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Report NEP-CMP-2019-09-02
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:
- Mossad, Omar S. & ElNainay, Mustafa & Torki, Marwan, 2019, "Modulations Recognition using Deep Neural Network in Wireless Communications," 2nd Europe – Middle East – North African Regional ITS Conference, Aswan 2019: Leveraging Technologies For Growth, International Telecommunications Society (ITS), number 201750.
- Jang, Youngsoo & Lee, Soyoung, 2019, "A Generalized Endogenous Grid Method for Models with the Option to Default," MPRA Paper, University Library of Munich, Germany, number 95721, Aug.
- Lisa-Cheree Martin, 2019, "Machine Learning vs Traditional Forecasting Methods: An Application to South African GDP," Working Papers, Stellenbosch University, Department of Economics, number 12/2019.
- Michel Alexandre & Gilberto Tadeu Lima, 2019, "Macroeconomic Impacts of Trade Credit: An Agent-Based Modeling Exploration," Working Papers, Department of Economics, University of São Paulo (FEA-USP), number 2019_31, Aug.
- Iordanis Kerenidis & Anupam Prakash & D'aniel Szil'agyi, 2019, "Quantum Algorithms for Portfolio Optimization," Papers, arXiv.org, number 1908.08040, Aug.
- Roos Elizabeth & Adams Philip, 2019, "Fiscal Reform – Aid or Hindrance: A Computable General Equilibrium (CGE) Analysis for Saudi Arabia," Working Papers, Economic Research Forum, number 1317, Aug, revised 21 Aug 2019.
- Manel Hamdi & Walid Chkili, 2019, "An artificial neural network augmented GARCH model for Islamic stock market volatility: Do asymmetry and long memory matter?," Working Papers, Economic Research Forum, number 13, Aug, revised 21 Aug 2019.
- David Byrd & Tucker Hybinette Balch, 2019, "Intra-day Equity Price Prediction using Deep Learning as a Measure of Market Efficiency," Papers, arXiv.org, number 1908.08168, Aug.
- Songul Tolan, 2018, "Fair and Unbiased Algorithmic Decision Making: Current State and Future Challenges," JRC Working Papers on Digital Economy, Joint Research Centre, number 2018-10, Dec.
- Lotfi Boudabsa & Damir Filipović, 2019, "Machine Learning With Kernels for Portfolio Valuation and Risk Management," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 19-34, Jun.
- Christian Bayer & Blanka Horvath & Aitor Muguruza & Benjamin Stemper & Mehdi Tomas, 2019, "On deep calibration of (rough) stochastic volatility models," Papers, arXiv.org, number 1908.08806, Aug.
- Nicola Cufaro Petroni & Piergiacomo Sabino, 2019, "Fast Pricing of Energy Derivatives with Mean-reverting Jump-diffusion Processes," Papers, arXiv.org, number 1908.03137, Aug, revised Mar 2020.
- Jingyuan Wang & Yang Zhang & Ke Tang & Junjie Wu & Zhang Xiong, 2019, "AlphaStock: A Buying-Winners-and-Selling-Losers Investment Strategy using Interpretable Deep Reinforcement Attention Networks," Papers, arXiv.org, number 1908.02646, Jul.
- Albanesi, Stefania & Vamossy, Domonkos, 2019, "Predicting Consumer Default: A Deep Learning Approach," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 13914, Aug.
- Agnieszka Borowska & Lennart Hoogerheide & Siem Jan Koopman & Herman van Dijk, 2019, "Partially Censored Posterior for Robust and Efficient Risk Evaluation," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 19-057/III, Aug.