Report NEP-CMP-2021-03-15

This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stan Miles issued this report. It is usually issued weekly.

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Other reports in NEP-CMP

The following items were announced in this report:

• Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.
• 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 2020-006, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
• Maximilien Germain & Mathieu Lauri`ere & Huy^en Pham & Xavier Warin, 2021. "DeepSets and their derivative networks for solving symmetric PDEs," Papers 2103.00838, arXiv.org, revised Jan 2022.
• Xingcai Zhou & Jiangyan Wang, 2021. "Panel semiparametric quantile regression neural network for electricity consumption forecasting," Papers 2103.00711, arXiv.org.
• Baptiste Barreau & Laurent Carlier, 2020. "History-Augmented Collaborative Filtering for Financial Recommendations," Post-Print hal-03144669, HAL.
• Gary Cornwall & Jeff Chen & Beau Sauley, 2021. "Standing on the Shoulders of Machine Learning: Can We Improve Hypothesis Testing?," Papers 2103.01368, arXiv.org.
• Zijian Shi & Yu Chen & John Cartlidge, 2021. "The LOB Recreation Model: Predicting the Limit Order Book from TAQ History Using an Ordinary Differential Equation Recurrent Neural Network," Papers 2103.01670, arXiv.org.
• Erik Heilmann & Andreas Zeiselmair & Thomas Estermann, 2021. "Matching supply and demand of electricity network-supportive flexibility: A case study with three comprehensible matching algorithms," MAGKS Papers on Economics 202110, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
• Yongyang Cai & Kenneth L. Judd, 2021. "A Simple but Powerful Simulated Certainty Equivalent Approximation Method for Dynamic Stochastic Problems," NBER Working Papers 28502, National Bureau of Economic Research, Inc.
• Branka Hadji Misheva & Joerg Osterrieder & Ali Hirsa & Onkar Kulkarni & Stephen Fung Lin, 2021. "Explainable AI in Credit Risk Management," Papers 2103.00949, arXiv.org.
• Philippe Goulet Coulombe, 2021. "Slow-Growing Trees," Papers 2103.01926, arXiv.org, revised Jul 2021.
• Nicholas Moehle & Jack Gindi & Stephen Boyd & Mykel Kochenderfer, 2021. "Portfolio Construction as Linearly Constrained Separable Optimization," Papers 2103.05455, arXiv.org, revised Jul 2022.
• Morris A. Davis & Jesse M. Gregory & Daniel A. Hartley & Kegon T.K. Tan, 2021. "Neighborhood Effects and Housing Vouchers," NBER Working Papers 28508, National Bureau of Economic Research, Inc.
• J. Ignacio Conde-Ruiz & Juan José Ganuza & Manu Garcia & Luis A. Puch, 2021. "Gender distribution across topics in Top 5 economics journals: A machine learning approach," Economics Working Papers 1771, Department of Economics and Business, Universitat Pompeu Fabra.
• Stamer, Vincent, 2021. "Thinking outside the container: A machine learning approach to forecasting trade flows," Kiel Working Papers 2179, Kiel Institute for the World Economy (IfW Kiel).
• M. Avellaneda & T. N. Li & A. Papanicolaou & G. Wang, 2021. "Trading Signals In VIX Futures," Papers 2103.02016, arXiv.org, revised Nov 2021.
• Furkan Gursoy & Bertan Badur, 2021. "An Agent-Based Modelling Approach to Brain Drain," Papers 2103.03234, arXiv.org, revised Mar 2021.
• Wu, Desheng Dang & Härdle, Wolfgang Karl, 2020. "Service Data Analytics and Business Intelligence," IRTG 1792 Discussion Papers 2020-002, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
• Doherr, Thorsten, 2021. "Disambiguation by namesake risk assessment," ZEW Discussion Papers 21-021, ZEW - Leibniz Centre for European Economic Research.
• Nikita Kozodoi & Johannes Jacob & Stefan Lessmann, 2021. "Fairness in Credit Scoring: Assessment, Implementation and Profit Implications," Papers 2103.01907, arXiv.org, revised Jun 2022.
• Shota Imaki & Kentaro Imajo & Katsuya Ito & Kentaro Minami & Kei Nakagawa, 2021. "No-Transaction Band Network: A Neural Network Architecture for Efficient Deep Hedging," Papers 2103.01775, arXiv.org.
• Philippe Goulet Coulombe & Massimiliano Marcellino & Dalibor Stevanovic, 2021. "Can Machine Learning Catch the COVID-19 Recession?," Papers 2103.01201, arXiv.org.
• Kristof Lommers & Ouns El Harzli & Jack Kim, 2021. "Confronting Machine Learning With Financial Research," Papers 2103.00366, arXiv.org, revised Mar 2021.
• Henry Hanifan & Ben Watson & John Cartlidge & Dave Cliff, 2021. "Time Matters: Exploring the Effects of Urgency and Reaction Speed in Automated Traders," Papers 2103.00600, arXiv.org.
• Bolte, Jérôme & Pauwels, Edouard, 2021. "A mathematical model for automatic differentiation in machine learning," TSE Working Papers 21-1184, Toulouse School of Economics (TSE).
• Spilak, Bruno & Härdle, Wolfgang Karl, 2020. "Tail-risk protection: Machine Learning meets modern Econometrics," IRTG 1792 Discussion Papers 2020-015, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
• Kéa Baret & Amélie Barbier-Gauchard & Théophilos Papadimitriou, 2021. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers of BETA 2021-01, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
• Sebastian Doerr & Leonardo Gambacorta & José María Serena Garralda, 2021. "Big data and machine learning in central banking," BIS Working Papers 930, Bank for International Settlements.
• Henri Fraisse & Matthias Laporte, 2021. "Return on Investment on AI: The Case of Capital Requirement," Working papers 809, Banque de France.
• Best, Katherine Laura & Speyer, Lydia Gabriela & Murray, Aja Louise & Ushakova, Anastasia, 2021. "Prediction of Attrition in Large Longitudinal Studies: Tree-based methods versus Multinomial Logistic Models," SocArXiv tyszr, Center for Open Science.
• Jérémy Fouliard & Michael Howell & Hélène Rey, 2021. "Answering the Queen: Machine learning and financial crises," BIS Working Papers 926, Bank for International Settlements.
• Ni, Xinwen & Härdle, Wolfgang Karl & Xie, Taojun, 2020. "A Machine Learning Based Regulatory Risk Index for Cryptocurrencies," IRTG 1792 Discussion Papers 2020-013, Humboldt University of Berlin, International Research Training Group 1792 "High Dimensional Nonstationary Time Series".
• Klaus Gründler & Tommy Krieger, 2021. "Using Machine Learning for Measuring Democracy: An Update," CESifo Working Paper Series 8903, CESifo.
• Tao Zou & Xian Li & Xuan Liang & Hansheng Wang, 2021. "On the Subbagging Estimation for Massive Data," Papers 2103.00631, arXiv.org.
• Jacques Bughin & Michele Cincera & Kelly Peters & Dorota Reykowska & Marcin Zyszkiewicz & Rafal Ohme, 2021. "Make it or Break it: Vaccination Intention at the Time of Covid-19," Working Papers TIMES² 2021-043, ULB -- Universite Libre de Bruxelles.
• Pavlova, Elitsa & Signore, Simone, 2021. "The European venture capital landscape: An EIF perspective. Volume VI: The impact of VC on the exit and innovation outcomes of EIF-backed start-ups," EIF Working Paper Series 2021/70, European Investment Fund (EIF).