Report NEP-CMP-2022-12-12

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:

• Jan Grudniewicz & Robert Ślepaczuk, 2021. "Application of machine learning in quantitative investment strategies on global stock markets," Working Papers 2021-23, Faculty of Economic Sciences, University of Warsaw.
• Xinyu Li, 2022. "The impact of moving expenses on social segregation: a simulation with RL and ABM," Papers 2211.12475, arXiv.org.
• Michał Lewandowski & Marcin Chlebus, 2021. "Predicting football outcomes from Spanish league using machine learning models," Working Papers 2021-22, Faculty of Economic Sciences, University of Warsaw.
• Amit Chaudhary & Daniele Pinna, 2022. "A multi-asset, agent-based approach applied to DeFi lending protocol modelling," Papers 2211.08870, arXiv.org, revised Dec 2022.
• Südbeck, Insa & Mindlina, Julia & Schnabel, André & Helber, Stefan, 2022. "Using Recurrent Neural Networks for the Performance Analysis and Optimization of Stochastic Milkrun-Supplied Flow Lines," Hannover Economic Papers (HEP) dp-703, Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät.
• Kea Baret & Amelie Barbier-Gauchard & Theophilos Papadimitriou, 2022. "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers 2022.11, International Network for Economic Research - INFER.
• Defu Cao & Yousef El-Laham & Loc Trinh & Svitlana Vyetrenko & Yan Liu, 2022. "DSLOB: A Synthetic Limit Order Book Dataset for Benchmarking Forecasting Algorithms under Distributional Shift," Papers 2211.11513, arXiv.org.
• Thanh Trung Huynh & Minh Hieu Nguyen & Thanh Tam Nguyen & Phi Le Nguyen & Matthias Weidlich & Quoc Viet Hung Nguyen & Karl Aberer, 2022. "Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction," Papers 2211.07400, arXiv.org, revised Nov 2022.
• Youru Li & Zhenfeng Zhu & Xiaobo Guo & Shaoshuai Li & Yuchen Yang & Yao Zhao, 2022. "HGV4Risk: Hierarchical Global View-guided Sequence Representation Learning for Risk Prediction," Papers 2211.07956, arXiv.org.
• Chinonso Nwankwo & Nneka Umeorah & Tony Ware & Weizhong Dai, 2022. "Deep learning and American options via free boundary framework," Papers 2211.11803, arXiv.org, revised Dec 2022.
• Roberto Roson, 2022. "Underemployment in a Computable General Equilibrium Model," Working Papers 2022:17, Department of Economics, University of Venice "Ca' Foscari".
• Surjaningsih, Ndari & Werdaningtyas, Hesti & Rahman, Faizal & Falaqh, Romadhon, 2022. "Predicting Household Resilience Before and During Pandemic with Classifier Algorithms," OSF Preprints w5q9g, Center for Open Science.
• Erhan Bayraktar & Qi Feng & Zhaoyu Zhang, 2022. "Deep Signature Algorithm for Multi-dimensional Path-Dependent Options," Papers 2211.11691, arXiv.org, revised Jan 2024.
• Tranos, Emmanouil & Incera, Andre Carrascal & Willis, George, 2022. "Using the web to predict regional trade flows: data extraction, modelling, and validation," OSF Preprints 9bu5z, Center for Open Science.
• Xiao-Yang Liu & Ziyi Xia & Jingyang Rui & Jiechao Gao & Hongyang Yang & Ming Zhu & Christina Dan Wang & Zhaoran Wang & Jian Guo, 2022. "FinRL-Meta: Market Environments and Benchmarks for Data-Driven Financial Reinforcement Learning," Papers 2211.03107, arXiv.org.
• Jan Hagemejer & Maria Dunin-Wąsowicz & Jan Jakub Michałek & Jacek Szyszka, 2021. "Trade-related effects of Brexit. Implications for Central and Eastern Europe," Working Papers 2021-17, Faculty of Economic Sciences, University of Warsaw.
• Dylan Radovic & Lucas Kruitwagen & Christian Schroeder de Witt & Ben Caldecott & Shane Tomlinson & Mark Workman, 2022. "Revealing Robust Oil and Gas Company Macro-Strategies using Deep Multi-Agent Reinforcement Learning," Papers 2211.11043, arXiv.org.
• Kanazawa, Kyogo & Kawaguchi, Daiji & Shigeoka, Hitoshi & Watanabe, Yasutora, 2022. "AI, Skill, and Productivity: The Case of Taxi Drivers," IZA Discussion Papers 15677, Institute of Labor Economics (IZA).
• Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022. "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers 2211.07903, arXiv.org.
• Monks, Thomas & Harper, Alison & Anagnostou, Anastasia & Taylor, Simon J.E., 2022. "Open Science for Computer Simulation," OSF Preprints zpxtm, Center for Open Science.