Report NEP-CMP-2024-01-08

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:

• Matteo Gambara & Giulia Livieri & Andrea Pallavicini, 2023, "Machine-learning regression methods for American-style path-dependent contracts," Papers, arXiv.org, number 2311.16762, Nov, revised Jul 2025.
• Philippe Goulet Coulombe & Mikael Frenette & Karin Klieber, 2023, "From Reactive to Proactive Volatility Modeling with Hemisphere Neural Networks," Papers, arXiv.org, number 2311.16333, Nov, revised Apr 2024.
• Vincent Gurgul & Stefan Lessmann & Wolfgang Karl Hardle, 2023, "Deep Learning and NLP in Cryptocurrency Forecasting: Integrating Financial, Blockchain, and Social Media Data," Papers, arXiv.org, number 2311.14759, Nov, revised Oct 2024.
• Robert Stok & Paul Bilokon, 2023, "From Deep Filtering to Deep Econometrics," Papers, arXiv.org, number 2311.06256, Sep.
• Ruslan Tepelyan & Achintya Gopal, 2023, "Generative Machine Learning for Multivariate Equity Returns," Papers, arXiv.org, number 2311.14735, Nov.
• Colin M. Van Oort & Ethan Ratliff-Crain & Brian F. Tivnan & Safwan Wshah, 2023, "Adaptive Agents and Data Quality in Agent-Based Financial Markets," Papers, arXiv.org, number 2311.15974, Nov.
• Salas Rojo, Pedro & Rodríguez, Juan Gabriel, 2022, "Inheritances and wealth inequality: a machine learning approach," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 120916, Mar.
• kilani, bochra hadj, 2023, "K-Means Clustering algorithms in Urban studies: A Review of Unsupervised Machine Learning techniques," OSF Preprints, Center for Open Science, number bs6wy, Nov, DOI: 10.31219/osf.io/bs6wy.
• Keegan Harris & Nicole Immorlica & Brendan Lucier & Aleksandrs Slivkins, 2023, "Algorithmic Persuasion Through Simulation," Papers, arXiv.org, number 2311.18138, Nov, revised Feb 2025.
• Yangyang Yu & Haohang Li & Zhi Chen & Yuechen Jiang & Yang Li & Denghui Zhang & Rong Liu & Jordan W. Suchow & Khaldoun Khashanah, 2023, "FinMem: A Performance-Enhanced LLM Trading Agent with Layered Memory and Character Design," Papers, arXiv.org, number 2311.13743, Nov, revised Dec 2023.
• Boyang Yu, 2023, "Benchmarking Large Language Model Volatility," Papers, arXiv.org, number 2311.15180, Nov.
• Shalini Sharma & Angshul Majumdar & Emilie Chouzenoux & Victor Elvira, 2023, "Deep State-Space Model for Predicting Cryptocurrency Price," Papers, arXiv.org, number 2311.14731, Nov.
• Cantone, Giulio Giacomo, 2023, "I mildly disagree that our opinions should not be averaged. A commentary on Carpentras and Quayle (2023)," MetaArXiv, Center for Open Science, number 5kzt4, Dec, DOI: 10.31219/osf.io/5kzt4.
• Konstantins Benkovskis & Dzintars Jaunzems & Olegs Matvejevs, 2023, "A Purpose-Based Energy Substitution Structure For CGE," Working Papers, Latvijas Banka, number 2023/07, Dec.
• Joao Guerreiro & Sergio Rebelo & Pedro Teles, 2023, "Regulating Artificial Intelligence," NBER Working Papers, National Bureau of Economic Research, Inc, number 31921, Nov.
• Jen-Yin Yeh & Hsin-Yu Chiu & Jhih-Huei Huang, 2023, "Predicting Failure of P2P Lending Platforms through Machine Learning: The Case in China," Papers, arXiv.org, number 2311.14577, Nov.
• Ummara Mumtaz & Summaya Mumtaz, 2023, "Potential of ChatGPT in predicting stock market trends based on Twitter Sentiment Analysis," Papers, arXiv.org, number 2311.06273, Oct.
• Haoqiang Kang & Xiao-Yang Liu, 2023, "Deficiency of Large Language Models in Finance: An Empirical Examination of Hallucination," Papers, arXiv.org, number 2311.15548, Nov.
• Ruiqi Sun & Daniel Trefler, 2023, "The Impact of AI and Cross-Border Data Regulation on International Trade in Digital Services: A Large Language Model," NBER Working Papers, National Bureau of Economic Research, Inc, number 31925, Nov.
• Hilde C. Bjørnland & Roberto Casarin & Marco Lorusso & Francesco Ravazzolo, 2023, "Fiscal Policy Regimes in Resource-Rich Economies," Working Papers, Centre for Applied Macro- and Petroleum economics (CAMP), BI Norwegian Business School, number No 13/2023, Oct.
• von der Heyde, Leah & Haensch, Anna-Carolina & Wenz, Alexander, 2023, "Assessing Bias in LLM-Generated Synthetic Datasets: The Case of German Voter Behavior," SocArXiv, Center for Open Science, number 97r8s, Dec, DOI: 10.31219/osf.io/97r8s.
• Yuzhakov, Vladimir (Южаков, Владимир) & Talapina, Elvira (Талапина, Эльвира) & Chereshneva, Irina (Черешнева, Ирина), 2022, "The Analysis Of Legal Means To Prevent Fundamental Human Rights Violations Due To The Use Of Artificial Intelligence In Public Administration
  [Анализ Правовых Способов Предотвращения Нарушений Фунд," Working Papers, Russian Presidential Academy of National Economy and Public Administration, number w20220285, Nov.
• Gabriel Turinici & Pierre Brugiere, 2023, "Onflow: an online portfolio allocation algorithm," Papers, arXiv.org, number 2312.05169, Dec.
• Nir Chemaya & Daniel Martin, 2023, "Perceptions and Detection of AI Use in Manuscript Preparation for Academic Journals," Papers, arXiv.org, number 2311.14720, Nov, revised Jan 2024.
• Lijun Bo & Yijie Huang & Xiang Yu, 2023, "On optimal tracking portfolio in incomplete markets: The reinforcement learning approach," Papers, arXiv.org, number 2311.14318, Nov, revised Oct 2024.
• Jingyi Tian & Jun Nagayasu, 2023, "Financial Systemic Risk behind Artificial Intelligence:Evidence from China," TUPD Discussion Papers, Graduate School of Economics and Management, Tohoku University, number 44, Nov.
• Modis, Theodore, 2023, "The Impact of Artificial Intelligence on Complexity," OSF Preprints, Center for Open Science, number rtw9b, Nov, DOI: 10.31219/osf.io/rtw9b.
• Goller, Daniel & Gschwendt, Christian & Wolter, Stefan C., 2023, "'This Time It's Different' - Generative Artificial Intelligence and Occupational Choice," IZA Discussion Papers, Institute of Labor Economics (IZA), number 16638, Nov.
• Yu, Chen, 2023, "AI Eat Men and the Awakening of the Middle Class," OSF Preprints, Center for Open Science, number bj2p4, Dec, DOI: 10.31219/osf.io/bj2p4.
• Deborah Miori & Constantin Petrov, 2023, "Narratives from GPT-derived Networks of News, and a link to Financial Markets Dislocations," Papers, arXiv.org, number 2311.14419, Nov.
• Mohammad Rasouli & Ravi Chiruvolu & Ali Risheh, 2023, "AI for Investment: A Platform Disruption," Papers, arXiv.org, number 2311.06251, Sep.
• GÓMEZ-GONZÁLEZ Emilio & GOMEZ Emilia, 2023, "Artificial intelligence for healthcare and well-being during exceptional times," JRC Research Reports, Joint Research Centre, number JRC134715, Nov.
• Christophe Carugati, 2023, "The competitive relationship between cloud computing and generative AI," Bruegel Working Papers, Bruegel, number node_9593, Dec.
• L. Ingber, 2023, "Quantum variables in Finance," Lester Ingber Papers, Lester Ingber, number 23qf.

Report NEP-CMP-2024-01-08

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: