Report NEP-CMP-2023-10-16

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:

• Gianluca Fabiani & Nikolaos Evangelou & Tianqi Cui & Juan M. Bello-Rivas & Cristina P. Martin-Linares & Constantinos Siettos & Ioannis G. Kevrekidis, 2023. "Tasks Makyth Models: Machine Learning Assisted Surrogates for Tipping Points," Papers 2309.14334, arXiv.org.
• Caravaggio, Nicola & Resce, Giuliano, 2023. "Enhancing Healthcare Cost Forecasting: A Machine Learning Model for Resource Allocation in Heterogeneous Regions," Economics & Statistics Discussion Papers esdp23090, University of Molise, Department of Economics.
• Achim Ahrens & Christian B. Hansen & Mark E. Schaffer & Thomas Wiemann, 2023. "pystacked and ddml: machine learning for prediction and causal inference in Stata," UK Stata Conference 2023 12, Stata Users Group.
• Foozhan Ataiefard & Hadi Hemmati, 2023. "Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents," Papers 2309.14615, arXiv.org.
• Paul Bilokon & Oleksandr Bilokon & Saeed Amen, 2023. "A compendium of data sources for data science, machine learning, and artificial intelligence," Papers 2309.05682, arXiv.org.
• Douglas Kiarelly Godoy de Araujo, 2023. "gingado: a machine learning library focused on economics and finance," BIS Working Papers 1122, Bank for International Settlements.
• Johan Brannlund & Helen Lao & Maureen MacIsaac & Jing Yang, 2023. "Predicting Changes in Canadian Housing Markets with Machine Learning," Discussion Papers 2023-21, Bank of Canada.
• Zhou, Yunzhe & Qi, Zhengling & Shi, Chengchun & Li, Lexin, 2023. "Optimizing pessimism in dynamic treatment regimes: a Bayesian learning approach," LSE Research Online Documents on Economics 118233, London School of Economics and Political Science, LSE Library.
• Jonas Hanetho, 2023. "Commodities Trading through Deep Policy Gradient Methods," Papers 2309.00630, arXiv.org.
• Udit Gupta, 2023. "GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models," Papers 2309.03079, arXiv.org.
• Molei Qin & Shuo Sun & Wentao Zhang & Haochong Xia & Xinrun Wang & Bo An, 2023. "EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading," Papers 2309.12891, arXiv.org.
• Giovanni Cerulli, 2023. "A Review of Machine Learning Commands in Stata: Performance and Usability Evaluation," UK Stata Conference 2023 08, Stata Users Group.
• Kumar, Rishabh & Koshiyama, Adriano & da Costa, Kleyton & Kingsman, Nigel & Tewarrie, Marvin & Kazim, Emre & Roy, Arunita & Treleaven, Philip & Lovell, Zac, 2023. "Deep learning model fragility and implications for financial stability and regulation," Bank of England working papers 1038, Bank of England.
• Sahed Abdelkader & Kahoui Hacene, 2023. "Electricity Consumption Forecasting in Algeria using ARIMA and Long Short-Term Memory Neural Network," Post-Print hal-04183403, HAL.
• Peer Nagy & Sascha Frey & Silvia Sapora & Kang Li & Anisoara Calinescu & Stefan Zohren & Jakob Foerster, 2023. "Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Network," Papers 2309.00638, arXiv.org.
• Ian R White & Tra My Pham & Matteo Quartagno & Tim P Morris, 2023. "How to check a simulation study," UK Stata Conference 2023 16, Stata Users Group.
• Lambrecht, Marco & Oechssler, Jörg & Weidenholzer, Simon, 2023. "On the benefits of robo-advice in financial markets," Working Papers 0734, University of Heidelberg, Department of Economics.
• Chassonnery-Zaïgouche, Cléo & Goutsmedt, Aurélien, 2023. "Modeling intervention: The Political element in Barbara Bergmann's micro-to-macro simulation projects," SocArXiv ynmbe, Center for Open Science.
• Bauer, Kevin & Liebich, Lena & Hinz, Oliver & Kosfeld, Michael, 2023. "Decoding GPT's hidden "rationality" of cooperation," SAFE Working Paper Series 401, Leibniz Institute for Financial Research SAFE.
• Gadat, Sébastien & Villeneuve, Stéphane, 2023. "Parsimonious Wasserstein Text-mining," TSE Working Papers 23-1471, Toulouse School of Economics (TSE).
• Xiyuan Ren & Joseph Y. J. Chow & Prateek Bansal, 2023. "Estimating a k-modal nonparametric mixed logit model with market-level data," Papers 2309.13159, arXiv.org, revised Aug 2024.
• Pawe{l} Niszczota & Sami Abbas, 2023. "GPT has become financially literate: Insights from financial literacy tests of GPT and a preliminary test of how people use it as a source of advice," Papers 2309.00649, arXiv.org, revised Sep 2024.
• Fahmida Khatun & Nadia Nawrin, 2021. "Artificial Intelligence and Its Impact on Information Technology (IT) Service Sector in Bangladesh," CPD Report 17, Centre for Policy Dialogue (CPD).
• Tae-Hwy Lee & Ekaterina Seregina, 2023. "Combining Forecasts under Structural Breaks Using Graphical LASSO," Working Papers 202310, University of California at Riverside, Department of Economics.
• Yi Yang & Yixuan Tang & Kar Yan Tam, 2023. "InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning," Papers 2309.13064, arXiv.org.