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

2020

• 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, Paradigm, 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, Paradigm, 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, Paradigm, 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, Paradigm, 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 Staff Working Paper series, Bank of England, number 784, Mar.
• Lyudmyla Маlyarets & Oleksandr Dorokhov & Vitaliya Koybichuk & Liudmyla Dorokhova, 2019, "Obtaining a Generalized Index of Bank Competitiveness Using a Fuzzy Approach," Journal of Central Banking Theory and Practice, Central bank of Montenegro, volume 8, issue 1, pages 163-182.
• Chuku Chuku & Anthony Simpasa & Jacob Oduor, 2019, "Intelligent forecasting of economic growth for developing economies," International Economics, CEPII research center, issue 159, pages 74-93.
• Paola Andrea Vaca González, 2019, "Cálculo y evaluación del riesgo operativo en entidades de salud a partir del enfoque de redes bayesianas," Ensayos de Economía, Universidad Nacional de Colombia Sede Medellín, number 18302, Jul, DOI: 10.15446/ede.v29n55.78411.
• Albanesi, Stefania & Vamossy, Domonkos, 2019, "Predicting Consumer Default: A Deep Learning Approach," CEPR Discussion Papers, Centre for Economic Policy Research, number 13914, Aug.
• Fernández-Villaverde, Jesús & Hurtado, Samuel & Nuño, Galo, 2019, "Financial Frictions and the Wealth Distribution," CEPR Discussion Papers, Centre for Economic Policy Research, number 14002, Sep.
• Mariapia Mendola & Mengesha Yayo Negasi, 2019, "Nutritional and Schooling Impact of a Cash Transfer Program in Ethiopia: A Retrospective Analysis of Childhood Experience," Development Working Papers, Centro Studi Luca d'Agliano, University of Milano, number 451, Jun.
• José Carlos Casas del Rosal & David E. Casas del Rosal & José María Caridad y Ocerin & Julia Núñez Tabales, 2019, "Mercado inmobiliario de españa: Una herramienta para el análisis de la oferta," Cuadernos de Economía - Spanish Journal of Economics and Finance, Asociación Cuadernos de Economía, volume 42, issue 120, pages 207-218, Diciembre.
• Jawwad Noor, 2019, "Intuitive Beliefs," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2216, Dec.
• Roncoroni, Alan & Battiston, Stefano & D'Errico, Marco & Hałaj, Grzegorz & Kok, Christoffer, 2019, "Interconnected banks and systemically important exposures," Working Paper Series, European Central Bank, number 2331, Nov.
• Richard Sarpong-Streetor & Rajalingam A/L Sokkalingam & Mahmod bin Othman & Dennis Ling Chuan Ching & Hamzah bin Sakidin, 2019, "A Hybrid Autoregressive Integrated Moving Average-phGMDH Model to Forecast Crude Oil Price," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, volume 9, issue 5, pages 135-141.
• Kolidakis, Stylianos & Botzoris, George & Profillidis, Vassilios & Lemonakis, Panagiotis, 2019, "Road traffic forecasting — A hybrid approach combining Artificial Neural Network with Singular Spectrum Analysis," Economic Analysis and Policy, Elsevier, volume 64, issue C, pages 159-171, DOI: 10.1016/j.eap.2019.08.002.
• Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Okhrin, Yarema, 2019, "Tail event driven networks of SIFIs," Journal of Econometrics, Elsevier, volume 208, issue 1, pages 282-298, DOI: 10.1016/j.jeconom.2018.09.016.
• Cheng, Fangzheng & Li, Tian & Wei, Yi-ming & Fan, Tijun, 2019, "The VEC-NAR model for short-term forecasting of oil prices," Energy Economics, Elsevier, volume 78, issue C, pages 656-667, DOI: 10.1016/j.eneco.2017.12.035.
• Beyca, Omer Faruk & Ervural, Beyzanur Cayir & Tatoglu, Ekrem & Ozuyar, Pinar Gokcin & Zaim, Selim, 2019, "Using machine learning tools for forecasting natural gas consumption in the province of Istanbul," Energy Economics, Elsevier, volume 80, issue C, pages 937-949, DOI: 10.1016/j.eneco.2019.03.006.
• Jasiński, Tomasz, 2019, "Modeling electricity consumption using nighttime light images and artificial neural networks," Energy, Elsevier, volume 179, issue C, pages 831-842, DOI: 10.1016/j.energy.2019.04.221.
• Huber, Martin & Imhof, David, 2019, "Machine learning with screens for detecting bid-rigging cartels," International Journal of Industrial Organization, Elsevier, volume 65, issue C, pages 277-301, DOI: 10.1016/j.ijindorg.2019.04.002.
• Chuku, Chuku & Simpasa, Anthony & Oduor, Jacob, 2019, "Intelligent forecasting of economic growth for developing economies," International Economics, Elsevier, volume 159, issue C, pages 74-93, DOI: 10.1016/j.inteco.2019.06.001.
• Szafranek, Karol, 2019, "Bagged neural networks for forecasting Polish (low) inflation," International Journal of Forecasting, Elsevier, volume 35, issue 3, pages 1042-1059, DOI: 10.1016/j.ijforecast.2019.04.007.
• Arifovic, Jasmina & Yıldızoğlu, Murat, 2019, "Learning the Ramsey outcome in a Kydland & Prescott economy," Journal of Economic Behavior & Organization, Elsevier, volume 157, issue C, pages 191-208, DOI: 10.1016/j.jebo.2017.11.001.
• Adcock, Robert & Gradojevic, Nikola, 2019, "Non-fundamental, non-parametric Bitcoin forecasting," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 531, issue C, DOI: 10.1016/j.physa.2019.121727.
• Sommervoll, Åvald & Sommervoll, Dag Einar, 2019, "Learning from man or machine: Spatial fixed effects in urban econometrics," Regional Science and Urban Economics, Elsevier, volume 77, issue C, pages 239-252, DOI: 10.1016/j.regsciurbeco.2019.04.005.
• Tiwari, Aviral Kumar & Gupta, Rangan, 2019, "Chaos in G7 stock markets using over one century of data: A note," Research in International Business and Finance, Elsevier, volume 47, issue C, pages 304-310, DOI: 10.1016/j.ribaf.2018.08.005.
• Tiwari, Aviral Kumar & Gupta, Rangan, 2019, "Reprint of: Chaos in G7 stock markets using over one century of data: A note," Research in International Business and Finance, Elsevier, volume 49, issue C, pages 315-321, DOI: 10.1016/j.ribaf.2019.05.002.
• Yan Pidvysotskyi, 2019, "Problems of Assessing Credit Risks of Structured Financial Instruments," European scientific journal of Economic and Financial innovation, "European Association of Economists", volume 1, issue 3, pages 62-69, April, DOI: 10.32750/2019-0105.
• Imaduddin Sahabat & Tumpak Silalahi & Ratih Indrastuti & Marizsa Herlina, 2019, "The interbank payment network and financial system stability," Studies in Economics and Finance, Emerald Group Publishing Limited, volume 37, issue 1, pages 1-17, September, DOI: 10.1108/SEF-10-2018-0310.
• Dejan Zivkov & Slavica Manic & Jasmina Duraskovic & Jelena Kovacevic, 2019, "Bidirectional Nexus between Inflation and Inflation Uncertainty in the Asian Emerging Markets – The GARCH-in-Mean Approach," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, volume 69, issue 6, pages 580-599, December.
• Andrey A. Kozlov & Andrey V. Vlasov, 2019, "Cryptoeconomics: Pilot Study on Investments in ICO Startups Using Neural Networks," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 1, pages 76-87, February, DOI: 10.31107/2075-1990-2019-1-76-87.
• Charlie Joyez, 2019, "Alignment of Multinational Firms along Global Value Chains: A Network-based Perspective," GREDEG Working Papers, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France, number 2019-05, Feb.
• Miriam Steurer & Robert Hill, 2019, "Metrics for Evaluating the Performance of Automated Valuation Models," Graz Economics Papers, University of Graz, Department of Economics, number 2019-02, Feb.
• Roman Matkovskyy & Taoufik Bouraoui, 2019, "Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model," Post-Print, HAL, number hal-02155402, Jun, DOI: 10.1007/s40953-018-0133-8.
• Jasmina Arifovic & Murat Yildizoglu, 2019, "Learning the Ramsey Outcome in a Kydland & Prescott Economy," Post-Print, HAL, number hal-03428629, Jan, DOI: 10.2139/ssrn.2487941.
• Steffen Q. Mueller & Patrick Ring & Maria Schmidt, 2019, "Forecasting economic decisions under risk: The predictive importance of choice-process data," Working Papers, Chair for Economic Policy, University of Hamburg, number 066, Jan.
• Grodecka, Anna & Hull, Isaiah, 2019, "The Impact of Local Taxes and Public Services on Property Values," Working Paper Series, Sveriges Riksbank (Central Bank of Sweden), number 374, Apr.
• Shahid Anjum & Naveeda Qaseem, 2019, "Big Data Algorithms And Prediction: Bingos And Risky Zones In Sharia Stock Market Index," Journal of Islamic Monetary Economics and Finance, Bank Indonesia, volume 5, issue 3, pages 475-490, November, DOI: https://doi.org/10.21098/jimf.v5i3..
• Vu Nhu Nguyet & Do Duc Trung, 2019, "Optimization Milling Process When Machining C45 Steel by Ball Nose Mill for Minimum Tool Wear using Taguchi Method," International Journal of Scientific Research in Science, Engineering and Technology, International Journal of Scientific Research in Science, Engineering and Technology, volume 6, issue 1, pages 476-479, February, DOI: 10.32628/IJSRSET196188.
• Şahap KAVCIOĞLU, 2019, "Kurumsal Kredi Skorlamasında Klasik Yöntemlerle Yapay Sinir Ağı Karşılaştırması," Istanbul Journal of Economics-Istanbul Iktisat Dergisi, Istanbul University, Faculty of Economics, volume 69, issue 2, pages 207-246, December, DOI: 10.26650/ISTJECON2019-0021.
• Boriss Siliverstovs & Daniel Wochner, 2019, "Recessions as Breadwinner for Forecasters State-Dependent Evaluation of Predictive Ability: Evidence from Big Macroeconomic US Data," KOF Working papers, KOF Swiss Economic Institute, ETH Zurich, number 19-463, Oct, DOI: 10.3929/ethz-b-000374306.
• Nazaraghaei, Mehdi & Ghiasi, Hosein & Asgharkhah Chafi, Mohammad, 2019, "Classification of Customer’s Credit Risk Using Ensemble learning (Case study: Sepah Bank)," Journal of Monetary and Banking Research (فصلنامه پژوهش‌های پولی-بانکی), Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, volume 12, issue 39, pages 166-129, May.
• Stefania Albanesi & Domonkos F. Vamossy, 2019, "Predicting Consumer Default: A Deep Learning Approach," NBER Working Papers, National Bureau of Economic Research, Inc, number 26165, Aug.
• Jesús Fernández-Villaverde & Samuel Hurtado & Galo Nuño, 2019, "Financial Frictions and the Wealth Distribution," NBER Working Papers, National Bureau of Economic Research, Inc, number 26302, Sep.
• Kireyev, A., 2019, "A Network Model of Multilateral Equilibrium Exchange Rates," Journal of the New Economic Association, New Economic Association, volume 41, issue 1, pages 12-33.
• Arnaud Pincet & Shu Okabe & Martin Pawelczyk, 2019, "Linking Aid to the Sustainable Development Goals – a machine learning approach," OECD Development Co-operation Working Papers, OECD Publishing, number 52, Feb, DOI: 10.1787/4bdaeb8c-en.
• Jesus Fernandez-Villaverde & Samuel Hurtado & Galo Nuno, 2019, "Financial Frictions and the Wealth Distribution," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, number 19-015, Sep.
• Jaromir Vrbka & Elvira Nica & Ivana Podhorska, 2019, "The application of Kohonen networks for identification of leaders in the trade sector in Czechia," Equilibrium. Quarterly Journal of Economics and Economic Policy, Institute of Economic Research, volume 14, issue 4, pages 739-761, December, DOI: 10.24136/eq.2019.034.
• Fajar, Muhammad, 2019, "An application of hybrid forecasting singular spectrum analysis – extreme learning machine method in foreign tourists forecasting," MPRA Paper, University Library of Munich, Germany, number 105044, Oct, revised 31 Oct 2019.
• Hossain, Md. Mobarak & Chowdhury, Md Niaz Murshed, 2019, "Econometric Ways to Estimate the Age and Price of Abalone," MPRA Paper, University Library of Munich, Germany, number 91210, Jan.
• Hollenbeck, Brett & Taylor, Wayne, 2019, "Leveraging Loyalty Programs Using Competitor Based Targeting," MPRA Paper, University Library of Munich, Germany, number 92900.
• Brummelhuis, Raymond & Luo, Zhongmin, 2019, "Bank Net Interest Margin Forecasting and Capital Adequacy Stress Testing by Machine Learning Techniques," MPRA Paper, University Library of Munich, Germany, number 94779, Mar.
• Bucci, Andrea, 2019, "Cholesky-ANN models for predicting multivariate realized volatility," MPRA Paper, University Library of Munich, Germany, number 95137, Jul.
• Bucci, Andrea, 2019, "Realized Volatility Forecasting with Neural Networks," MPRA Paper, University Library of Munich, Germany, number 95443, Aug.
• Zolnikov, Pavel & Zubov, Maxim & Nikitinsky, Nikita & Makarov, Ilya, 2019, "Efficient Algorithms for Constructing Multiplex Networks Embedding," MPRA Paper, University Library of Munich, Germany, number 97310, Sep, revised 23 Sep 2019.
• Benkovich, Nikita & Dedenok, Roman & Golubev, Dmitry, 2019, "Deep Quarantine for Suspicious Mail," MPRA Paper, University Library of Munich, Germany, number 97311, Sep, revised 23 Sep 2019.
• Kadyrov, Timur & Ignatov, Dmitry I., 2019, "Attribution of Customers’ Actions Based on Machine Learning Approach," MPRA Paper, University Library of Munich, Germany, number 97312, Sep, revised 23 Sep 2019.
• Osipov, Vasiliy & Zhukova, Nataly & Miloserdov, Dmitriy, 2019, "Neural Network Associative Forecasting of Demand for Goods," MPRA Paper, University Library of Munich, Germany, number 97314, Sep, revised 23 Sep 2019.
• Sumi, P. Sobana & Delhibabu, Radhakrishnan, 2019, "Glioblastoma Multiforme Classification On High Resolution Histology Image Using Deep Spatial Fusion Network," MPRA Paper, University Library of Munich, Germany, number 97315, Sep, revised 23 Sep 2019.
• Milan Fičura, 2019, "Forecasting Foreign Exchange Rate Movements with k-Nearest-Neighbour, Ridge Regression and Feed-Forward Neural Networks," FFA Working Papers, Prague University of Economics and Business, number 1.001, Nov, revised 24 Nov 2019.
• Thomas Cook, 2019, "Macroeconomic Indicator Forecasting with Deep Neural Networks," 2019 Meeting Papers, Society for Economic Dynamics, number 402.
• Periklis Gogas & Theophilos Papadimitriou & Vasilios Plakandaras & Rangan Gupta, 2019, "The Informational Content of the Term-Spread in Forecasting the U.S. Inflation Rate: A Nonlinear Approach," DUTH Research Papers in Economics, Democritus University of Thrace, Department of Economics, number 3-2016, May.
• Rolando Gonzales & Jonathan Wareham, 2019, "Analysing the impact of a business intelligence system and new conceptualizations of system use," Journal of Economics, Finance and Administrative Science, Universidad ESAN, volume 24, issue 48, pages 345-368.
• Yogesh Malhotra, 2019, "Ai Augmentation For Large-Scale Global Systemic And Cyber Risk Management Projects: Model Risk Management For Minimizing The Downside Risks Of Ai And Machine Learning," Journal of Financial Transformation, Capco Institute, volume 49, pages 94-99.
• José Carlos Preciado Carrillo, 2019, "Redes neuronales artificiales, una aplicación al mercado inmobiliario habitacional de segmento medio de la ciudad de Morelia Michoacán, México
  [Künstliche neuronale Netze – eine Anwendung auf den Wohnimmobilienmarkt im mittleren Preissegment der S," Revista Nicolaita de Estudios Económicos, Universidad Michoacana de San Nicolás de Hidalgo, Instituto de Investigaciones Económicas y Empresariales, volume 0, issue 1, pages 49-68, January.
• Gheorghe RUXANDA & Sorin OPINCARIU & Stefan IONESCU, 2019, "Modelling Non-Stationary Financial Time Series with Input Warped Student T-Processes," Journal for Economic Forecasting, Institute for Economic Forecasting, volume 0, issue 3, pages 51-61, September.
• Elda Xhumari & Julian Fejzaj, 2019, "Usage of artificial neural networks in data classification," Proceedings of International Academic Conferences, International Institute of Social and Economic Sciences, number 9211565, Jul.
• Fernando Fernandes Neto & Claudio Garcia, Rodrigo de Losso da Silveira Bueno, Pedro Delano Cavalcanti, Alemayehu Solomon Admas, 2019, "Deep Haar Scattering Networks in Unidimensional Pattern Recognition Problems," Working Papers, Department of Economics, University of São Paulo (FEA-USP), number 2019_16, May.
• Roman Matkovskyy & Taoufik Bouraoui, 2019, "Application of Neural Networks to Short Time Series Composite Indexes: Evidence from the Nonlinear Autoregressive with Exogenous Inputs (NARX) Model," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), volume 17, issue 2, pages 433-446, June, DOI: 10.1007/s40953-018-0133-8.
• Daria Maltseva & Vladimir Batagelj, 2019, "Social network analysis as a field of invasions: bibliographic approach to study SNA development," Scientometrics, Springer;Akadémiai Kiadó, volume 121, issue 2, pages 1085-1128, November, DOI: 10.1007/s11192-019-03193-x.
• Ugis Sarma & Ugis Sarma & Girts Karnitis & Janis Zuters & Edvins Karnitis, 2019, "District heating networks: enhancement of the efficiency," Insights into Regional Development, VsI Entrepreneurship and Sustainability Center, volume 1, issue 3, pages 200-213, September, DOI: 10.9770/ird.2019.1.3(2).
• Lisa-Cheree Martin, 2019, "Machine Learning vs Traditional Forecasting Methods: An Application to South African GDP," Working Papers, Stellenbosch University, Department of Economics, number 12/2019.
• Dungey, Mardi & Islam, Raisul & Volkov, Vladimir, 2019, "Crisis transmission: visualizing vulnerability," Working Papers, University of Tasmania, Tasmanian School of Business and Economics, number 2019-07.
• Lily Shen & Stephen L. Ross, 2019, "Information Value of Property Description: A Machine Learning Approach," Working papers, University of Connecticut, Department of Economics, number 2019-20, Dec, revised Sep 2020.
• Bo Cowgill, 2019, "Bias and Productivity in Humans and Machines," Upjohn Working Papers, W.E. Upjohn Institute for Employment Research, number 19-309, Aug.
• Nahapetyan Yervand, 2019, "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Paradigm, volume 6, issue 53, pages 286-303, January, DOI: 10.2478/ceej-2019-0018.
• Nahapetyan Yervand, 2019, "The benefits of the Velvet Revolution in Armenia: Estimation of the short-term economic gains using deep neural networks," Central European Economic Journal, Paradigm, volume 6, issue 53, pages 286-303, January, DOI: 10.2478/ceej-2019-0018.
• Maryna Zenkova & Robert Ślepaczuk, 2019, "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2019-02, Jan.
• Michał Latoszek & Robert Ślepaczuk, 2019, "Does the inclusion of exposure to volatility into diversified portfolio improve the investment results? Portfolio construction from the perspective of a Polish investor," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2019-14, Aug.
• Kamil Korzeń & Robert Ślepaczuk, 2019, "Hybrid Investment Strategy Based on Momentum and Macroeconomic Approach," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2019-17, Oct.
• Katarzyna Maciejowska & Rafal Weron, 2019, "Electricity price forecasting," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number HSC/19/01, Feb.
• Guodong Guo & Brad R. Humphreys & Mohammad Iqbal Nouyed & Yang Zhou, 2019, "Attractive or Aggressive? A Face Recognition and Machine Learning Approach for Estimating Returns to Visual Appearance," Working Papers, Department of Economics, West Virginia University, number 19-01, Aug.
• Tölö, Eero, 2019, "Predicting systemic financial crises with recurrent neural networks," Bank of Finland Research Discussion Papers, Bank of Finland, number 14/2019.
• Bilal Zorić, Alisa, 2019, "Predicting Students’ Success Using Neural Networks," Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference (2019), Rovinj, Croatia, IRENET - Society for Advancing Innovation and Research in Economy, Zagreb, "Proceedings of the ENTRENOVA - ENTerprise REsearch InNOVAtion Conference, Rovinj, Croatia, 12-14 September 2019".
• Hinterlang, Natascha, 2019, "Predicting Monetary Policy Using Artificial Neural Networks," VfS Annual Conference 2019 (Leipzig): 30 Years after the Fall of the Berlin Wall - Democracy and Market Economy, Verein für Socialpolitik / German Economic Association, number 203503.

2018

• Shigeyuki Hamori & Takahiro Kume, 2018, "Artificial Intelligence And Economic Growth," Advances in Decision Sciences, Asia University, Taiwan, volume 22, issue 1, pages 256-278, December.
• Andrea Magda NAGY, 2018, "International Scientific Cooperation Networks of Top Universities in the CEE Region," Journal of Emerging Trends in Marketing and Management, The Bucharest University of Economic Studies, volume 1, issue 1, pages 45-54, November.
• Emrah Gulay, 2018, "Comparing Simple Forecasting Methods and Complex Methods: A Frame of Forecasting Competition," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, volume 65, issue 2, pages 159-169, June.
• Aytuğ Onan, 2018, "A Clustering Based Classifier Ensemble Approach to Corporate Bankruptcy Prediction," Alphanumeric Journal, Bahadir Fatih Yildirim, volume 6, issue 2, pages 365-376, December, DOI: http://dx.doi.org/10.17093/alphanum.
• Oscar Claveria & Enric Monte & Salvador Torra, 2018, "“A regional perspective on the accuracy of machine learning forecasts of tourism demand based on data characteristics”," AQR Working Papers, University of Barcelona, Regional Quantitative Analysis Group, number 201802, Apr, revised Apr 2018.
• Raymond Brummelhuis & Zhongmin Luo, 2018, "Arbitrage Opportunities in CDS Term Structure: Theory and Implications for OTC Derivatives," Papers, arXiv.org, number 1811.08038, Nov, revised Dec 2018.
• Renáta Myšková & Petr Hájek & Vladimír Olej, 2018, "Predicting Abnormal Stock Return Volatility Using Textual Analysis of News ? A Meta-Learning Approach," The Audit Financiar journal, Chamber of Financial Auditors of Romania, volume 20, issue 47, pages 185-185, February.
• Vyacheslav Dzhedzhula & Iryna Yepifanova, 2018, "Use Of Apparatus Of Hybrid Neural Networks For Evaluation Of An Intellectual Component Of The Energy-Saving Policy Of The Enterprise," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", volume 4, issue 1, DOI: 10.30525/2256-0742/2018-4-1-126-130.
• Oksana Omelchenko & Oleksandr Dorokhov & Oleg Kolodiziev & Liudmyla Dorokhova, 2018, "Fuzzy Modeling of the Creditworthiness Assessments of Bank’s Potential Borrowers in Ukraine," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 4, pages 100-125.
• Carlos León & Fabio Ortega, 2018, "Nowcasting economic activity with electronic payments data: A predictive modeling approach," Borradores de Economia, Banco de la Republica de Colombia, number 1037, Feb, DOI: 10.32468/be.1037.
• Natalia Lamberova & Konstantin Sonin, 2018, "Economic transition and the rise of alternative institutions : Political connections in Putin's Russia," The Economics of Transition, The European Bank for Reconstruction and Development, volume 26, issue 4, pages 615-648, October, DOI: 10.1111/ecot.12167.
• Kim Ristolainen, 2018, "Predicting Banking Crises with Artificial Neural Networks: The Role of Nonlinearity and Heterogeneity," Scandinavian Journal of Economics, Wiley Blackwell, volume 120, issue 1, pages 31-62, January, DOI: 10.1111/sjoe.12216.
• José Alberto Molina & David Iñiguez & Gonzalo Ruiz & Alfonso Tarancón, 2018, "The Nobel Prize in Economics: individual or collective merits?," Boston College Working Papers in Economics, Boston College Department of Economics, number 966, Oct.
• Nobuhiro Abe & Kimiaki Shinozaki, 2018, "Compilation of Experimental Price Indices Using Big Data and Machine Learning:A Comparative Analysis and Validity Verification of Quality Adjustments," Bank of Japan Working Paper Series, Bank of Japan, number 18-E-13, Aug.
• Kunčič Aljaž, 2018, "SDG-Specific Country Groups: Subregional Analysis of the Arab Region," Review of Middle East Economics and Finance, De Gruyter, volume 14, issue 2, pages 1-22, August, DOI: 10.1515/rmeef-2017-0020.
• Johannes Berens & Simon Oster & Kerstin Schneider & Julian Burghoff, 2018, "Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," Schumpeter Discussion Papers, Universitätsbibliothek Wuppertal, University Library, number sdp18006, Jul.
• Johannes Berens & Kerstin Schneider & Simon Görtz & Simon Oster & Julian Burghoff, 2018, "Early Detection of Students at Risk – Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," CESifo Working Paper Series, CESifo, number 7259.
• Feng Zhou & Zhang Qun & Didier Sornette & Liu Jiang, 2018, "Cascading Logistic Regression Onto Gradient Boosted Decision Trees to Predict Stock Market Changes Using Technical Analysis," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 18-50, Jul, revised Aug 2018.
• Michael Mayer & Steven C. Bourassa & Martin Hoesli & Donato Flavio Scognamiglio, 2018, "Estimation and Updating Methods for Hedonic Valuation," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 18-76, Dec.
• Carlos León & Fabio Ortega, 2018, "Nowcasting Economic Activity with Electronic Payments Data: A Predictive Modeling Approach," Revista de Economía del Rosario, Universidad del Rosario, volume 21, issue 2, pages 381-407.
• Lamberova, Natalia & Sonin, Konstantin, 2018, "Economic Transition and the Rise of Alternative Institutions: Political Connections in Putin's Russia," CEPR Discussion Papers, Centre for Economic Policy Research, number 13177, Sep.
• Hall, S.G. & Henry, S.G.B., 2018, "Macro Modelling at the NIESR: Its Recent History," National Institute Economic Review, National Institute of Economic and Social Research, volume 246, issue , pages 15-23, November.
• Christoph Engel & Alexandra Fedorets & Olga Gorelkina, 2018, "How Do Households Allocate Risk?," SOEPpapers on Multidisciplinary Panel Data Research, DIW Berlin, The German Socio-Economic Panel (SOEP), number 1000.
• Jorge Barrientos Marin & Elkin Tabares Orozco & Esteban Velilla, 2018, "Forecasting electricity price in Colombia: A comparison between Neural Network, ARMA process and Hybrid Models," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, volume 8, issue 3, pages 97-106.
• Dellas, Harris & Gibson, Heather D. & Hall, Stephen G. & Tavlas, George S., 2018, "The macroeconomic and fiscal implications of inflation forecast errors," Journal of Economic Dynamics and Control, Elsevier, volume 93, issue C, pages 203-217, DOI: 10.1016/j.jedc.2018.01.030.
• Di Gangi, Domenico & Lillo, Fabrizio & Pirino, Davide, 2018, "Assessing systemic risk due to fire sales spillover through maximum entropy network reconstruction," Journal of Economic Dynamics and Control, Elsevier, volume 94, issue C, pages 117-141, DOI: 10.1016/j.jedc.2018.07.001.
• Zhu, Bangzhu & Ye, Shunxin & Wang, Ping & He, Kaijian & Zhang, Tao & Wei, Yi-Ming, 2018, "A novel multiscale nonlinear ensemble leaning paradigm for carbon price forecasting," Energy Economics, Elsevier, volume 70, issue C, pages 143-157, DOI: 10.1016/j.eneco.2017.12.030.
• Wang, Minggang & Tian, Lixin & Zhou, Peng, 2018, "A novel approach for oil price forecasting based on data fluctuation network," Energy Economics, Elsevier, volume 71, issue C, pages 201-212, DOI: 10.1016/j.eneco.2018.02.021.
• He, Kaijian & Chen, Yanhui & Tso, Geoffrey K.F., 2018, "Forecasting exchange rate using Variational Mode Decomposition and entropy theory," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 510, issue C, pages 15-25, DOI: 10.1016/j.physa.2018.05.135.
• Huber, Martin & Imhof, David, 2018, "Machine Learning with Screens for Detecting Bid-Rigging Cartels," FSES Working Papers, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland, number 494, Mar.
• Sommervoll, Dag Einar & Sommervoll, Åvald, 2018, "Learning from man or machine: Spatial aggregation and house price prediction," CLTS Working Papers, Norwegian University of Life Sciences, Centre for Land Tenure Studies, number 4/18, Apr, revised 16 Oct 2019.
• KANAZAWA, Nobuyuki & 金澤, 伸幸, 2018, "Radial Basis Functions Neural Networks for Nonlinear Time Series Analysis and Time-Varying Effects of Supply Shocks," Discussion paper series, Hitotsubashi Institute for Advanced Study, Hitotsubashi University, number HIAS-E-64, Mar.
• Zárate, Héctor & Zapata-Sanabria, Daniel R., 2018, "Forecasting Inflation Expectations from the CESifo World Economic Survey: An Empirical Application in Inflation Targeting," IDB Publications (Working Papers), Inter-American Development Bank, number 9053, Jul, DOI: http://dx.doi.org/10.18235/0001264.
• Saiful Anwar & A.M Hasan Ali, 2018, "ANNs-BASED Early Warning System for Indonesian Islamic Banks," Bulletin of Monetary Economics and Banking, Bank Indonesia, volume 20, issue 3, pages 325-342, January, DOI: https://doi.org/10.21098/bemp.v20i3.
• Luis Manuel León Anaya & Víctor Manuel Landassuri Moreno & Héctor Rafael Orozco Aguirre & Maricela Quintana López, 2018, "Predicción del IPC mexicano combinando modelos econométricos e inteligencia artificial," 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 13, issue 4, pages 603-629, Octubre-D.
• Christoph Engel & Alexandra Fedorets & Olga Gorelkina, 2018, "How Do Households Allocate Risk?," Working Papers, University of Liverpool, Department of Economics, number 20186, Nov.
• Christoph Engel & Alexandra Fedorets & Olga Gorelkina, 2018, "Risk Taking in the Household: Strategic Behavior, Social Preferences, or Interdependent Preferences?," Discussion Paper Series of the Max Planck Institute for Behavioral Economics, Max Planck Institute for Behavioral Economics, number 2018_14, Nov, revised Feb 2020.
• Marina Azzimonti & Marcos Fernandes, 2018, "Social Media Networks, Fake News, and Polarization," NBER Working Papers, National Bureau of Economic Research, Inc, number 24462, Mar.
• Shihao Gu & Bryan Kelly & Dacheng Xiu, 2018, "Empirical Asset Pricing via Machine Learning," NBER Working Papers, National Bureau of Economic Research, Inc, number 25398, Dec.
• Millán Solarte, Julio César & Caicedo Cerezo, Edinson, 2018, "Modelos para otorgamiento y seguimiento en la gestión del riesgo de crédito || Models for Granting and Tracking in Credit Risk Management," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, volume 25, issue 1, pages 23-41, Junio.
• Brummelhuis, Raymond & Luo, Zhongmin, 2018, "Arbitrage Opportunities in CDS Term Structure: Theory and Implications for OTC Derivatives," MPRA Paper, University Library of Munich, Germany, number 94778, Nov.
• Matúš Mihalovič, 2018, "Využitie skóringových modelov pri predikcii defaultu ekonomických subjektov v Slovenskej republike
  [Applicability of Scoring Models in Firms' Default Prediction. The Case of Slovakia]," Politická ekonomie, Prague University of Economics and Business, volume 2018, issue 6, pages 689-708, DOI: 10.18267/j.polek.1226.
• S.G. Hall & S.G.B. Henry, 2018, "Macro Modelling at the NIESR: Its Recent History," National Institute Economic Review, National Institute of Economic and Social Research, volume 246, issue 1, pages 15-23, November.
• Muhammad Nadim Hanif & Khurrum S. Mughal & Javed Iqbal, 2018, "A Thick ANN Model for Forecasting Inflation," SBP Working Paper Series, State Bank of Pakistan, Research Department, number 99, Oct.
• Somsri Banditvilai & Siriluck Anansatitzin, 2018, "Comparative Study of Three Time Series Methods in Forecasting Dengue Hemorrhagic Fever Incidence in Thailand," Proceedings of International Academic Conferences, International Institute of Social and Economic Sciences, number 6409199, Jun.
• Lei Zhang, 2018, "Artificial Neural Network Based Chaotic Generator Design for The Prediction of Financial Time Series," Proceedings of International Academic Conferences, International Institute of Social and Economic Sciences, number 6409417, Jun.
• Díaz, Héctor & Sosa, Miriam & Ortiz, Edgar, 2018, "Inclusión financiera y ahorro en México: un análisis logístico binario y de redes neuronales artificiales/Financial inclusion and savings in Mexico: a binary logistic and artificial neural networks analysis," Estocástica: finanzas y riesgo, Departamento de Administración de la Universidad Autónoma Metropolitana Unidad Azcapotzalco, volume 8, issue 1, pages 53-84, enero-jun.
• Gordon H. Dash & Nina Kajiji & Domenic Vonella, 2018, "The role of supervised learning in the decision process to fair trade US municipal debt," EURO Journal on Decision Processes, Springer;EURO - The Association of European Operational Research Societies, volume 6, issue 1, pages 139-168, June, DOI: 10.1007/s40070-018-0079-2.
• Patrick Röhm, 2018, "Exploring the landscape of corporate venture capital: a systematic review of the entrepreneurial and finance literature," Management Review Quarterly, Springer, volume 68, issue 3, pages 279-319, August, DOI: 10.1007/s11301-018-0140-z.
• Félix J. López-Iturriaga & Iván Pastor Sanz, 2018, "Predicting Public Corruption with Neural Networks: An Analysis of Spanish Provinces," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, volume 140, issue 3, pages 975-998, December, DOI: 10.1007/s11205-017-1802-2.
• Anton Kolotilin & Valentyn Panchenko, 2018, "Estimation of a Scale-Free Network Formation Model," Discussion Papers, School of Economics, The University of New South Wales, number 2018-10, Jun.
• Carlo Fezzi & Luca Mosetti, 2018, "Size matters: Estimation sample length and electricity price forecasting accuracy," DEM Working Papers, Department of Economics and Management, number 2018/10.
• Gulay Emrah, 2018, "Comparing Simple Forecasting Methods and Complex Methods: A Frame of Forecasting Competition," Scientific Annals of Economics and Business, Paradigm, volume 65, issue 2, pages 159-169, June, DOI: 10.2478/saeb-2018-0010.
• Ślepaczuk Robert & Zenkova Maryna, 2018, "Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market," Central European Economic Journal, Paradigm, volume 5, issue 52, pages 186-205, January, DOI: 10.1515/ceej-2018-0022.
• Nehrebecka Natalia, 2018, "Predicting the Default Risk of Companies. Comparison of Credit Scoring Models: Logit Vs Support Vector Machines," Econometrics. Advances in Applied Data Analysis, Paradigm, volume 22, issue 2, pages 54-73, June, DOI: 10.15611/eada.2018.2.05.
• Nehrebecka Natalia, 2018, "An Evaluation of the Discriminatory Power of Selected Polish Bankruptcy Prediction Models As Part of the Validation Process," Financial Sciences. Nauki o Finansach, Paradigm, volume 23, issue 4, pages 63-88, December, DOI: 10.15611/fins.2018.4.05.
• Kaczmarczyk Paweł, 2018, "Neural Network Application to Support Regression Model in Forecasting Single-Sectional Demand for Telecommunications Services," Folia Oeconomica Stetinensia, Paradigm, volume 18, issue 2, pages 159-177, December, DOI: 10.2478/foli-2018-0025.