Report NEP-CMP-2019-08-26

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:

• Fatima Zahra Azayite & Said Achchab, 2019. "A hybrid neural network model based on improved PSO and SA for bankruptcy prediction," Papers 1907.12179, arXiv.org.
• Loermann, Julius & Maas, Benedikt, 2019. "Nowcasting US GDP with artificial neural networks," MPRA Paper 95459, University Library of Munich, Germany.
• Auclert, Adrien & Bardoczy, Bence & Rognlie, Matthew & Straub, Ludwig, 2019. "Using the Sequence-Space Jacobian to Solve and Estimate Heterogeneous-Agent Models," CEPR Discussion Papers 13890, C.E.P.R. Discussion Papers.
• Ningyuan Chen & Guillermo Gallego & Zhuodong Tang, 2019. "The Use of Binary Choice Forests to Model and Estimate Discrete Choices," Papers 1908.01109, arXiv.org, revised Apr 2024.
• Bucci, Andrea, 2019. "Realized Volatility Forecasting with Neural Networks," MPRA Paper 95443, University Library of Munich, Germany.
• Seruca, Manuel & Mota, Andrade & Rodrigues, David, 2019. "Solving the Economic Scheduling of Grid-Connected Microgrid Based on the Strength Pareto Approach," MPRA Paper 95391, University Library of Munich, Germany.
• Jin, Ding & Hedtrich, Johannes & Henning, Christian H. C. A., 2018. "Applying meta-modeling for extended CGE-modeling: Sampling techniques and potential application," Working Papers of Agricultural Policy WP2018-03, University of Kiel, Department of Agricultural Economics, Chair of Agricultural Policy.
• Mahdavi, Sadegh & Bayat, Alireza & Khazaei, Ehsan & Jamaledini, Ashkan, 2019. "Economic Operation of Self-Sustained Microgrid Optimal Operation by Multiobjective Evolutionary Algorithm Based on Decomposition," MPRA Paper 95393, University Library of Munich, Germany.
• Shan Huang, 2019. "Taxable Stock Trading with Deep Reinforcement Learning," Papers 1907.12093, arXiv.org, revised Jul 2019.
• Badruddoza, Syed & Amin, Modhurima D., 2019. "Determining the Importance of an Attribute in a Demand System: Structural versus Machine Learning Approach," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 291210, Agricultural and Applied Economics Association.
• Haoran Wang, 2019. "Large scale continuous-time mean-variance portfolio allocation via reinforcement learning," Papers 1907.11718, arXiv.org, revised Aug 2019.
• Shen, Ze & Wan, Qing & Leatham, David J., 2019. "Bitcoin Return Volatility Forecasting: A Comparative Study of GARCH Model and Machine Learning Model," 2019 Annual Meeting, July 21-23, Atlanta, Georgia 290696, Agricultural and Applied Economics Association.
• Arno Botha & Conrad Beyers & Pieter de Villiers, 2019. "A procedure for loss-optimising default definitions across simulated credit risk scenarios," Papers 1907.12615, arXiv.org, revised Feb 2021.
• Anna Stelzer, 2019. "Predicting credit default probabilities using machine learning techniques in the face of unequal class distributions," Papers 1907.12996, arXiv.org.
• Subhojit Biswas & Diganta Mukherjee, 2019. "Discrete time portfolio optimisation managing value at risk under heavy tail return distribution," Papers 1908.03907, arXiv.org, revised Nov 2020.
• Jong Jun Park & Kyungsub Lee, 2019. "Computational method for probability distribution on recursive relationships in financial applications," Papers 1908.04959, arXiv.org.
• Bastien Baldacci & Philippe Bergault & Olivier Gu'eant, 2019. "Algorithmic market making for options," Papers 1907.12433, arXiv.org, revised Jul 2020.