Printed from https://ideas.repec.org/n/nep-cmp/2019-10-21.html
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. 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:
- Vinci Chow, 2019, "Predicting Auction Price of Vehicle License Plate with Deep Residual Learning," Papers, arXiv.org, number 1910.04879, Oct.
- Stefan Irnich & Timo Hintsch & Lone Kiilerich, 2019, "Branch-Price-and-Cut for the Soft-Clustered Capacitated Arc-Routing Problem," Working Papers, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, number 1911, Oct.
- Bolte, Jérôme & Castera, Camille & Pauwels, Edouard & Févotte, Cédric, 2019, "An Inertial Newton Algorithm for Deep Learning," TSE Working Papers, Toulouse School of Economics (TSE), number 19-1043, Oct.
- J. Lussange & S. Bourgeois-Gironde & S. Palminteri & B. Gutkin, 2019, "Stock price formation: useful insights from a multi-agent reinforcement learning model," Papers, arXiv.org, number 1910.05137, Oct.
- Damir Filipovi'c & Kathrin Glau & Yuji Nakatsukasa & Francesco Statti, 2019, "Weighted Monte Carlo with least squares and randomized extended Kaczmarz for option pricing," Papers, arXiv.org, number 1910.07241, Oct.
- Böhringer, Christoph & Rosendahl, Knut Einar & Briseid Storrøsten, Halvor, 2019, "Smart hedging against carbon leakage," Working Paper Series, Norwegian University of Life Sciences, School of Economics and Business, number 4-2019, Oct.
- 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, London School of Economics and Political Science, LSE Library, number 101889, Oct.
- Maas, Benedikt, 2019, "Nowcasting and forecasting US recessions: Evidence from the Super Learner," MPRA Paper, University Library of Munich, Germany, number 96408, Sep.
- Bolte, Jérôme & Pauwels, Edouard, 2019, "Conservative set valued fields, automatic differentiation, stochastic gradient methods and deep learning," TSE Working Papers, Toulouse School of Economics (TSE), number 19-1044, Oct.
- L. Jason Anastasopoulos, 2019, "Principled estimation of regression discontinuity designs," Papers, arXiv.org, number 1910.06381, Oct, revised May 2020.
- Helene Maisonnave & Bernard Decaluwé & Margaret Chitiga, 2019, "Does South African Affirmative Action Policy Reduce Poverty? A CGE Analysis," Working Papers, HAL, number hal-02314221, Oct.
- 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, University Library of Munich, Germany, number 96524, Oct. - Deli Chen & Yanyan Zou & Keiko Harimoto & Ruihan Bao & Xuancheng Ren & Xu Sun, 2019, "Incorporating Fine-grained Events in Stock Movement Prediction," Papers, arXiv.org, number 1910.05078, Oct.
- Smith, Gary, 2019, "The Paradox of Big Data," Economics Department, Working Paper Series, Economics Department, Pomona College, number 1003, Jan, revised 04 Jun 2019.
- Jifei Wang & Lingjing Wang, 2019, "Residual Switching Network for Portfolio Optimization," Papers, arXiv.org, number 1910.07564, Oct.
- Stefania Albanesi & Domonkos F. Vamossy, 2019, "Predicting Consumer Default: A Deep Learning Approach," Working Papers, Human Capital and Economic Opportunity Working Group, number 2019-056, Sep.