Report NEP-CMP-2019-10-21

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:

• Vinci Chow, 2019. "Predicting Auction Price of Vehicle License Plate with Deep Residual Learning," Papers 1910.04879, arXiv.org.
• Stefan Irnich & Timo Hintsch & Lone Kiilerich, 2019. "Branch-Price-and-Cut for the Soft-Clustered Capacitated Arc-Routing Problem," Working Papers 1911, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
• Bolte, Jérôme & Castera, Camille & Pauwels, Edouard & Févotte, Cédric, 2019. "An Inertial Newton Algorithm for Deep Learning," TSE Working Papers 19-1043, Toulouse School of Economics (TSE).
• J. Lussange & S. Bourgeois-Gironde & S. Palminteri & B. Gutkin, 2019. "Stock price formation: useful insights from a multi-agent reinforcement learning model," Papers 1910.05137, arXiv.org.
• Damir Filipovi'c & Kathrin Glau & Yuji Nakatsukasa & Francesco Statti, 2019. "Weighted Monte Carlo with least squares and randomized extended Kaczmarz for option pricing," Papers 1910.07241, arXiv.org.
• Böhringer, Christoph & Rosendahl, Knut Einar & Briseid Storrøsten, Halvor, 2019. "Smart hedging against carbon leakage," Working Paper Series 4-2019, Norwegian University of Life Sciences, School of Economics and Business.
• Dieckmann, Peter & Patterson, Mary & Lahlou, Saadi & Mesman, Jessica & Nyström, Patrik & Krage, Ralf, 2017. "Variation and adaptation: learning from success in patient safety-oriented simulation training," LSE Research Online Documents on Economics 101889, London School of Economics and Political Science, LSE Library.
• Maas, Benedikt, 2019. "Nowcasting and forecasting US recessions: Evidence from the Super Learner," MPRA Paper 96408, University Library of Munich, Germany.
• Bolte, Jérôme & Pauwels, Edouard, 2019. "Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning," TSE Working Papers 19-1044, Toulouse School of Economics (TSE).
• L. Jason Anastasopoulos, 2019. "Principled estimation of regression discontinuity designs," Papers 1910.06381, arXiv.org, revised May 2020.
• Helene Maisonnave & Bernard Decaluwé & Margaret Chitiga, 2019. "Does South African Affirmative Action Policy Reduce Poverty? A CGE Analysis," Working Papers hal-02314221, HAL.
• Geni, Bias Yulisa & Santony, Julius & Sumijan, Sumijan, 2019. "Prediksi Pendapatan Terbesar pada Penjualan Produk Cat dengan Menggunakan Metode Monte Carlo [Prediction of the Biggest Revenue in the Sales of Cat Products Using the Monte Carlo Method]," MPRA Paper 96524, University Library of Munich, Germany.
• Deli Chen & Yanyan Zou & Keiko Harimoto & Ruihan Bao & Xuancheng Ren & Xu Sun, 2019. "Incorporating Fine-grained Events in Stock Movement Prediction," Papers 1910.05078, arXiv.org.
• Smith, Gary, 2019. "The Paradox of Big Data," Economics Department, Working Paper Series 1003, Economics Department, Pomona College, revised 04 Jun 2019.
• Jifei Wang & Lingjing Wang, 2019. "Residual Switching Network for Portfolio Optimization," Papers 1910.07564, arXiv.org.
• Stefania Albanesi & Domonkos F. Vamossy, 2019. "Predicting Consumer Default: A Deep Learning Approach," Working Papers 2019-056, Human Capital and Economic Opportunity Working Group.