos, 2020, "Forecasting Economic Recessions Using Machine Learning:An Empirical Study in Six Countries," South-Eastern Europe Journal of Economics, Association of Economic Universities of South and Eastern Europe and the Black Sea Region, volume 18, issue 1, pages 40-99.
• Medina Reyes, José Eduardo & Castro Pérez, Judith Jazmin & Cabrera Llanos, Agustín Ignacio & Cruz Aké, Salvador, 2020, "Red neuronal autorregresiva difusa tipo Sugeno con funciones de membresía triangular y trapezoidal: una aplicación al pronóstico de índices del mercado bursátil / Sugeno Type Fuzzy Nonlinear Autoregressive Neural Networks with Triangular and Trapezoi," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, volume 10, issue 1, pages 77-101, enero-jun.
• Anton Gerunov, 2020, "Binary Classification Problems in Economics and 136 Different Ways to Solve Them," Bulgarian Economic Papers, Faculty of Economics and Business Administration, Sofia University St Kliment Ohridski - Bulgaria // Center for Economic Theories and Policies at Sofia University St Kliment Ohridski, number bep-2020-02, Mar, revised Mar 2020.
• Uğur ERCAN & Sezgin IRMAK & Kerim Kürşat ÇEVİK & Erokan CANBAZOĞLU, 2020, "Estimating Electricity Consumption Levels in Dwellings Using Artificial Neural NetworksAbstract: Most of the studies on electricity consumption were conducted using econometric models and statistical methods. Studies that aiming at predicting electri," Sosyoekonomi Journal, Sosyoekonomi Society, issue 28(46).
• Yakup SÖYLEMEZ, 2020, "Prediction of Gold Prices Using Multilayer Artificial Neural Networks Method," Sosyoekonomi Journal, Sosyoekonomi Society, issue 28(46).
• Alexander Jakob Dautel & Wolfgang Karl Härdle & Stefan Lessmann & Hsin-Vonn Seow, 2020, "Forex exchange rate forecasting using deep recurrent neural networks," Digital Finance, Springer, volume 2, issue 1, pages 69-96, September, DOI: 10.1007/s42521-020-00019-x.
• Marcos Álvarez-Díaz, 2020, "Is it possible to accurately forecast the evolution of Brent crude oil prices? An answer based on parametric and nonparametric forecasting methods," Empirical Economics, Springer, volume 59, issue 3, pages 1285-1305, September, DOI: 10.1007/s00181-019-01665-w.
• Wolfgang Breuer & Bertram I. Steininger, 2020, "Recent trends in real estate research: a comparison of recent working papers and publications using machine learning algorithms," Journal of Business Economics, Springer, volume 90, issue 7, pages 963-974, August, DOI: 10.1007/s11573-020-01005-w.
• Octavian Machidon & Dragoș Stoica & Aleš Tavčar, 2020, "Enhancing the Usability of European Digital Cultural Library Using Web Architectures and Deep Learning," Springer Proceedings in Business and Economics, Springer, in: Vicky Katsoni & Thanasis Spyriadis, "Cultural and Tourism Innovation in the Digital Era", DOI: 10.1007/978-3-030-36342-0_16.
• Nataliya Matveeva & Anuška Ferligoj, 2020, "Scientific collaboration in Russian universities before and after the excellence initiative Project 5-100," Scientometrics, Springer;Akadémiai Kiadó, volume 124, issue 3, pages 2383-2407, September, DOI: 10.1007/s11192-020-03602-6.
• Agustín García & Agustín García & Miguel A. Jaramillo-Morán, 2020, "Short-term European Union Allowance price forecasting with artificial neural networks," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 8, issue 1, pages 261-275, September, DOI: 10.9770/jesi.2020.8.1(18).
• Islam, Raisul & Volkov, Vladimir, 2020, "Contagion or interdependence? Comparing signed and unsigned spillovers," Working Papers, University of Tasmania, Tasmanian School of Business and Economics, number 2020-05.
• Islam, Raisul & Volkov, Vladimir, 2020, "Calm before the storm: an early warning approach before and during the COVID-19 crisis," Working Papers, University of Tasmania, Tasmanian School of Business and Economics, number 2020-09.
• Kohei Maehashi & Mototsugu Shintani, 2020, "Macroeconomic Forecasting Using Factor Models and Machine Learning: An Application to Japan," CIRJE F-Series, CIRJE, Faculty of Economics, University of Tokyo, number CIRJE-F-1146, Mar.
• Heni Boubaker & Giorgio Canarella & Rangan Gupta & Stephen M. Miller, 2020, "Hybrid ARFIMA Wavelet Artificial Neural Network Model for DJIA Index Forecasting," Working papers, University of Connecticut, Department of Economics, number 2020-10, Aug.
• Dmytro Krukovets, 2020, "Data Science Opportunities at Central Banks: Overview," Visnyk of the National Bank of Ukraine, National Bank of Ukraine, issue 249, pages 13-24, DOI: 10.26531/vnbu2020.249.02.
• Jerić Silvija Vlah, 2020, "Comparing classification algorithms for prediction on CROBEX data," Croatian Review of Economic, Business and Social Statistics, Sciendo, volume 6, issue 2, pages 4-11, December, DOI: 10.2478/crebss-2020-0007.
• Wójcik Filip & Górnik Michał, 2020, "Improvement of E-Commerce Recommendation Systems with Deep Hybrid Collaborative Filtering with Content: A Case Study," Econometrics. Advances in Applied Data Analysis, Sciendo, volume 24, issue 3, pages 37-50, September, DOI: 10.15611/eada.2020.3.03.
• Latoszek Michał & Ślepaczuk Robert, 2020, "Does the inclusion of exposure to volatility into diversified portfolio improve the investment results? Portfolio construction from the perspective of a Polish investor," Economics and Business Review, Sciendo, volume 6, issue 1, pages 46-81, March, DOI: 10.18559/ebr.2020.1.3.
• Tratkowski Grzegorz, 2020, "Identification of nonlinear determinants of stock indices derived by Random Forest algorithm," International Journal of Management and Economics, Warsaw School of Economics, Collegium of World Economy, volume 56, issue 3, pages 209-217, September, DOI: 10.2478/ijme-2020-0017.
• Lee Changro & Park Keith Key-Ho, 2020, "Representing Uncertainty in Property Valuation Through a Bayesian Deep Learning Approach," Real Estate Management and Valuation, Sciendo, volume 28, issue 4, pages 15-23, December, DOI: 10.1515/remav-2020-0028.
• Oleh Bilyk & Paweł Sakowski & Robert Ślepaczuk, 2020, "Investing in VIX futures based on rolling GARCH models forecasts," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-10.
• Maciej Wysocki & Robert Ślepaczuk, 2020, "Artificial Neural Networks Performance in WIG20 Index Options Pricing," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-19.
• Mateusz Kijewski & Robert Ślepaczuk, 2020, "Predicting prices of S&P500 index using classical methods and recurrent neural networks," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-27.
• Karol Kielak & Robert Ślepaczuk, 2020, "Value-at-risk — the comparison of state-of-the-art models on various assets," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-28.
• Bartłomiej Bollin & Robert Ślepaczuk, 2020, "Variance Gamma Model in Hedging Vanilla and Exotic Options," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-31.
• Robert Ślepaczuk & Igor Wabik, 2020, "The impact of the results of football matches on the stock prices of soccer clubs," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2020-35.
• Andrea Bucci, 2020, "Cholesky–ANN models for predicting multivariate realized volatility," Journal of Forecasting, John Wiley & Sons, Ltd., volume 39, issue 6, pages 865-876, September, DOI: 10.1002/for.2664.
• Hinterlang, Natascha, 2020, "Predicting monetary policy using artificial neural networks," Discussion Papers, Deutsche Bundesbank, number 44/2020.
• Ollech, Daniel & Webel, Karsten, 2020, "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers, Deutsche Bundesbank, number 55/2020.
• Diunugala, Hemantha Premakumara & Mombeuil, Claudel, 2020, "Modeling and predicting foreign tourist arrivals to Sri Lanka: A comparison of three different methods," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, volume 6, issue 3, pages 3-13, DOI: 10.5281/zenodo.4055960.
• Dautel, Alexander Jakob & Härdle, Wolfgang Karl & Lessmann, Stefan & Seow, Hsin-Vonn, 2020, "Forex exchange rate forecasting using deep recurrent neural networks," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2020-006.
• Ni, Xinwen & Härdle, Wolfgang Karl & Xie, Taojun, 2020, "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2020-013.

2019

• Ali Babikir & Mohammed Elamin Hassan & Henry Mwambi, 2019, "Asymmetry, Fat-tail and Autoregressive Conditional Density in Daily Stocks Return Data," Annals of Economics and Statistics, GENES, issue 135, pages 57-68, DOI: 10.15609/annaeconstat2009.135.0057.
• Jens Ludwig & Sendhil Mullainathan & Jann Spiess, 2019, "Augmenting Pre-Analysis Plans with Machine Learning," AEA Papers and Proceedings, American Economic Association, volume 109, pages 71-76, May.
• Cristiana Chiriac & Ștefan Grapă & Mihai-Cristian Orzan, 2019, "An EEG Analysis on the Perception of the Consumers Regarding Video-Commercials from the Automotive Industry," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, volume 1, issue 1, pages 318-326, November.
• Cristiana Chiriac & Laura Daniela RoÈ™ca, 2019, "Automotive Industry Video-Commercials – A Pluralistic Research Based on an Eye-Tracking Experiment," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, volume 1, issue 1, pages 327-336, November.
• Alfonso Aja Kindelan & Leovardo Mata Mata & Jaime Humberto Beltrán Godoy, 2019, "Analysis and projection of Pfizer's stock returns, in the period 2018-2020, through differential neural networks," The Anahuac Journal, Business and Economics School. Anahuac University (Mexico)., volume 19, issue 1, pages 13-34, June, DOI: 10.36105/theanahuacjour.2019v19n1.0.
• Күзенбаев С.Т. // Kuzenbayev S.T. & Крупа Е. А. // Krupa E.A., 2019, "Использование технологии искусственного интеллекта при осуществлении денежно-кредитной политики // The use of artificial intelligence technology in the implementation of monetary policy," Economic Review(National Bank of Kazakhstan), National Bank of Kazakhstan, issue 1, pages 55-69.
• Andreas Joseph, 2019, "From interpretability to inference: an estimation framework for universal approximators," Papers, arXiv.org, number 1903.04209, Mar, revised Dec 2024.
• Ali Habibnia & Esfandiar Maasoumi, 2019, "Forecasting in Big Data Environments: an Adaptable and Automated Shrinkage Estimation of Neural Networks (AAShNet)," Papers, arXiv.org, number 1904.11145, Apr.
• Stefania Albanesi & Domonkos F. Vamossy, 2019, "Predicting Consumer Default: A Deep Learning Approach," Papers, arXiv.org, number 1908.11498, Aug, revised Oct 2019.
• Freddy Cepeda-Lopez & Fredy Gamboa-Estrada & Carlos León & Hernán Rincón-Castro, 2019, "Colombian liberalization and integration to world trade markets: Much ado about nothing," Borradores de Economia, Banco de la Republica de Colombia, number 1065, Feb, DOI: 10.32468/be.1065.pdf?sequence=10&is.
• Denis Shibitov & Mariam Mamedli, 2019, "The finer points of model comparison in machine learning: forecasting based on russian banks’ data," Bank of Russia Working Paper Series, Bank of Russia, number wps43, Aug.
• Andreas Joseph, 2019, "Parametric inference with universal function approximators," Bank of England working papers, Bank of England, number 784, Mar.