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

• 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, Sciendo, 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, Sciendo, 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 التَّكَي," 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, SoDa Laboratories, 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, SoDa Laboratories, 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.
• Andrea Matranga & Joan Serrat & Jonathan Hersh & Andre Groeger & Hannes Mueller, 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 working papers, 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 working papers, 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.
• Gambacorta, Leonardo & Amstad, Marlene & 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, C.E.P.R. Discussion Papers, number 15682, Jan.
• Smith, Adam & Seiler, Stephan & Aggarwal, Ishant, 2022, "Optimal Price Targeting," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16096, Jul.
• Calvo Pardo, Héctor & Olmo, Jose & Mancini, Tullio, 2021, "Machine Learning the Carbon Footprint of Bitcoin Mining," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16267, Jun.
• Fernández-Villaverde, Jesús & Ebrahimi Kahou, Mahdi & Perla, Jesse & Sood, Arnav, 2021, "Exploiting Symmetry in High-Dimensional Dynamic Programming," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16285, Jun.
• Ash, Elliott & Durante, Ruben & Grebenshchikova, Mariia & Schwarz, Carlo, 2022, "Visual Representation and Stereotypes in News Media," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 16624, May.
• 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, Econjournals, 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, Econjournals, 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, Econjournals, 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.
• Bhar, Ramaprasad & Malliaris, A.G., 2021, "Modeling U.S. monetary policy during the global financial crisis and lessons for Covid-19," Journal of Policy Modeling, Elsevier, volume 43, issue 1, pages 15-33, DOI: 10.1016/j.jpolmod.2020.07.001.
• Shen, Lily & Ross, Stephen, 2021, "Information value of property description: A Machine learning approach," Journal of Urban Economics, Elsevier, volume 121, issue C, DOI: 10.1016/j.jue.2020.103299.
• Sabetti, Leonard & Heijmans, Ronald, 2021, "Shallow or deep? Training an autoencoder to detect anomalous flows in a retail payment system," Latin American Journal of Central Banking (previously Monetaria), Elsevier, volume 2, issue 2, DOI: 10.1016/j.latcb.2021.100031.
• Magazzino, Cosimo & Mele, Marco & Schneider, Nicolas, 2021, "A machine learning approach on the relationship among solar and wind energy production, coal consumption, GDP, and CO2 emissions," Renewable Energy, Elsevier, volume 167, issue C, pages 99-115, DOI: 10.1016/j.renene.2020.11.050.
• Gaggero, Alberto A. & Piazza, Giovanni, 2021, "Multilayer networks and route entry into the airline industry: Evidence from the U.S. domestic market," Research in Transportation Economics, Elsevier, volume 90, issue C, DOI: 10.1016/j.retrec.2021.101044.
• Chen, Shun & Ge, Lei, 2021, "A learning-based strategy for portfolio selection," International Review of Economics & Finance, Elsevier, volume 71, issue C, pages 936-942, DOI: 10.1016/j.iref.2020.07.010.
• Barrales-Ruiz, Jose & Arnim, Rudiger von, 2021, "Endogenous fluctuations in demand and distribution: An empirical investigation," Structural Change and Economic Dynamics, Elsevier, volume 58, issue C, pages 204-220, DOI: 10.1016/j.strueco.2021.05.005.
• Bernt P. Stigum, 2021, "Consumer Choice under Certainty and Uncertainty in Applied Econometrics," EERI Research Paper Series, Economics and Econometrics Research Institute (EERI), Brussels, number EERI RP 2021/08, Oct.
• Martin Vallejos, 2021, "La Dinámica de los Precios del Petróleo y Tipos de Cambio en Latinoamérica," Cuadernos de Investigación Económica Boliviana, Ministerio de Economía y Finanzas Públicas de Bolivia, volume 4, issue 1, pages 67-110, Junio.
• Taniya Ghosh & Sakshi Agarwal, 2021, "Do Machine Learning Models Hold the Key to Better Money Demand Forecasting?," International Symposia in Economic Theory and Econometrics, Emerald Group Publishing Limited, "Environmental, Social, and Governance Perspectives on Economic Development in Asia", DOI: 10.1108/S1571-03862021000029A017.
• Marcin Hernes & Adrianna Kozierkiewicz & Marcin Maleszka & Artur Rot & Agata Kozina & Karolina Matenczuk & Jakub Janus & Ewelina Wrobel, 2021, "Deep Learning for Repayment Prediction in Leasing Companies," European Research Studies Journal, European Research Studies Journal, volume 0, issue 2 - Part , pages 1134-1148.
• Bartosz Przysucha & Tomasz Rymarczyk & Dariusz Wojcik & Marcin Kowalski & Ryszard Bialek, 2021, "Management of Early Failure Detection of Production Process: The Case of the Clutch Shaft Alignment using LSTM Deep Learning Algorithm," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special 1, pages 189-197.
• Grzegorz Kłosowski & Monika Kulisz & Jerzy Lipski & Michal Maj & Ryszard Bialek, 2021, "The Use of Transfer Learning with Very Deep Convolutional Neural Network in Quality Management," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special 1, pages 253-263.
• Pawel Rymarczyk & Piotr Golabek & Sylwia Skrzypek - Ahmed & Magdalena Rzemieniak, 2021, "Profiling and Segmenting Clients with the Use of Machine Learning Algorithms," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special 1, pages 513-522.
• Damian Pliszczuk & Piotr Lesiak & Krzysztof Zuk & Tomasz Cieplak, 2021, "Forecasting Sales in the Supply Chain Based on the LSTM Network: The Case of Furniture Industry," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special 1, pages 627-636.
• Natalia Nehrebecka, 2021, "Internal Credit Risk Models and Digital Transformation: What to Prepare for? An Application to Poland," European Research Studies Journal, European Research Studies Journal, volume 0, issue Special 2, pages 719-736.
• Thomas R. Cook & Zach Modig & Nathan M. Palmer, 2021, "Explaining Machine Learning by Bootstrapping Partial Marginal Effects and Shapley Values," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 21-12, Nov, revised 06 Aug 2024, DOI: 10.18651/RWP2021-12.
• Michael Creel, 2021, "Inference Using Simulated Neural Moments," Econometrics, MDPI, volume 9, issue 4, pages 1-15, September.
• Simon Liebermann & Jung-Sup Um & YoungSeok Hwang & Stephan Schlüter, 2021, "Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts," Energies, MDPI, volume 14, issue 11, pages 1-21, May.
• Dimitris Korobilis & Kenichi Shimizu, 2021, "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers, Business School - Economics, University of Glasgow, number 2021_19, Nov.
• Fedor Zagumennov, 2021, "In-Firm Planning and Business Processes Management Using Deep Neural Networks," GATR Journals, Global Academy of Training and Research (GATR) Enterprise, number jber213, Dec, DOI: https://doi.org/10.35609/jber.2021..
• Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021, "Urban economics in a historical perspective: Recovering data with machine learning," PSE Working Papers, HAL, number halshs-03231786, May.
• Pierre-Philippe Combes & Laurent Gobillon & Yanos Zylberberg, 2021, "Urban economics in a historical perspective: Recovering data with machine learning," Working Papers, HAL, number halshs-03231786, May.
• Rajka Hrbić & Tomislav Grebenar, 2021, "Assessment of Readiness of Croatian Companies to Introduce I4.0 Technologies," Working Papers, The Croatian National Bank, Croatia, number 63, Mar.
• Ai Nur Bayinah & Muhammad Said & Munzier Suparta, 2021, "Social-Commercial Interconnection: Lessons From Bank Muamalat Indonesia & Baitulmaal Muamalat Affiliation," Journal of Islamic Monetary Economics and Finance, Bank Indonesia, volume 7, issue 2, pages 341-368, May, DOI: https://doi.org/10.21098/jimf.v7i2..
• Rogelio Ladrón de Guevara Cortés & Salvador Torra Porras & Enric Monte Moreno, 2021, "Comparison of Statistical Underlying Systematic Risk Factors and Betas Driving Returns on Equities," 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 16, issue TNEA, pages 1-25, Septiembr.
• Judith Jazmin Castro Pérez & José Eduardo Medina Reyes, 2021, "Fuzzy Portfolio Selection with Sugeno Type Fuzzy Neural Network: Investing 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 16, issue TNEA, pages 1-25, Septiembr.
• Ntigkaris Alexandros, 2021, "Cryptocurrency analysis: Benefits, dangers and price prediction using neural networks," Romanian Journal of Economics, Institute of National Economy, volume 52, issue 1(61), pages 05-17, June.
• Comola, Margherita & Inguaggiato, Carla & Mendola, Mariapia, 2021, "Learning about Farming: Innovation and Social Networks in a Resettled Community in Brazil," IZA Discussion Papers, IZA Network @ LISER, number 14092, Feb.
• Boller, Daniel & Lechner, Michael & Okasa, Gabriel, 2021, "The Effect of Sport in Online Dating: Evidence from Causal Machine Learning," IZA Discussion Papers, IZA Network @ LISER, number 14259, Apr.
• Combes, Pierre-Philippe & Gobillon, Laurent & Zylberberg, Yanos, 2021, "Urban Economics in a Historical Perspective: Recovering Data with Machine Learning," IZA Discussion Papers, IZA Network @ LISER, number 14392, May.
• Gimenez-Nadal, José Ignacio & Gracia-Lazaro, Carlos & Molina, José Alberto, 2021, "Bike-Sharing: Network Efficiency and Demand Profiles," IZA Discussion Papers, IZA Network @ LISER, number 14818, Oct.
• Jing Yuan & Yajing Dong & Weijie Zhai & Zongwu Cai, 2021, "Economic Policy Uncertainty: Cross-Country Linkages and Spillover Effects on Economic Development in Some Belt and Road Countries," WORKING PAPERS SERIES IN THEORETICAL AND APPLIED ECONOMICS, University of Kansas, Department of Economics, number 202110, Feb, revised Nov 2021.
• Raushan Kumar, 2021, "Predicting Wheat Futures Prices in India," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, volume 28, issue 1, pages 121-140, March, DOI: 10.1007/s10690-020-09320-6.
• Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou, 2021, "Gold Against the Machine," Computational Economics, Springer;Society for Computational Economics, volume 57, issue 1, pages 5-28, January, DOI: 10.1007/s10614-020-10019-z.
• Athanasios Tsadiras & Maria Pempetzoglou & Iosif Viktoratos, 2021, "Making Predictions of Global Warming Impacts Using a Semantic Web Tool that Simulates Fuzzy Cognitive Maps," Computational Economics, Springer;Society for Computational Economics, volume 58, issue 3, pages 715-745, October, DOI: 10.1007/s10614-020-10025-1.
• Hui Chen & Antoine Didisheim & Simon Scheidegger, 2021, "Deep Structural Estimation:With an Application to Option Pricing," Cahiers de Recherches Economiques du Département d'économie, Université de Lausanne, Faculté des HEC, Département d’économie, number 21.14, Feb.
• Timothy Holt & Mitsuru Igami & Simon Scheidegger, 2021, "Detecting Edgeworth Cycles," Cahiers de Recherches Economiques du Département d'économie, Université de Lausanne, Faculté des HEC, Département d’économie, number 21.16, Nov.
• Luis Eduardo Peñafiel Chang, 2021, "Economic, Political and Health Panorama of Latin America and the Caribbean at the Beginning of The COVID-19 Pandemic," Lecturas de Economía, Universidad de Antioquia, Departamento de Economía, issue 95, pages 11-44, July-Dece, DOI: 10.17533/udea.le.n95a344608.
• 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.