Report NEP-CMP-2023-06-26

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:

• Sinan Deng & John Inekwe & Vladimir Smirnov & Andrew Wait & Chao Wang, 2023. "Machine Learning and Deep Learning Forecasts of Electricity Imbalance Prices," Working Papers 2023-03, University of Sydney, School of Economics.
• Jingjing Guo, 2023. "Gated Deeper Models are Effective Factor Learners," Papers 2305.10693, arXiv.org.
• Ludovic Gouden`ege & Andrea Molent & Antonino Zanette, 2023. "Backward Hedging for American Options with Transaction Costs," Papers 2305.06805, arXiv.org, revised Jun 2023.
• Khuc, Quy Van & Tran, Duc-Trung, 2023. "Contingent valuation machine learning (CVML): A novel method for estimating citizens’ willingness- to- pay for safer and cleaner environment," OSF Preprints r35bz, Center for Open Science.
• Masanori Hirano & Kentaro Imajo & Kentaro Minami & Takuya Shimada, 2023. "Efficient Learning of Nested Deep Hedging using Multiple Options," Papers 2305.12264, arXiv.org.
• Benjamin Fan & Edward Qiao & Anran Jiao & Zhouzhou Gu & Wenhao Li & Lu Lu, 2023. "Deep Learning for Solving and Estimating Dynamic Macro-Finance Models," Papers 2305.09783, arXiv.org.
• Michael Allan Ribers & Hannes Ullrich, 2023. "Machine learning and physician prescribing: a path to reduced antibiotic use," Berlin School of Economics Discussion Papers 0019, Berlin School of Economics.
• Mollen, Anne & Hondrich, Lukas, 2023. "From risk mitigation to employee action along the machine learning pipeline: A paradigm shift in European regulatory perspectives on automated decision-making systems in the workplace," Working Paper Forschungsförderung 278, Hans-Böckler-Stiftung, Düsseldorf.
• Lavko, Matus & Klein, Tony & Walther, Thomas, 2023. "Reinforcement Learning and Portfolio Allocation: Challenging Traditional Allocation Methods," QBS Working Paper Series 2023/01, Queen's University Belfast, Queen's Business School.
• Marcin Chlebus & Artur Nowak, 2023. "From Alchemy to Analytics: Unleashing the Potential of Technical Analysis in Predicting Noble Metal Price Movement," Working Papers 2023-13, Faculty of Economic Sciences, University of Warsaw.
• Pletcher, Scott Nicholas, 2023. "Practical and Ethical Perspectives on AI-Based Employee Performance Evaluation," OSF Preprints 29yej, Center for Open Science.
• Petar Soric & Enric Monte & Salvador Torra & Oscar Claveria, 2022. "“Density forecasts of inflation using Gaussian process regression models”," AQR Working Papers 202207, University of Barcelona, Regional Quantitative Analysis Group, revised Jul 2022.
• Zacharia Issa & Blanka Horvath & Maud Lemercier & Cristopher Salvi, 2023. "Non-adversarial training of Neural SDEs with signature kernel scores," Papers 2305.16274, arXiv.org.
• Maximilian Ahrens & Deniz Erdemlioglu & Michael McMahon & Christopher J. Neely & Xiye Yang, 2023. "Mind Your Language: Market Responses to Central Bank Speeches," Working Papers 2023-013, Federal Reserve Bank of St. Louis, revised 28 Sep 2024.
• Aurélien Alfonsi & Bernard Lapeyre & Jérôme Lelong, 2023. "How many inner simulations to compute conditional expectations with least-square Monte Carlo?," Post-Print hal-03770051, HAL.
• Liu, Weilong & Zhang, Yong & Liu, Kailong & Quinn, Barry & Yang, Xingyu & Peng, Qiao, 2023. "Evolutionary multi-objective optimisation for large-scale portfolio selection with both random and uncertain returns," QBS Working Paper Series 2023/02, Queen's University Belfast, Queen's Business School.
• Abdulnasser Hatemi-J & Alan Mustafa, 2023. "A Simulation Package in VBA to Support Finance Students for Constructing Optimal Portfolios," Papers 2305.12826, arXiv.org.