Report NEP-CMP-2020-09-21

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:

• Shuaiqiang Liu & Lech A. Grzelak & Cornelis W. Oosterlee, 2020. "The Seven-League Scheme: Deep learning for large time step Monte Carlo simulations of stochastic differential equations," Papers 2009.03202, arXiv.org, revised Sep 2021.
• Michael Rollins & Dave Cliff, 2020. "Which Trading Agent is Best? Using a Threaded Parallel Simulation of a Financial Market Changes the Pecking-Order," Papers 2009.06905, arXiv.org.
• Pihnastyi, Oleh & Khodusov, Valery, 2020. "Neural model of conveyor type transport system," MPRA Paper 101527, University Library of Munich, Germany, revised 01 May 2020.
• Zhengxin Joseph Ye & Bjorn W. Schuller, 2020. "Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning," Papers 2009.03094, arXiv.org.
• Qiao Zhou & Ningning Liu, 2020. "A Stock Prediction Model Based on DCNN," Papers 2009.03239, arXiv.org.
• Yan Wang & Xuelei Sherry Ni, 2020. "Improving Investment Suggestions for Peer-to-Peer (P2P) Lending via Integrating Credit Scoring into Profit Scoring," Papers 2009.04536, arXiv.org.
• Benjamin Patrick Evans & Kirill Glavatskiy & Michael S. Harr'e & Mikhail Prokopenko, 2020. "The impact of social influence in Australian real-estate: market forecasting with a spatial agent-based model," Papers 2009.06914, arXiv.org, revised Feb 2021.
• Calvin Price, 2020. "Economic forecasting with multiequation simulation models," 2020 Stata Conference 5, Stata Users Group.
• Eric Benhamou & David Saltiel & Jean-Jacques Ohana & Jamal Atif, 2020. "Detecting and adapting to crisis pattern with context based Deep Reinforcement Learning," Papers 2009.07200, arXiv.org, revised Nov 2020.
• Joop van de Heijning & Stephan Leitner & Alexandra Rausch, 2020. "On the Effectiveness of Minisum Approval Voting in an Open Strategy Setting: An Agent-Based Approach," Papers 2009.04912, arXiv.org, revised Sep 2020.
• Song, Suihong & Mukerji, Tapan & Hou, Jiagen, 2020. "GANSim: Conditional Facies Simulation Using an Improved Progressive Growing of Generative Adversarial Networks (GANs)," Earth Arxiv fm24b, Center for Open Science.
• Asim Kumer Dey & Toufiqul Haq & Kumer Das & Irina Panovska, 2020. "Quantifying the impact of COVID-19 on the US stock market: An analysis from multi-source information," Papers 2008.10885, arXiv.org, revised Oct 2020.
• Castiel, Eyal & Borst, Sem & Miclo, Laurent & Simatos, Florian & Whiting, Phil, 2020. "Induced idleness leads to deterministic heavy traffic limits for queue-based random-access algorithms," TSE Working Papers 20-1129, Toulouse School of Economics (TSE).
• Patrick Reinwald & Stephan Leitner & Friederike Wall, 2020. "An Agent-Based Model of Delegation Relationships With Hidden-Action: On the Effects of Heterogeneous Memory on Performance," Papers 2009.07124, arXiv.org, revised Sep 2020.
• Billy Buchanan, 2020. "Implementing programming patterns in Mata to optimize your code," 2020 Stata Conference 9, Stata Users Group.
• Foltas, Alexander, 2020. "Testing investment forecast efficiency with textual data," Working Papers 19, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
• RODOMANOV Anton, & NESTEROV Yurii,, 2020. "Greedy quasi-Newton methods with explicit superlinear convergence," LIDAM Discussion Papers CORE 2020006, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
• Matthias Schonlau, 2020. "Text mining with n-gram variables," 2020 Stata Conference 10, Stata Users Group.
• Kye Lippold, 2020. "Applying symbolic mathematics in Stata using Python," 2020 Stata Conference 22, Stata Users Group.
• Angelo Cozzubo, 2020. "The social costs of crime over trust: An approach with machine learning," 2020 Stata Conference 27, Stata Users Group.