Report NEP-CMP-2023-07-31

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:

• Juan Esteban Berger, 2023. "Pricing European Options with Google AutoML, TensorFlow, and XGBoost," Papers 2307.00476, arXiv.org.
• David Noel, 2023. "Stock Price Prediction using Dynamic Neural Networks," Papers 2306.12969, arXiv.org.
• Gerhard Hellstern & Vanessa Dehn & Martin Zaefferer, 2023. "Quantum computer based Feature Selection in Machine Learning," Papers 2306.10591, arXiv.org.
• Marc Chen & Mohammad Shirazi & Peter A. Forsyth & Yuying Li, 2023. "Machine Learning and Hamilton-Jacobi-Bellman Equation for Optimal Decumulation: a Comparison Study," Papers 2306.10582, arXiv.org.
• Stempel, Daniel & Zahner, Johannes, 2023. "Whose inflation rates matter most? A DSGE model and machine learning approach to monetary policy in the Euro area," IMFS Working Paper Series 188, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS).
• Pumplun, Luisa & Peters, Felix & Gawlitza, Joshua & Buxmann, Peter, 2023. "Bringing Machine Learning Systems into Clinical Practice: A Design Science Approach to Explainable Machine Learning-Based Clinical Decision Support Systems," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 138523, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Marc Velay & Bich-Li^en Doan & Arpad Rimmel & Fabrice Popineau & Fabrice Daniel, 2023. "Benchmarking Robustness of Deep Reinforcement Learning approaches to Online Portfolio Management," Papers 2306.10950, arXiv.org.
• Wenbo Ge & Pooia Lalbakhsh & Leigh Isai & Artem Lensky & Hanna Suominen, 2023. "Comparing Deep Learning Models for the Task of Volatility Prediction Using Multivariate Data," Papers 2306.12446, arXiv.org, revised Jun 2023.
• Johanna Deperi & Ludovic Dibiaggio & Mohamed Keita & Lionel Nesta, 2023. "Ideas Without Scale in French Artificial Intelligence Innovations," SciencePo Working papers Main hal-04144817, HAL.
• Ellenrieder, Sara & Jourdan, Nicolas & Biegel, Tobias & Bretones Cassoli, Beatriz & Metternich, Joachim & Buxmann, Peter, 2023. "Toward the sustainable development of machine learning applications in Industry 4.0," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 138521, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Arnau Quera-Bofarull & Joel Dyer & Anisoara Calinescu & Michael Wooldridge, 2023. "Some challenges of calibrating differentiable agent-based models," Papers 2307.01085, arXiv.org.
• Yang Qiao & Yiping Xia & Xiang Li & Zheng Li & Yan Ge, 2023. "Higher-order Graph Attention Network for Stock Selection with Joint Analysis," Papers 2306.15526, arXiv.org.
• Joel Ong & Dorien Herremans, 2023. "Constructing Time-Series Momentum Portfolios with Deep Multi-Task Learning," Papers 2306.13661, arXiv.org.
• Sherly Alfonso-S'anchez & Jes'us Solano & Alejandro Correa-Bahnsen & Kristina P. Sendova & Cristi'an Bravo, 2023. "Optimizing Credit Limit Adjustments Under Adversarial Goals Using Reinforcement Learning," Papers 2306.15585, arXiv.org, revised Feb 2024.
• Severin Reissl & Alessandro Caiani & Francesco Lamperti & Mattia Guerini & Fabio Vanni & Giorgio Fagiolo & Tommaso Ferraresi & Leonardo Ghezzi & Mauro Napoletano & Andrea Roventini, 2021. "Assessing the Economic Impact of Lockdowns in Italy: A Computational Input-Output Approach," Working Papers hal-04103906, HAL.
• Yu-Chin Hsu & Martin Huber & Yu-Min Yen, 2023. "Doubly Robust Estimation of Direct and Indirect Quantile Treatment Effects with Machine Learning," Papers 2307.01049, arXiv.org.
• Gideon du Rand & Hylton Hollander & Dawie van Lill, 2023. "A deep learning approach to estimation of the Phillips curve in South Africa," WIDER Working Paper Series wp-2023-79, World Institute for Development Economic Research (UNU-WIDER).
• Haohan Zhang & Fengrui Hua & Chengjin Xu & Hao Kong & Ruiting Zuo & Jian Guo, 2023. "Unveiling the Potential of Sentiment: Can Large Language Models Predict Chinese Stock Price Movements?," Papers 2306.14222, arXiv.org, revised May 2024.
• Gianluca Cubadda & Francesco Giancaterini & Alain Hecq & Joann Jasiak, 2023. "Optimization of the Generalized Covariance Estimator in Noncausal Processes," Papers 2306.14653, arXiv.org, revised Jan 2024.
• Abha Naik & Esra Yeniaras & Gerhard Hellstern & Grishma Prasad & Sanjay Kumar Lalta Prasad Vishwakarma, 2023. "From Portfolio Optimization to Quantum Blockchain and Security: A Systematic Review of Quantum Computing in Finance," Papers 2307.01155, arXiv.org.
• Mingxiao Song & Yunsong Liu & Agam Shah & Sudheer Chava, 2023. "Abnormal Trading Detection in the NFT Market," Papers 2306.04643, arXiv.org, revised Aug 2023.
• Manuel Alejandro Cardenete & M. Carmen Lima & Ferran Sancho, 2023. "A methodology to study price-quantity interactions in input-output modeling: an application to NextGenerationEU funds," UFAE and IAE Working Papers 973.23, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
• Boyu Zhang & Hongyang Yang & Xiao-Yang Liu, 2023. "Instruct-FinGPT: Financial Sentiment Analysis by Instruction Tuning of General-Purpose Large Language Models," Papers 2306.12659, arXiv.org.
• Item repec:ags:aaea22:335950 is not listed on IDEAS anymore

• Cong Zheng & Jiafa He & Can Yang, 2023. "Optimal Execution Using Reinforcement Learning," Papers 2306.17178, arXiv.org.