Report NEP-CMP-2020-08-17

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:

• Maciej Wysocki & Robert Ślepaczuk, 2020. "Artificial Neural Networks Performance in WIG20 Index Options Pricing," Working Papers 2020-19, Faculty of Economic Sciences, University of Warsaw.
• Mateusz Kijewski & Robert Ślepaczuk, 2020. "Predicting prices of S&P500 index using classical methods and recurrent neural networks," Working Papers 2020-27, Faculty of Economic Sciences, University of Warsaw.
• Debnath, R. & Darby, S. & Bardhan, R. & Mohaddes, K. & Sunikka-Blank, M., 2020. "Grounded reality meets machine learning: A deep-narrative analysis framework for energy policy research," Cambridge Working Papers in Economics 2062, Faculty of Economics, University of Cambridge.
• Situngkir, Hokky & Lumbantobing, Andika Bernad, 2020. "The Pandemics in Artificial Society: Agent-Based Model to Reflect Strategies on COVID-19," MPRA Paper 102075, University Library of Munich, Germany.
• Marta Kłosok & Marcin Chlebus, 2020. "Towards better understanding of complex machine learning models using Explainable Artificial Intelligence (XAI) - case of Credit Scoring modelling," Working Papers 2020-18, Faculty of Economic Sciences, University of Warsaw.
• Nicholas Moehle & Mykel J. Kochenderfer & Stephen Boyd & Andrew Ang, 2020. "Tax-Aware Portfolio Construction via Convex Optimization," Papers 2008.04985, arXiv.org, revised Feb 2021.
• Wen Chen & Nicolas Langrené, 2020. "Deep neural network for optimal retirement consumption in defined contribution pension system [Réseau de neurones profond pour consommation à la retraite optimale en système de retraite à cotisatio," Working Papers hal-02909818, HAL.
• Karim Barigou & Lukasz Delong, 2021. "Pricing equity-linked life insurance contracts with multiple risk factors by neural networks," Working Papers hal-02896141, HAL.
• Hannes Mueller & Christopher Rauh, 2019. "The hard problem of prediction for conflict prevention," Cahiers de recherche 2019-02, Universite de Montreal, Departement de sciences economiques.
• Friberg, Richard, 2019. "All the bottles in one basket? Diversification and product portfolio composition," CEPR Discussion Papers 14119, C.E.P.R. Discussion Papers.
• Karim Barigou & Lukasz Delong, 2020. "Pricing equity-linked life insurance contracts with multiple risk factors by neural networks," Papers 2007.08804, arXiv.org, revised Nov 2021.
• Rui Zhou & Daniel P. Palomar, 2020. "Solving High-Order Portfolios via Successive Convex Approximation Algorithms," Papers 2008.00863, arXiv.org.
• Hossain, Md Mahbub & McKyer, E. Lisako J. & Ma, Ping, 2020. "Applications of artificial intelligence technologies on mental health research during COVID-19," SocArXiv w6c9b, Center for Open Science.
• Jan Pablo Burgard & Patricia Dörr & Ralf Münnich, 2020. "Monte-Carlo Simulation Studies in Survey Statistics – An Appraisal," Research Papers in Economics 2020-04, University of Trier, Department of Economics.
• Daniel Arribas-Bel & Miquel-Àngel Garcia-López & Elisabet Viladecans-Marsal, 2019. "Building(s and) cities: delineating urban areas with a machine learning algorithm," Working Papers 2019/10, Institut d'Economia de Barcelona (IEB).
• Maximilian Andres & Lisa Bruttel & Jana Friedrichsen, 2020. "Choosing between explicit cartel formation and tacit collusion – An experiment," CEPA Discussion Papers 19, Center for Economic Policy Analysis.
• Christopher Rauh, 2019. "Measuring uncertainty at the regional level using newspaper text," Cahiers de recherche 2019-07, Universite de Montreal, Departement de sciences economiques.
• Deshpande, Advait, 2020. "The potential influence of machine learning and data science on the future of economics: Overview of highly-cited research," SocArXiv 9nh8g, Center for Open Science.
• Ariel Lanza & Enrico Bernardini & Ivan Faiella, 2020. "Mind the gap! Machine learning, ESG metrics and sustainable investment," Questioni di Economia e Finanza (Occasional Papers) 561, Bank of Italy, Economic Research and International Relations Area.
• Jake Anders & Catherine Dilnot & Lindsey Macmillan & Gill Wyness, 2020. "Grade Expectations: How well can we predict future grades based on past performance?," CEPEO Working Paper Series 20-14, UCL Centre for Education Policy and Equalising Opportunities, revised Aug 2020.