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. 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:

• Nguyen, Phong Thanh, 2020. "Application Machine Learning in Construction Management," MPRA Paper 109899, University Library of Munich, Germany, 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 2109.12621, arXiv.org.
• Bali, Turan G. & Beckmeyer, Heiner & Moerke, Mathis & Weigert, Florian, 2021. "Option return predictability with machine learning and big data," CFR Working Papers 21-08, University of Cologne, Centre for Financial Research (CFR).
• Ioanna Arkoudi & Carlos Lima Azevedo & Francisco C. Pereira, 2021. "Combining Discrete Choice Models and Neural Networks through Embeddings: Formulation, Interpretability and Performance," Papers 2109.12042, arXiv.org, revised Sep 2021.
• G. Mazzei & F. G. Bellora & J. A. Serur, 2021. "Delta Hedging with Transaction Costs: Dynamic Multiscale Strategy using Neural Nets," Papers 2109.12337, arXiv.org.
• Ye-Sheen Lim & Denise Gorse, 2021. "Intra-Day Price Simulation with Generative Adversarial Modelling of the Order Flow," Papers 2109.13905, arXiv.org.
• 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 109838, University Library of Munich, Germany.
• 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 thg23, Center for Open Science.
• Ali Al-Ameer & Khaled Alshehri, 2021. "Conditional Value-at-Risk for Quantitative Trading: A Direct Reinforcement Learning Approach," Papers 2109.14438, arXiv.org.
• Shuo Sun & Rundong Wang & Bo An, 2021. "Reinforcement Learning for Quantitative Trading," Papers 2109.13851, arXiv.org.
• Schulz, Jan & Mayerhoffer, Daniel M., 2021. "A network approach to consumption," BERG Working Paper Series 173, Bamberg University, Bamberg Economic Research Group.
• Damián Pierri, 2021. "Useful Results for the Simulation of Non-Optimal Economies with Heterogeneous Agents," Working Papers 156, Universidad de San Andres, Departamento de Economia, revised Aug 2021.