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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. 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:
- Jan Grudniewicz & Robert Ślepaczuk, 2021, "Application of machine learning in quantitative investment strategies on global stock markets," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-23.
- Xinyu Li, 2022, "The impact of moving expenses on social segregation: a simulation with RL and ABM," Papers, arXiv.org, number 2211.12475, Nov.
- Michał Lewandowski & Marcin Chlebus, 2021, "Predicting football outcomes from Spanish league using machine learning models," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-22.
- Amit Chaudhary & Daniele Pinna, 2022, "A multi-asset, agent-based approach applied to DeFi lending protocol modelling," Papers, arXiv.org, number 2211.08870, Nov, 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), Leibniz Universität Hannover, Wirtschaftswissenschaftliche Fakultät, number dp-703, Nov.
- Kea Baret & Amelie Barbier-Gauchard & Theophilos Papadimitriou, 2022, "Forecasting the Stability and Growth Pact compliance using Machine Learning," Working Papers, International Network for Economic Research - INFER, number 2022.11.
- 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, arXiv.org, number 2211.11513, Nov.
- 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, arXiv.org, number 2211.07400, Nov, 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, arXiv.org, number 2211.07956, Nov.
- Chinonso Nwankwo & Nneka Umeorah & Tony Ware & Weizhong Dai, 2022, "Deep learning and American options via free boundary framework," Papers, arXiv.org, number 2211.11803, Nov, revised Dec 2022.
- Roberto Roson, 2022, "Underemployment in a Computable General Equilibrium Model," Working Papers, Department of Economics, University of Venice "Ca' Foscari", number 2022:17.
- Surjaningsih, Ndari & Werdaningtyas, Hesti & Rahman, Faizal & Falaqh, Romadhon, 2022, "Predicting Household Resilience Before and During Pandemic with Classifier Algorithms," OSF Preprints, Center for Open Science, number w5q9g, Jul, DOI: 10.31219/osf.io/w5q9g.
- Erhan Bayraktar & Qi Feng & Zhaoyu Zhang, 2022, "Deep Signature Algorithm for Multi-dimensional Path-Dependent Options," Papers, arXiv.org, number 2211.11691, Nov, 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, Center for Open Science, number 9bu5z, Jul, DOI: 10.31219/osf.io/9bu5z.
- 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, arXiv.org, number 2211.03107, Nov.
- Jan Hagemejer & Maria Dunin-Wąsowicz & Jan Jakub Michałek & Jacek Szyszka, 2021, "Trade-related effects of Brexit. Implications for Central and Eastern Europe," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2021-17.
- 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, arXiv.org, number 2211.11043, Nov.
- Kanazawa, Kyogo & Kawaguchi, Daiji & Shigeoka, Hitoshi & Watanabe, Yasutora, 2022, "AI, Skill, and Productivity: The Case of Taxi Drivers," IZA Discussion Papers, IZA Network @ LISER, number 15677, Oct.
- Zequn Jin & Lihua Lin & Zhengyu Zhang, 2022, "Identification and Auto-debiased Machine Learning for Outcome Conditioned Average Structural Derivatives," Papers, arXiv.org, number 2211.07903, Nov.
- Monks, Thomas & Harper, Alison & Anagnostou, Anastasia & Taylor, Simon J.E., 2022, "Open Science for Computer Simulation," OSF Preprints, Center for Open Science, number zpxtm, Jul, DOI: 10.31219/osf.io/zpxtm.