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Report NEP-CMP-2020-11-16
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:
- Stefan Kremsner & Alexander Steinicke & Michaela Szolgyenyi, 2020, "A deep neural network algorithm for semilinear elliptic PDEs with applications in insurance mathematics," Papers, arXiv.org, number 2010.15757, Oct, revised Dec 2020.
- Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2020, "Deep Learning for Individual Heterogeneity," Papers, arXiv.org, number 2010.14694, Oct, revised Apr 2025.
- Takanori Sakai & Yusuke Hara & Ravi Seshadri & Andr'e Alho & Md Sami Hasnine & Peiyu Jing & ZhiYuan Chua & Moshe Ben-Akiva, 2020, "E-Commerce Delivery Demand Modeling Framework for An Agent-Based Simulation Platform," Papers, arXiv.org, number 2010.14375, Oct.
- Sidra Mehtab & Jaydip Sen, 2020, "Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models," Papers, arXiv.org, number 2010.13891, Oct.
- Katsafados, Apostolos G. & Androutsopoulos, Ion & Chalkidis, Ilias & Fergadiotis, Manos & Leledakis, George N. & Pyrgiotakis, Emmanouil G., 2020, "Textual Information and IPO Underpricing: A Machine Learning Approach," MPRA Paper, University Library of Munich, Germany, number 103813, Oct.
- Isao Yagi & Shunya Maruyama & Takanobu Mizuta, 2020, "Trading Strategies of a Leveraged ETF in a Continuous Double Auction Market Using an Agent-Based Simulation," Papers, arXiv.org, number 2010.13036, Oct.
- Andrés Alonso & José Manuel Carbó, 2020, "Machine learning in credit risk: measuring the dilemma between prediction and supervisory cost," Working Papers, Banco de España, number 2032, Oct.
- Comincioli, Nicola & Panteghini, Paolo M. & Vergalli, Sergio, , "Debt and Transfer Pricing: Implications on Business Tax Policy," 2030 Agenda, Fondazione Eni Enrico Mattei (FEEM), number 307307, DOI: 10.22004/ag.econ.307307.
- Antti J. Tanskanen, 2020, "Deep reinforced learning enables solving rich discrete-choice life cycle models to analyze social security reforms," Papers, arXiv.org, number 2010.13471, Oct, revised Feb 2022.
- Dongming Wei & Yogi Ahmad Erlangga & Gulzat Zhumakhanova, 2020, "A Finite Element Approach to the Numerical Solutions of Leland's Mode," Papers, arXiv.org, number 2010.13541, Oct.
- Aur'elien Alfonsi & Adel Cherchali & Jose Arturo Infante Acevedo, 2020, "Multilevel Monte-Carlo for computing the SCR with the standard formula and other stress tests," Papers, arXiv.org, number 2010.12651, Oct, revised Apr 2021.
- Michael Allan Ribers & Hannes Ullrich, 2020, "Machine Predictions and Human Decisions with Variation in Payoffs and Skills," Discussion Papers of DIW Berlin, DIW Berlin, German Institute for Economic Research, number 1911.
- Andrea Borsato, 2020, "Secular Stagnation and innovation dynamics: an agent-based SFC model. Part I," Department of Economics University of Siena, Department of Economics, University of Siena, number 840, Sep.
- Perone, G., 2020, "Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," Health, Econometrics and Data Group (HEDG) Working Papers, HEDG, c/o Department of Economics, University of York, number 20/18, Nov.
- Isao Yagi & Yuji Masuda & Takanobu Mizuta, 2020, "Analysis of the Impact of High-Frequency Trading on Artificial Market Liquidity," Papers, arXiv.org, number 2010.13038, Oct.
- Kristoffer Andersson & Cornelis W. Oosterlee, 2020, "Deep learning for CVA computations of large portfolios of financial derivatives," Papers, arXiv.org, number 2010.13843, Oct.
- A. Georgantas, 2020, "Robust Optimization Approaches for Portfolio Selection: A Computational and Comparative Analysis," Papers, arXiv.org, number 2010.13397, Oct.