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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. 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:
- 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, arXiv.org, number 2309.14334, Sep.
- Caravaggio, Nicola & Resce, Giuliano, 2023, "Enhancing Healthcare Cost Forecasting: A Machine Learning Model for Resource Allocation in Heterogeneous Regions," Economics & Statistics Discussion Papers, University of Molise, Department of Economics, number esdp23090, Oct.
- 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, Stata Users Group, number 12, Sep.
- Foozhan Ataiefard & Hadi Hemmati, 2023, "Gray-box Adversarial Attack of Deep Reinforcement Learning-based Trading Agents," Papers, arXiv.org, number 2309.14615, Sep.
- Paul Bilokon & Oleksandr Bilokon & Saeed Amen, 2023, "A compendium of data sources for data science, machine learning, and artificial intelligence," Papers, arXiv.org, number 2309.05682, Sep.
- Douglas Kiarelly Godoy de Araujo, 2023, "gingado: a machine learning library focused on economics and finance," BIS Working Papers, Bank for International Settlements, number 1122, Sep.
- Johan Brannlund & Helen Lao & Maureen MacIsaac & Jing Yang, 2023, "Predicting Changes in Canadian Housing Markets with Machine Learning," Discussion Papers, Bank of Canada, number 2023-21, Sep, DOI: 10.34989/sdp-2023-21.
- 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, London School of Economics and Political Science, LSE Library, number 118233, Dec.
- Jonas Hanetho, 2023, "Commodities Trading through Deep Policy Gradient Methods," Papers, arXiv.org, number 2309.00630, Aug.
- Udit Gupta, 2023, "GPT-InvestAR: Enhancing Stock Investment Strategies through Annual Report Analysis with Large Language Models," Papers, arXiv.org, number 2309.03079, Sep.
- Molei Qin & Shuo Sun & Wentao Zhang & Haochong Xia & Xinrun Wang & Bo An, 2023, "EarnHFT: Efficient Hierarchical Reinforcement Learning for High Frequency Trading," Papers, arXiv.org, number 2309.12891, Sep.
- Giovanni Cerulli, 2023, "A Review of Machine Learning Commands in Stata: Performance and Usability Evaluation," UK Stata Conference 2023, Stata Users Group, number 08, Sep.
- Rishabh Kumar & Adriano Koshiyama & Kleyton da Costa & Nigel Kingsman & Marvin Tewarrie & Emre Kazim & Arunita Roy & Philip Treleaven & Zac Lovell, 2023, "Deep learning model fragility and implications for financial stability and regulation," Bank of England working papers, Bank of England, number 1038, Sep.
- Sahed Abdelkader & Kahoui Hacene, 2023, "Electricity Consumption Forecasting in Algeria using ARIMA and Long Short-Term Memory Neural Network," Post-Print, HAL, number hal-04183403, Jun.
- 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, arXiv.org, number 2309.00638, Aug.
- Ian R White & Tra My Pham & Matteo Quartagno & Tim P Morris, 2023, "How to check a simulation study," UK Stata Conference 2023, Stata Users Group, number 16, Sep.
- Lambrecht, Marco & Oechssler, Jörg & Weidenholzer, Simon, 2023, "On the benefits of robo-advice in financial markets," Working Papers, University of Heidelberg, Department of Economics, number 0734, Sep.
- Chassonnery-Zaïgouche, Cléo & Goutsmedt, Aurélien, 2023, "Modeling intervention: The Political element in Barbara Bergmann's micro-to-macro simulation projects," SocArXiv, Center for Open Science, number ynmbe, Sep, DOI: 10.31219/osf.io/ynmbe.
- Bauer, Kevin & Liebich, Lena & Hinz, Oliver & Kosfeld, Michael, 2023, "Decoding GPT's hidden "rationality" of cooperation," SAFE Working Paper Series, Leibniz Institute for Financial Research SAFE, number 401, DOI: 10.2139/ssrn.4576036.
- Gadat, Sébastien & Villeneuve, Stéphane, 2023, "Parsimonious Wasserstein Text-mining," TSE Working Papers, Toulouse School of Economics (TSE), number 23-1471, Sep.
- Xiyuan Ren & Joseph Y. J. Chow & Prateek Bansal, 2023, "Nonparametric mixed logit model with market-level parameters estimated from market share data," Papers, arXiv.org, number 2309.13159, Sep, revised Apr 2025.
- 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, arXiv.org, number 2309.00649, Aug, revised Sep 2024.
- Fahmida Khatun & Nadia Nawrin, 2021, "Artificial Intelligence and Its Impact on Information Technology (IT) Service Sector in Bangladesh," CPD Report, Centre for Policy Dialogue (CPD), number 17, Nov.
- Tae-Hwy Lee & Ekaterina Seregina, 2023, "Combining Forecasts under Structural Breaks Using Graphical LASSO," Working Papers, University of California at Riverside, Department of Economics, number 202310, Sep.
- Yi Yang & Yixuan Tang & Kar Yan Tam, 2023, "InvestLM: A Large Language Model for Investment using Financial Domain Instruction Tuning," Papers, arXiv.org, number 2309.13064, Sep.