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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. Stanley Miles issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
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, 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.
- 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, Faculty of Economics, University of Cambridge, number 2062, Jul.
- Situngkir, Hokky & Lumbantobing, Andika Bernad, 2020, "The Pandemics in Artificial Society: Agent-Based Model to Reflect Strategies on COVID-19," MPRA Paper, University Library of Munich, Germany, number 102075, Jul.
- 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, Faculty of Economic Sciences, University of Warsaw, number 2020-18.
- Nicholas Moehle & Mykel J. Kochenderfer & Stephen Boyd & Andrew Ang, 2020, "Tax-Aware Portfolio Construction via Convex Optimization," Papers, arXiv.org, number 2008.04985, Aug, 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 cotisations définies]," Working Papers, HAL, number hal-02909818, Jul. - Item repec:hal:wpaper:hal-02896141 is not listed on IDEAS anymore
- Hannes Mueller & Christopher Rauh, 2019, "The hard problem of prediction for conflict prevention," Cahiers de recherche, Universite de Montreal, Departement de sciences economiques, number 2019-02, Apr.
- Friberg, Richard, 2019, "All the bottles in one basket? Diversification and product portfolio composition," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 14119, Nov.
- Karim Barigou & Lukasz Delong, 2020, "Pricing equity-linked life insurance contracts with multiple risk factors by neural networks," Papers, arXiv.org, number 2007.08804, Jul, revised Nov 2021.
- Rui Zhou & Daniel P. Palomar, 2020, "Solving High-Order Portfolios via Successive Convex Approximation Algorithms," Papers, arXiv.org, number 2008.00863, Aug.
- Hossain, Md Mahbub & McKyer, E. Lisako J. & Ma, Ping, 2020, "Applications of artificial intelligence technologies on mental health research during COVID-19," SocArXiv, Center for Open Science, number w6c9b, Jun, DOI: 10.31219/osf.io/w6c9b.
- Jan Pablo Burgard & Patricia Dörr & Ralf Münnich, 2020, "Monte-Carlo Simulation Studies in Survey Statistics – An Appraisal," Research Papers in Economics, University of Trier, Department of Economics, number 2020-04.
- 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, Institut d'Economia de Barcelona (IEB), number 2019/10.
- Maximilian Andres & Lisa Bruttel & Jana Friedrichsen, 2020, "Choosing between explicit cartel formation and tacit collusion – An experiment," CEPA Discussion Papers, Center for Economic Policy Analysis, number 19, Jul, DOI: 10.25932/publishup-47388.
- Christopher Rauh, 2019, "Measuring uncertainty at the regional level using newspaper text," Cahiers de recherche, Universite de Montreal, Departement de sciences economiques, number 2019-07, Aug.
- Deshpande, Advait, 2020, "The potential influence of machine learning and data science on the future of economics: Overview of highly-cited research," SocArXiv, Center for Open Science, number 9nh8g, Apr, DOI: 10.31219/osf.io/9nh8g.
- Ariel Lanza & Enrico Bernardini & Ivan Faiella, 2020, "Mind the gap! Machine learning, ESG metrics and sustainable investment," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 561, Jun.
- 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, UCL Centre for Education Policy and Equalising Opportunities, number 20-14, Aug, revised Aug 2020.