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

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.
• 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, Sciendo, 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, Sciendo, 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.
• 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.
• 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.
• 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.
• 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.
• Sonin, Konstantin & Lamberova, Natalia, 2018, "Economic Transition and the Rise of Alternative Institutions: Political Connections in Putin's Russia," CEPR Discussion Papers, C.E.P.R. Discussion Papers, 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, Econjournals, 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, Sciendo, 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, Sciendo, 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, Sciendo, 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, Sciendo, 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, Sciendo, volume 18, issue 2, pages 159-177, December, DOI: 10.2478/foli-2018-0025.
• Przemysław Ryś & Robert Ślepaczuk, 2018, "Machine learning in algorithmic trading strategy optimization - implementation and efficiency," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2018-25.
• Katarzyna Hubicka & Grzegorz Marcjasz & Rafal Weron, 2018, "A note on averaging day-ahead electricity price forecasts across calibration windows," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number HSC/18/03, Jul.
• Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2018, "Probabilistic electricity price forecasting with NARX networks: Combine point or probabilistic forecasts?," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number HSC/18/05, Jul.
• Rafal Weron & Florian Ziel, 2018, "Electricity price forecasting," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number HSC/18/08, Sep.
• Martinho, Vítor João Pereira Domingues, 2018, "Ranking the socioeconomic and environmental framework of European Union farms: A network analysis," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 173285.
• Steinkraus, Arne, 2018, "Rethinking Policy Evaluation – Do Simple Neural Nets Bear Comparison with Synthetic Control Method?," EconStor Preprints, ZBW - Leibniz Information Centre for Economics, number 177390.
• Jahn, Malte, 2018, "Artificial neural network regression models: Predicting GDP growth," HWWI Research Papers, Hamburg Institute of International Economics (HWWI), number 185.
• Härdle, Wolfgang Karl & Chen, Shi & Liang, Chong & Schienle, Melanie, 2018, "Time-varying Limit Order Book Networks," IRTG 1792 Discussion Papers, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series", number 2018-016.
• Schneider, Kerstin & Berens, Johannes & Oster, Simon & Burghoff, Julian, 2018, "Early Detection of Students at Risk - Predicting Student Dropouts Using Administrative Student Data and Machine Learning Methods," VfS Annual Conference 2018 (Freiburg, Breisgau): Digital Economy, Verein für Socialpolitik / German Economic Association, number 181544.

2017

• Miroslav Karahuta & Peter Gallo & Daniela Matušíková & Anna Šenková & Kristína Šambronská, 2017, "Forecast Of Using Neural Networks In The Tourism Sector," CBU International Conference Proceedings, ISE Research Institute, volume 5, issue 0, pages 218-223, September, DOI: 10.12955/cbup.v5.928.
• Nimet Melis Esenyel & Melda Akın, 2017, "Comparing Accuracy Performance of ELM, ARMA and ARMA-GARCH Model In Predicting Exchange Rate Return," Alphanumeric Journal, Bahadir Fatih Yildirim, volume 5, issue 1, pages 1-14, June, DOI: http://dx.doi.org/10.17093/alphanum.
• Ufuk Çelik & Çağatay Başarır, 2017, "The Prediction of Precious Metal Prices via Artificial Neural Network by Using RapidMiner," Alphanumeric Journal, Bahadir Fatih Yildirim, volume 5, issue 1, pages 45-54, June, DOI: http://dx.doi.org/10.17093/alphanum.
• Yusuf Kuvvetli, 2017, "Returned Product Acquisition Pricing by Adaptive Neuro Fuzzy Inference System," Alphanumeric Journal, Bahadir Fatih Yildirim, volume 5, issue 2, pages 207-214, October, DOI: http://dx.doi.org/10.17093/alphanum.
• Engin Taş, 2017, "Classification of Gene Samples Using Pair-Wise Support Vector Machines," Alphanumeric Journal, Bahadir Fatih Yildirim, volume 5, issue 2, pages 283-292, November, DOI: http://dx.doi.org/10.17093/alphanum.
• Raymond Brummelhuis & Zhongmin Luo, 2017, "CDS Rate Construction Methods by Machine Learning Techniques," Papers, arXiv.org, number 1705.06899, May.
• Inna Strelchenko, 2017, "Modelling Of Scenarios Of The Crisis Phenomena Transfer Among Financial Markets," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", volume 3, issue 2, DOI: 10.30525/2256-0742/2017-3-2-136-140.
• Aleksey Mints, 2017, "Classification Of Tasks Of Data Mining And Data Processing In The Economy," Baltic Journal of Economic Studies, Publishing house "Baltija Publishing", volume 3, issue 3, DOI: 10.30525/2256-0742/2017-3-3-47-52.
• Mehmet OZCALICI, 2017, "Market Segmentation with Self-Organizing Maps in Banking Industry," Journal of BRSA Banking and Financial Markets, Banking Regulation and Supervision Agency, volume 11, issue 2, pages 9-30.
• Danilo Leiva-Leon, 2017, "Measuring business cycles intra-synchronization in us: a regime-switching interdependence framework," Working Papers, Banco de España, number 1726, Jul.
• Hector M. Zarate-Solano & Daniel R. Zapata-Sanabria, 2017, "Clustering and forecasting inflation expectations using the World Economic Survey: the case of the 2014 oil price shock on inflation targeting countries," Borradores de Economia, Banco de la Republica de Colombia, number 993, May, DOI: 10.32468/be.993.
• Danilo Leiva-Leon, 2017, "Measuring Business Cycles Intra-Synchronization in US: A Regime-switching Interdependence Framework," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, volume 79, issue 4, pages 513-545, August.
• Chiranjit Chakraborty & Andreas Joseph, 2017, "Machine learning at central banks," Bank of England working papers, Bank of England, number 674, Sep.
• Patrick Kouontchou & Bertrand Maillet & Alejandro Modesto & Sessi Tokpavi, 2017, "Quand l’union fait la force : un indice de risque systémique," Revue économique, Presses de Sciences-Po, volume 68, issue HS1, pages 87-106.
• Alvaro J. Riascos & Mauricio Romero & Natalia Serna, 2017, "Risk Adjustment Revisited using Machine Learning Techniques," Documentos CEDE, Universidad de los Andes, Facultad de Economía, CEDE, number 15601, Mar.
• Andrés González, 2017, "Evaluación de pronósticos de modelos lineales y no lineales de la tasa de cambio de Colombia," Vniversitas Económica, Universidad Javeriana - Bogotá, volume 0, issue 0, pages 1-45.
• Luis Sandoval Garrido & Margarita Marin Jaramillo, 2017, "The effect of a police sectoral communication network on crime rates in Bogotá, Colombia," Revista Ecos de Economía, Universidad EAFIT, volume 21, issue 45, pages 5-25, DOI: 10.17230/ecos.2017.45.1.
• Andrej Srakar, 2017, "Prevalence of Diseases and Health Care Utilization ofthe Self-Employed Artists and TheirEmpirical Determinants: Evidence From a Slovenian Survey," ACEI Working Paper Series, Association for Cultural Economics International, number AWP-08-2017, Sep, revised Sep 2017.
• Narges TALEBIMOTLAGH & Farzad HASHEMZADEH & Amir RIKHTEHGAR GHIASI & Sehraneh GHAEMI, 2017, "A Novel Method of Modeling Dynamic Evolutionary Game with Rational Agents for Market Forecasting," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, volume 51, issue 1, pages 281-302.
• Huseyin INCE & Theodore B. TRAFALİS, 2017, "A Hybrid Forecasting Model for Stock Market Prediction," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, volume 51, issue 3, pages 263-280.
• Charlie Joyez, 2017, "Network Structure of French Multinational Firms," Working Papers, DIAL (Développement, Institutions et Mondialisation), number DT/2017/08, Oct.
• Sophie Harnay & Elisabeth Tovar, 2017, "Obeying vs. resisting unfair laws. A structural analysis of the internalization of collective preferences on redistribution using classification trees and random forests," EconomiX Working Papers, University of Paris Nanterre, EconomiX, number 2017-34.
• Maryam Hosseinzadeh & Saeed Daei-Karimzadeh, 2017, "Investigate the Effect of Exchange Rate Volatility on the Demand for Life Insurance in Iran," International Journal of Economics and Financial Issues, Econjournals, volume 7, issue 2, pages 166-174.
• Zhang, Heng-Guo & Su, Chi-Wei & Song, Yan & Qiu, Shuqi & Xiao, Ran & Su, Fei, 2017, "Calculating Value-at-Risk for high-dimensional time series using a nonlinear random mapping model," Economic Modelling, Elsevier, volume 67, issue C, pages 355-367, DOI: 10.1016/j.econmod.2017.02.014.
• Creel, Michael, 2017, "Neural nets for indirect inference," Econometrics and Statistics, Elsevier, volume 2, issue C, pages 36-49, DOI: 10.1016/j.ecosta.2016.11.008.
• Zhao, Yang & Li, Jianping & Yu, Lean, 2017, "A deep learning ensemble approach for crude oil price forecasting," Energy Economics, Elsevier, volume 66, issue C, pages 9-16, DOI: 10.1016/j.eneco.2017.05.023.
• Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017, "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, volume 50, issue C, pages 67-80, DOI: 10.1016/j.irfa.2017.02.004.
• Brida, Juan Gabriel & Gómez, David Matesanz & Seijas, Maria Nela, 2017, "Debt and growth: A non-parametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, volume 486, issue C, pages 883-894, DOI: 10.1016/j.physa.2017.05.060.
• O.V. Andreeva & E.V. Shevchik, 2017, "Organizational and Financial Modeling of Transnational Industrial Clusters Sustainable Development: Experience, Risks, Management Innovation," European Research Studies Journal, European Research Studies Journal, volume 0, issue 1, pages 137-147.
• Thomas R. Cook & Aaron Smalter Hall, 2017, "Macroeconomic Indicator Forecasting with Deep Neural Networks," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 17-11, Sep, DOI: 10.18651/RWP2017-11.
• Jason Brown & Maeve Maloney & Jordan Rappaport & Aaron Smalter Hall, 2017, "How Centralized is U.S. Metropolitan Employment?," Research Working Paper, Federal Reserve Bank of Kansas City, number RWP 17-16, Nov, DOI: 10.18651/RWP2017-16.
• Héctor Horacio Garza Sánchez & Klender Aimer Cortez Alejandro & Alma Berenice Méndez Sáenz & Martha del Pilar Rodríguez García, 2017, "Efecto en la calidad de la información ante cambios en la normatividad contable: caso aplicado al sector real mexicano," Contaduría y Administración, Accounting and Management, volume 62, issue 3, pages 746-760, Julio-Sep.
• Héctor Horacio Garza Sánchez & Klender Aimer Cortez Alejandro & Alma Berenice Méndez Sáenz & Martha del Pilar Rodríguez García, 2017, "Effect of information quality due accounting regulatory changes: Applied case to Mexican real sector," Contaduría y Administración, Accounting and Management, volume 62, issue 3, pages 761-774, Julio-Sep.
• Karol Szafranek, 2017, "Bagged artificial neural networks in forecasting inflation: An extensive comparison with current modelling frameworks," NBP Working Papers, Narodowy Bank Polski, number 262.
• Evgenia Vasileva, 2017, "Creating of Something from Nothing. Methodic. Applying the Principles of Chaos and Complex Systems in a Learning Environment," Nauchni trudove, University of National and World Economy, Sofia, Bulgaria, issue 2, pages 169-186, October.
• Fajar, Muhammad & Hartini, Sri, 2017, "Inflation forecasting by hybrid singular spectrum analysis – multilayer perceptrons neural network method, case of Indonesia," MPRA Paper, University Library of Munich, Germany, number 105100, Oct, revised 11 May 2018.
• de Rigo, Daniele & Caudullo, Giovanni & San-Miguel-Ayanz, Jesús & Barredo, José I., 2017, "Robust modelling of the impacts of climate change on the habitat suitability of forest tree species," MPRA Paper, University Library of Munich, Germany, number 78623, Mar.
• Brummelhuis, Raymond & Luo, Zhongmin, 2017, "CDS Rate Construction Methods by Machine Learning Techniques," MPRA Paper, University Library of Munich, Germany, number 79194, May.
• Milan Fičura, 2017, "Forecasting Stock Market Realized Variance with Echo State Neural Networks," European Financial and Accounting Journal, Prague University of Economics and Business, volume 2017, issue 3, pages 145-155, DOI: 10.18267/j.efaj.193.
• Kaznacheev, Peter F. (Казначеев, Петр) & Kjurchiski, Nikola V. (Кюрчиски, Никола) & Samoilova, Regina V. (Самойлова, Регина), 2017, "Adaptation to Lower Oil Prices: International Corporations and Junior Shale Companies
  [Адаптация К Снижению Цен На Нефть: Международные Корпорации И Сланцевые Компании]," Ekonomicheskaya Politika / Economic Policy, Russian Presidential Academy of National Economy and Public Administration, volume 6, pages 148-159, December.
• Dimitrios Lyridis & Nikolaos Manos & Panayotis Zacharioudakis & Athanassios Pappas & Aristidis Mavris, 2017, "Measuring Tanker Market Future Risk with the use of FORESIM," SPOUDAI Journal of Economics and Business, SPOUDAI Journal of Economics and Business, University of Piraeus, volume 67, issue 1, pages 38-53, January-M.
• Florian Kreuchauff & Vladimir Korzinov, 2017, "A patent search strategy based on machine learning for the emerging field of service robotics," Scientometrics, Springer;Akadémiai Kiadó, volume 111, issue 2, pages 743-772, May, DOI: 10.1007/s11192-017-2268-3.
• Anthony Mouraud, 2017, "Innovative time series forecasting: auto regressive moving average vs deep networks," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, volume 4, issue 3, pages 282-293, March, DOI: 10.9770/jesi.2017.4.3S(4).
• León, C. & Moreno, José Fernando & Cely, Jorge, 2017, "Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition," Discussion Paper, Tilburg University, Center for Economic Research, number 2017-009.
• León, C. & Moreno, José Fernando & Cely, Jorge, 2017, "Whose Balance Sheet is this? Neural Networks for Banks' Pattern Recognition," Other publications TiSEM, Tilburg University, School of Economics and Management, number 75d8648e-9855-4c5c-9aa9-0.
• Jaime Tinto Arandes & Kléber Antonio Luna Altamirano & William Henry Sarmiento Espinoza & Diego Patricio Cisneros Quintanilla, 2017, "STIM12 creativity model for the design lady shoes under the approach of fuzzy subsets," Economía, Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela, volume 42, issue 44, pages 129-152, july-dece.
• Vasilios Plakandaras & Periklis Gogas & Theophilos Papadimitriou & Rangan Gupta, 2017, "The Informational Content of the Term Spread in Forecasting the US Inflation Rate: A Nonlinear Approach," Journal of Forecasting, John Wiley & Sons, Ltd., volume 36, issue 2, pages 109-121, March.
• Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2017, "Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models," HSC Research Reports, Hugo Steinhaus Center, Wroclaw University of Science and Technology, number HSC/17/03, Jul.
• Altgelt, Friederike & Koetter, Michael, 2017, "Too connected to fail? Wie die Vernetzung der Banken staatliche Rettungsmaßnahmen vorhersagen kann," Wirtschaft im Wandel, Halle Institute for Economic Research (IWH), volume 23, issue 4, pages 75-78.
• Chen, Cathy Yi-Hsuan & Härdle, Wolfgang Karl & Okhrin, Yarema, 2017, "Tail event driven networks of SIFIs," SFB 649 Discussion Papers, Humboldt University Berlin, Collaborative Research Center 649: Economic Risk, number 2017-004.

2016

• Anne Péguin-Feissolle & Bilel Sanhaji, 2016, "Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models," Annals of Economics and Statistics, GENES, issue 123-124, pages 77-101, DOI: 10.15609/annaeconstat2009.123-124.0.
• Borghesi, Simone & Flori, Andrea, , "EU ETS Facets in the Net: How Account Types Influence the Structure of the System," MITP: Mitigation, Innovation and Transformation Pathways, Fondazione Eni Enrico Mattei (FEEM), number 232214, DOI: 10.22004/ag.econ.232214.
• Jozwiak, Akos & Milkovics, Matyas & Lakner, Zoltan, None, "A Network-Science Support System for Food Chain Safety: A Case from Hungarian Cattle Production," International Food and Agribusiness Management Review, International Food and Agribusiness Management Association, volume 19, issue A, pages 1-26, DOI: 10.22004/ag.econ.240694.
• Yaroslav I. VYKLYUK & Valeriy K. YEVDOKYMENKO & Ihor V. YASKAL, 2016, "The Proportions And Rates Of Economic Activities As A Factor Of Gross Value Added Maximization In Transition Economy," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, volume 63, issue 1, pages 55-64, March.
• Gintaras CERNIUS & Liucija BIRSKYTE & Arturas BALKEVICIUS, 2016, "Influence Of Rules For Computing Corporate Income Tax On The Accuracy Of Financial Statements Of Lithuanian Companies," Scientific Annals of Economics and Business (continues Analele Stiintifice), Alexandru Ioan Cuza University, Faculty of Economics and Business Administration, volume 63, issue 1, pages 65-81, March.
• Özcan Mutlu & Muhammed Ordu & Olcay Polat, 2016, "Comparison of Individual Pension System and Bank's Deposit System for Low-Risk Investors," Alphanumeric Journal, Bahadir Fatih Yildirim, volume 4, issue 2, pages 95-114, September, DOI: http://dx.doi.org/10.17093/aj.2016..
• Evgeniya Kozlova & Vladimir Volynsky, 2016, "A fuzzy model for the evaluation of suppliers of material resources to machine-building enterprises," International Economics, University of Lodz, Faculty of Economics and Sociology, issue 15, pages 245-277, September.
• A?da Kammoun & Imen Triki, 2016, "Credit Scoring Models for a Tunisian Microfinance Institution: Comparison between Artificial Neural Network and Logistic Regression," Review of Economics & Finance, Better Advances Press, Canada, volume 6, pages 61-78, February.
• Carlos León & José Fernando Moreno & Jorge Cely, 2016, "Whose Balance Sheet is this? Neural Networks for Banks’ Pattern Recognition," Borradores de Economia, Banco de la Republica de Colombia, number 959, Sep, DOI: 10.32468/be.959.
• Michael Creel, 2016, "Neural Nets for Indirect Inference," Working Papers, Barcelona School of Economics, number 942, Nov.
• Jonnathan R. Cáceres Santos, 2016, "Pronóstico de la actividad económica con base en el volumen transaccional - caso boliviano," Revista de Análisis del BCB, Banco Central de Bolivia, volume 24, issue 1, pages 115-145, June.
• José Alberto Molina & Alberto Alcolea & Alfredo Ferrer & Alberto Alcolea & David Iñiguez & Alejandro Rivero & Gonzalo Ruiz & Alfonso Tarancón, 2016, "Co-authorship and Academic Productivity in Economics: Interaction Maps from the Complex Networks Approach," Boston College Working Papers in Economics, Boston College Department of Economics, number 914, Jun.
• Oscar Claveria & Enric Monte & Salvador Torra, 2016, "A self-organizing map analysis of survey-based agents? expectations before impending shocks for model selection: The case of the 2008 financial crisis," International Economics, CEPII research center, issue 146, pages 40-58.
• Gustavo Peralta, 2016, "The Nature of Volatility Spillovers across the International Capital Markets," CNMV Working Papers, CNMV- Spanish Securities Markets Commission - Research and Statistics Department, number CNMV Working Papers no. 6.
• Andrej Srakar & Petja Grafenauer & Marilena Vecco, 2016, "Being Central and Productive? Evidence from Slovenian Visual Artists in the 19th and 20th Century," ACEI Working Paper Series, Association for Cultural Economics International, number AWP-09-2016, Sep, revised Sep 2016.
• Vasile GEORGESCU, 2016, "Using Nature-Inspired Metaheuristics to Train Predictive Machines," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, volume 50, issue 2, pages 5-24.
• Cătălina-Lucia COCIANU & Hakob GRIGORYAN, 2016, "Machine Learning Techniques For Stock Market Prediction.Acase Study Of Omv Petrom," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, volume 50, issue 3, pages 63-82.
• Vergote, Olivier, 2016, "Credit risk spillover between financials and sovereigns in the euro area during 2007-2015," Working Paper Series, European Central Bank, number 1898, Apr.
• Kok, Christoffer & Montagna, Mattia, 2016, "Multi-layered interbank model for assessing systemic risk," Working Paper Series, European Central Bank, number 1944, Aug.
• Endrész, Marianna & Skudelny, Frauke, 2016, "Crisis severity and the international trade network," Working Paper Series, European Central Bank, number 1971, Oct.
• Baruník, Jozef & Malinská, Barbora, 2016, "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, volume 164, issue C, pages 366-379, DOI: 10.1016/j.apenergy.2015.11.051.
• Kiani, Khurshid M., 2016, "On business cycle fluctuations in USA macroeconomic time series," Economic Modelling, Elsevier, volume 53, issue C, pages 179-186, DOI: 10.1016/j.econmod.2015.11.022.
• Ductor, Lorenzo & Leiva-Leon, Danilo, 2016, "Dynamics of global business cycle interdependence," Journal of International Economics, Elsevier, volume 102, issue C, pages 110-127, DOI: 10.1016/j.jinteco.2016.07.003.
• Claveria, Oscar & Monte, Enric & Torra, Salvador, 2016, "A self-organizing map analysis of survey-based agents׳ expectations before impending shocks for model selection: The case of the 2008 financial crisis," International Economics, Elsevier, volume 146, issue C, pages 40-58, DOI: 10.1016/j.inteco.2015.11.003.
• Simone Borghesi & Andrea Flori, 2016, "EU ETS Facets in the Net: How Account Types Influence the Structure of the System," Working Papers, Fondazione Eni Enrico Mattei, number 2016.08, Jan.
• Ben R. Craig & Martin Saldias Zambrana, 2016, "Spatial Dependence and Data-Driven Networks of International Banks," Working Papers (Old Series), Federal Reserve Bank of Cleveland, number 1627, Dec.
• David E. Allen & Michael McAleer & Shelton Peiris & Abhay K. Singh, 2016, "Nonlinear Time Series and Neural-Network Models of Exchange Rates between the US Dollar and Major Currencies," Risks, MDPI, volume 4, issue 1, pages 1-14, March.
• Leoni Eleni Oikonomikou, 2016, "Forecasting the Market Risk Premium with Artificial Neural Networks," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers, Courant Research Centre PEG, number 202, Apr.
• Leoni Eleni Oikonomikou, 2016, "Comparing the market risk premia forecasts in JSE and NYSE equity markets," Courant Research Centre: Poverty, Equity and Growth - Discussion Papers, Courant Research Centre PEG, number 203, Apr.
• Anne Peguin-Feissolle & Bilel Sanhaji, 2016, "Tests of the Constancy of Conditional Correlations of Unknown Functional Form in Multivariate GARCH Models," Post-Print, HAL, number hal-04218472, DOI: 10.15609/annaeconstat2009.123-124.0.
• Fahima Charef & Fethi Ayachi, 2016, "A Comparison between Neural Networks and GARCH Models in Exchange Rate Forecasting," International Journal of Academic Research in Accounting, Finance and Management Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Accounting, Finance and Management Sciences, volume 6, issue 1, pages 94-99, January.
• Fahima Charef & Fethi Ayachi, 2016, "A Comparison between Neural Networks and GARCH Models in Exchange Rate Forecasting," International Journal of Academic Research in Business and Social Sciences, Human Resource Management Academic Research Society, International Journal of Academic Research in Business and Social Sciences, volume 6, issue 1, pages 244-253, January.
• Stephanie Valdivia & Arturo Morales, 2016, "Determinants Of The Index Of Prices And Quotations On The Mexican Stock Exchange: Sensitivity Analysis Based On Artificial Neural Networks," Global Journal of Business Research, The Institute for Business and Finance Research, volume 10, issue 2, pages 27-32.
• Mr. Alexei P Kireyev & Andrei Leonidov, 2016, "A Network Model of Multilaterally Equilibrium Exchange Rates," IMF Working Papers, International Monetary Fund, number 2016/130, Jul.
• Ben Craig & Martín Saldías, 2016, "Spatial Dependence and Data-Driven Networks of International Banks," IMF Working Papers, International Monetary Fund, number 2016/184, Sep.
• Michel Philipp & Achim Zeileis & Carolin Strobl, 2016, "A Toolkit for Stability Assessment of Tree-Based Learners," Working Papers, Faculty of Economics and Statistics, Universität Innsbruck, number 2016-11, May.
• Florian Wickelmaier & Achim Zeileis, 2016, "Using Recursive Partitioning to Account for Parameter Heterogeneity in Multinomial Processing Tree Models," Working Papers, Faculty of Economics and Statistics, Universität Innsbruck, number 2016-26, Sep.
• Molina, José Alberto & Alcolea, Alberto & Ferrer, Alfredo & Iñiguez, David & Rivero, Alejandro & Ruiz, Gonzalo & Tarancón, Alfonso, 2016, "Co-authorship and Academic Productivity in Economics: Interaction Maps from the Complex Networks Approach," IZA Discussion Papers, IZA Network @ LISER, number 10008, Jun.
• Julian Hagenauer, 2016, "Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks," Journal of Geographical Systems, Springer, volume 18, issue 1, pages 1-15, January, DOI: 10.1007/s10109-015-0220-8.
• Julian Hagenauer, 2016, "Weighted merge context for clustering and quantizing spatial data with self-organizing neural networks," Journal of Geographical Systems, Springer, volume 18, issue 1, pages 1-15, January, DOI: 10.1007/s10109-015-0220-8.
• Salehi, Mehdi & Hamidehpour, Kiana & Khadem, Hamid, 2016, "Comparison of Forecasting the Index Price Movement in Financial Institutions using Artificial Intelligence," Journal of Monetary and Banking Research (فصلنامه پژوهش‌های پولی-بانکی), Monetary and Banking Research Institute, Central Bank of the Islamic Republic of Iran, volume 9, issue 27, pages 131-170, April.
• Áron Horváth & Blanka Imre & Zoltán Sápi, 2016, "The International Practice of Statistical Property Valuation Methods and the Possibilities of Introducing Automated Valuation Models in Hungary," Financial and Economic Review, Magyar Nemzeti Bank (Central Bank of Hungary), volume 15, issue 4, pages 45-64.
• Ricardo Artemio Chávez Meza & Arturo Ángel Lara Rivero, 2016, "The diversity of agents and evolution of overlapping patents on electric vehicles," Contaduría y Administración, Accounting and Management, volume 61, issue 4, pages 603-628, Octubre-D.
• Daniel Goetz, 2016, "Broadband Mergers and Dynamic Bargaining: An Application to Netflix," Working Papers, NET Institute, number 16-07, Sep.
• Xiao Liu & Dokyun Lee & Kannan Srinivasan, 2016, "The Effect of Word of Mouth on Sales: New Answers from the Comprehensive Consumer Journey Data," Working Papers, NET Institute, number 16-09, Sep.