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. 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:

• Aniruddha Dutta & Saket Kumar & Meheli Basu, 2019, "A Gated Recurrent Unit Approach to Bitcoin Price Prediction," Papers, arXiv.org, number 1912.11166, Dec.
• 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, arXiv.org, number 1912.04242, Dec.
• Dennis Kristensen & Patrick K. Mogensen & Jong-Myun Moon & Bertel Schjerning, 2019, "Solving dynamic discrete choice models using smoothing and sieve methods," CeMMAP working papers, Centre for Microdata Methods and Practice, Institute for Fiscal Studies, number CWP15/19, Apr.
• 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, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 2019-3, Jan.
• Melinscak, Filip & Bach, Dominik R, 2019, "Computational optimization of associative learning experiments," OSF Preprints, Center for Open Science, number cgpmh, Feb, DOI: 10.31219/osf.io/cgpmh.
• Bräuning, Michael & Malikkidou, Despo & Scricco, Giorgio & Scalone, Stefano, 2019, "A new approach to Early Warning Systems for small European banks," Working Paper Series, European Central Bank, number 2348, Dec.
• Mirko Moscatelli & Simone Narizzano & Fabio Parlapiano & Gianluca Viggiano, 2019, "Corporate default forecasting with machine learning," Temi di discussione (Economic working papers), Bank of Italy, Economic Research and International Relations Area, number 1256, Dec.
• 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, arXiv.org, number 1912.04015, Dec, revised Sep 2020.
• Benkovich, Nikita & Dedenok, Roman & Golubev, Dmitry, 2019, "Deep Quarantine for Suspicious Mail," MPRA Paper, University Library of Munich, Germany, number 97311, Sep, revised 23 Sep 2019.
• Jie Fang & Shutao Xia & Jianwu Lin & Yong Jiang, 2019, "Automatic Financial Feature Construction," Papers, arXiv.org, number 1912.06236, Dec, 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, arXiv.org, number 2001.01127, Jan.
• Andres Quiros-Granados & JAvier Trejos-Zelaya, 2019, "Estimation of the yield curve for Costa Rica using combinatorial optimization metaheuristics applied to nonlinear regression," Papers, arXiv.org, number 2001.00920, Nov.
• Henry Hanifan & John Cartlidge, 2019, "Fools Rush In: Competitive Effects of Reaction Time in Automated Trading," Papers, arXiv.org, number 1912.02775, Dec, 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, Center for Open Science, number 5be6a, Aug, DOI: 10.31219/osf.io/5be6a.
• Ao Kong & Hongliang Zhu & Robert Azencott, 2019, "Predicting intraday jumps in stock prices using liquidity measures and technical indicators," Papers, arXiv.org, number 1912.07165, Dec.
• Zolnikov, Pavel & Zubov, Maxim & Nikitinsky, Nikita & Makarov, Ilya, 2019, "Efficient Algorithms for Constructing Multiplex Networks Embedding," MPRA Paper, University Library of Munich, Germany, number 97310, Sep, 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, arXiv.org, number 1912.06809, Dec.
• Akimov, Dmitry & Makarov, Ilya, 2019, "Deep Reinforcement Learning with VizDoomFirst-Person Shooter," MPRA Paper, University Library of Munich, Germany, number 97307, Sep, revised 23 Sep 2019.
• Anna Knezevic & Nikolai Dokuchaev, 2019, "Approximating intractable short ratemodel distribution with neural network," Papers, arXiv.org, number 1912.12615, Dec, revised Apr 2024.
• Christian Tilk & Michael Forbes, 2019, "Branch-and-Cut for the Active-Passive Vehicle Routing Problem," Working Papers, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, number 1915, Dec.
• 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, Research Institute for Economics & Business Administration, Kobe University, number DP2019-24, Dec.
• Tianyi Liu & Enlu Zhou, 2019, "Online Quantification of Input Model Uncertainty by Two-Layer Importance Sampling," Papers, arXiv.org, number 1912.11172, Dec, revised Feb 2020.
• Xi Chen & Ye Luo & Martin Spindler, 2019, "Adaptive Discrete Smoothing for High-Dimensional and Nonlinear Panel Data," Papers, arXiv.org, number 1912.12867, Dec, 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, KU Leuven, Faculty of Economics and Business (FEB), Department of Decision Sciences and Information Management, Leuven, number 634831, Mar.
• Joseph F. DeCarolis & Samaneh Babaee & Binghui Li & Suyash Kanungo, 2019, "Energy Scenario Exploration with Modeling to Generate Alternatives (MGA)," Papers, arXiv.org, number 1912.03788, Dec.
• Michael, Friedrich & Ignatov, Dmitry I., 2019, "General Game Playing B-to-B Price Negotiations," MPRA Paper, University Library of Munich, Germany, number 97313, Sep, 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, National Research University Higher School of Economics, number WP BRP 93/LAW/2019.
• Wenhang Bao, 2019, "Fairness in Multi-agent Reinforcement Learning for Stock Trading," Papers, arXiv.org, number 2001.00918, Dec.
• Reinhard Neck & Dmitri Blueschke & Viktoria Blueschke-Nikolaeva, 2019, "Finite Horizon Dynamic Games with and without a Scrap Value," Proceedings of International Academic Conferences, International Institute of Social and Economic Sciences, number 9712064, Oct.
• 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, Bank for International Settlements, number 834, Dec.

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. 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: