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Report NEP-CMP-2021-10-04
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:
- Nguyen, Phong Thanh, 2020, "Application Machine Learning in Construction Management," MPRA Paper, University Library of Munich, Germany, number 109899, Dec, revised 01 Aug 2021.
- Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2021, "Multi-Transformer: A New Neural Network-Based Architecture for Forecasting S&P Volatility," Papers, arXiv.org, number 2109.12621, Sep.
- Bali, Turan G. & Beckmeyer, Heiner & Moerke, Mathis & Weigert, Florian, 2021, "Option return predictability with machine learning and big data," CFR Working Papers, University of Cologne, Centre for Financial Research (CFR), number 21-08.
- Ioanna Arkoudi & Carlos Lima Azevedo & Francisco C. Pereira, 2021, "Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance," Papers, arXiv.org, number 2109.12042, Sep, revised Sep 2021.
- G. Mazzei & F. G. Bellora & J. A. Serur, 2021, "Delta Hedging with Transaction Costs: Dynamic Multiscale Strategy using Neural Nets," Papers, arXiv.org, number 2109.12337, Sep.
- Ye-Sheen Lim & Denise Gorse, 2021, "Intra-Day Price Simulation with Generative Adversarial Modelling of the Order Flow," Papers, arXiv.org, number 2109.13905, Sep.
- Olubusoye, Olusanya E & Akintande, Olalekan J. & Yaya, OlaOluwa S. & Ogbonna, Ahamuefula & Adenikinju, Adeola F., 2021, "Energy Pricing during the COVID-19 Pandemic: Predictive Information-Based Uncertainty Indexes with Machine Learning Algorithm," MPRA Paper, University Library of Munich, Germany, number 109838, Sep.
- Angell, Mintaka & Gold, Samantha & Hastings, Justine S. & Howison, Mark & Jensen, Scott & Keleher, Niall & Molitor, Daniel & Roberts, Amelia, 2021, "Estimating value-added returns to labor training programs with causal machine learning," OSF Preprints, Center for Open Science, number thg23, Sep, DOI: 10.31219/osf.io/thg23.
- Ali Al-Ameer & Khaled Alshehri, 2021, "Conditional Value-at-Risk for Quantitative Trading: A Direct Reinforcement Learning Approach," Papers, arXiv.org, number 2109.14438, Sep.
- Shuo Sun & Rundong Wang & Bo An, 2021, "Reinforcement Learning for Quantitative Trading," Papers, arXiv.org, number 2109.13851, Sep.
- Schulz, Jan & Mayerhoffer, Daniel M., 2021, "A network approach to consumption," BERG Working Paper Series, Bamberg University, Bamberg Economic Research Group, number 173.
- Damián Pierri, 2021, "Useful Results for the Simulation of Non-Optimal Economies with Heterogeneous Agents," Working Papers, Universidad de San Andres, Departamento de Economia, number 156, Aug, revised Aug 2021.