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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. 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:
- Fatima Zahra Azayite & Said Achchab, 2019, "A hybrid neural network model based on improved PSO and SA for bankruptcy prediction," Papers, arXiv.org, number 1907.12179, Jul.
- Loermann, Julius & Maas, Benedikt, 2019, "Nowcasting US GDP with artificial neural networks," MPRA Paper, University Library of Munich, Germany, number 95459, May.
- Auclert, Adrien & Bardoczy, Bence & Rognlie, Matthew & Straub, Ludwig, 2019, "Using the Sequence-Space Jacobian to Solve and Estimate Heterogeneous-Agent Models," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 13890, Jul.
- Ningyuan Chen & Guillermo Gallego & Zhuodong Tang, 2019, "The Use of Binary Choice Forests to Model and Estimate Discrete Choices," Papers, arXiv.org, number 1908.01109, Aug, revised Oct 2025.
- Bucci, Andrea, 2019, "Realized Volatility Forecasting with Neural Networks," MPRA Paper, University Library of Munich, Germany, number 95443, Aug.
- Seruca, Manuel & Mota, Andrade & Rodrigues, David, 2019, "Solving the Economic Scheduling of Grid-Connected Microgrid Based on the Strength Pareto Approach," MPRA Paper, University Library of Munich, Germany, number 95391.
- 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, University of Kiel, Department of Agricultural Economics, Chair of Agricultural Policy, number WP2018-03.
- 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, University Library of Munich, Germany, number 95393.
- Shan Huang, 2019, "Taxable Stock Trading with Deep Reinforcement Learning," Papers, arXiv.org, number 1907.12093, Jul, revised Jul 2019.
- Badruddoza, Syed & Amin, Modhurima D., , "Determining the Importance of an Attribute in a Demand System: Structural versus Machine Learning Approach," 2019 Annual Meeting, July 21-23, Atlanta, Georgia, Agricultural and Applied Economics Association, number 291210, DOI: 10.22004/ag.econ.291210.
- Haoran Wang, 2019, "Large scale continuous-time mean-variance portfolio allocation via reinforcement learning," Papers, arXiv.org, number 1907.11718, Jul, revised Aug 2019.
- Shen, Ze & Wan, Qing & Leatham, David J., , "Bitcoin Return Volatility Forecasting: A Comparative Study of GARCH Model and Machine Learning Model," 2019 Annual Meeting, July 21-23, Atlanta, Georgia, Agricultural and Applied Economics Association, number 290696, DOI: 10.22004/ag.econ.290696.
- Arno Botha & Conrad Beyers & Pieter de Villiers, 2019, "A procedure for loss-optimising default definitions across simulated credit risk scenarios," Papers, arXiv.org, number 1907.12615, Jul, revised Feb 2021.
- Anna Stelzer, 2019, "Predicting credit default probabilities using machine learning techniques in the face of unequal class distributions," Papers, arXiv.org, number 1907.12996, Jul.
- Subhojit Biswas & Diganta Mukherjee, 2019, "Discrete time portfolio optimisation managing value at risk under heavy tail return distribution," Papers, arXiv.org, number 1908.03907, Aug, revised Nov 2020.
- Jong Jun Park & Kyungsub Lee, 2019, "Computational method for probability distribution on recursive relationships in financial applications," Papers, arXiv.org, number 1908.04959, Aug.
- Bastien Baldacci & Philippe Bergault & Olivier Gu'eant, 2019, "Algorithmic market making for options," Papers, arXiv.org, number 1907.12433, Jul, revised Jul 2020.