Report NEP-CMP-2020-01-13

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:

• Aniruddha Dutta & Saket Kumar & Meheli Basu, 2019. "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," Papers 1912.11166, arXiv.org.
• Jacobo Roa-Vicens & Yuanbo Wang & Virgile Mison & Yarin Gal & Ricardo Silva, 2019. "Adversarial recovery of agent rewards from latent spaces of the limit order book," Papers 1912.04242, arXiv.org.
• Dennis Kristensen & Patrick K. Mogensen & Jong-Myun Moon & Bertel Schjerning, 2019. "Solving dynamic discrete choice models using smoothing and sieve methods," CeMMAP working papers CWP15/19, Centre for Microdata Methods and Practice, Institute for Fiscal Studies.
• Nhi N.Y.Vo & Xue-Zhong He & Shaowu Liu & Guandong Xu, 2019. "Deep Learning for Decision Making and the Optimization of Socially Responsible Investments and Portfolio," Published Paper Series 2019-3, Finance Discipline Group, UTS Business School, University of Technology, Sydney.
• Melinscak, Filip & Bach, Dominik R, 2019. "Computational optimization of associative learning experiments," OSF Preprints cgpmh, Center for Open Science.
• Bräuning, Michael & Malikkidou, Despo & Scricco, Giorgio & Scalone, Stefano, 2019. "A new approach to Early Warning Systems for small European banks," Working Paper Series 2348, European Central Bank.
• Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019. "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers) 1256, Bank of Italy, Economic Research and International Relations Area.
• Somayeh Kokabisaghi & Mohammadesmaeil Ezazi & Reza Tehrani & Nourmohammad Yaghoubi, 2019. "Sanction or Financial Crisis? An Artificial Neural Network-Based Approach to model the impact of oil price volatility on Stock and industry indices," Papers 1912.04015, arXiv.org, revised Sep 2020.
• Benkovich, Nikita & Dedenok, Roman & Golubev, Dmitry, 2019. "Deep Quarantine for Suspicious Mail," MPRA Paper 97311, University Library of Munich, Germany, revised 23 Sep 2019.
• Jie Fang & Shutao Xia & Jianwu Lin & Yong Jiang, 2019. "Automatic Financial Feature Construction," Papers 1912.06236, arXiv.org, revised Oct 2020.
• Nicola Uras & Lodovica Marchesi & Michele Marchesi & Roberto Tonelli, 2020. "Forecasting Bitcoin closing price series using linear regression and neural networks models," Papers 2001.01127, arXiv.org.
• Andres Quiros-Granados & JAvier Trejos-Zelaya, 2019. "Estimation of the yield curve for Costa Rica using combinatorial optimization metaheuristics applied to nonlinear regression," Papers 2001.00920, arXiv.org.
• Henry Hanifan & John Cartlidge, 2019. "Fools Rush In: Competitive Effects of Reaction Time in Automated Trading," Papers 1912.02775, arXiv.org, revised Nov 2020.
• Gauthier, Nicolas, 2019. "Multilevel Simulation of Demography and Food Production in Ancient Agrarian Societies: A Case Study from Roman North Africa," SocArXiv 5be6a, Center for Open Science.
• Ao Kong & Hongliang Zhu & Robert Azencott, 2019. "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Papers 1912.07165, arXiv.org.
• Zolnikov, Pavel & Zubov, Maxim & Nikitinsky, Nikita & Makarov, Ilya, 2019. "Efficient Algorithms for Constructing Multiplex Networks Embedding," MPRA Paper 97310, University Library of Munich, Germany, revised 23 Sep 2019.
• Lynn Boen & Karel J. in 't Hout, 2019. "Operator splitting schemes for American options under the two-asset Merton jump-diffusion model," Papers 1912.06809, arXiv.org.
• Akimov, Dmitry & Makarov, Ilya, 2019. "Deep Reinforcement Learning with VizDoomFirst-Person Shooter," MPRA Paper 97307, University Library of Munich, Germany, revised 23 Sep 2019.
• Anna Knezevic & Nikolai Dokuchaev, 2019. "Approximating intractable short ratemodel distribution with neural network," Papers 1912.12615, arXiv.org, revised Apr 2024.
• Christian Tilk & Michael Forbes, 2019. "Branch-and-Cut for the Active-Passive Vehicle Routing Problem," Working Papers 1915, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz.
• Ronaldo Carpio & Takashi Kamihigashi, 2019. "Fast Value Iteration: An Application of Legendre-Fenchel Duality to a Class of Deterministic Dynamic Programming Problems in Discrete Time," Discussion Paper Series DP2019-24, Research Institute for Economics & Business Administration, Kobe University.
• Tianyi Liu & Enlu Zhou, 2019. "Online Quantification of Input Model Uncertainty by Two-Layer Importance Sampling," Papers 1912.11172, arXiv.org, revised Feb 2020.
• Xi Chen & Ye Luo & Martin Spindler, 2019. "Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data," Papers 1912.12867, arXiv.org, revised Jan 2020.
• Sebastian Rojas Gonzalez & Inneke Van Nieuwenhuyse, 2019. "A survey on kriging-based infill algorithms for multiobjective simulation optimization," Working Papers of Department of Decision Sciences and Information Management, Leuven 634831, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven.
• Joseph F. DeCarolis & Samaneh Babaee & Binghui Li & Suyash Kanungo, 2019. "Energy Scenario Exploration with Modeling to Generate Alternatives (MGA)," Papers 1912.03788, arXiv.org.
• Michael, Friedrich & Ignatov, Dmitry I., 2019. "General Game Playing B-to-B Price Negotiations," MPRA Paper 97313, University Library of Munich, Germany, revised 23 Sep 2019.
• Ekaterina Semenova & Ekaterina Perevoshchikova & Alexey Ivanov & Mikhail Erofeev, 2019. "Fairness Meets Machine Learning: Searching For A Better Balance," HSE Working papers WP BRP 93/LAW/2019, National Research University Higher School of Economics.
• Wenhang Bao, 2019. "Fairness in Multi-agent Reinforcement Learning for Stock Trading," Papers 2001.00918, arXiv.org.
• Reinhard Neck & Dmitri Blueschke & Viktoria Blueschke-Nikolaeva, 2019. "Finite Horizon Dynamic Games with and without a Scrap Value," Proceedings of International Academic Conferences 9712064, International Institute of Social and Economic Sciences.
• Leonardo Gambacorta & Yiping Huang & Han Qiu & Jingyi Wang, 2019. "How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm," BIS Working Papers 834, Bank for International Settlements.