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Report NEP-CMP-2024-03-04
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:
- Franck Ramaharo & Gerzhino Rasolofomanana, 2023, "Nowcasting Madagascar's real GDP using machine learning algorithms," Papers, arXiv.org, number 2401.10255, Dec.
- Pierre Renucci, 2023, "Optimal Linear Signal: An Unsupervised Machine Learning Framework to Optimize PnL with Linear Signals," Papers, arXiv.org, number 2401.05337, Nov.
- Weidong Lin & Abderrahim Taamouti, 2023, "Portfolio Selection Under Non-Gaussianity And Systemic Risk: A Machine Learning Based Forecasting Approach," Working Papers, University of Liverpool, Department of Economics, number 202310, Aug.
- Kim, Dongin, 2022, "Preferential Trading in Agriculture: New Insights from a Structural Gravity Analysis and Machine Learning," 2022: Transforming Global Value Chains, December 11-13, Clearwater Beach, FL, International Agricultural Trade Research Consortium, number 339469, Dec, DOI: 10.22004/ag.econ.339469.
- Gordeev, Stepan & Jelliffe, Jeremy & Kim, Dongin & Steinbach, Sandro, 2023, "What Matters for Agricultural Trade? Assessing the Role of Trade Deal Provisions using Machine Learning," 2023: The Future of (Ag-) Trade and Trade Governance in Times of Economic Sanctions and Declining Multilateralism, December 10-12, Clearwater Beach, FL, International Agricultural Trade Research Consortium, number 339533, Dec, DOI: 10.22004/ag.econ.339533.
- David Almog & Romain Gauriot & Lionel Page & Daniel Martin, 2024, "AI Oversight and Human Mistakes: Evidence from Centre Court," Papers, arXiv.org, number 2401.16754, Jan, revised Feb 2025.
- Diwas Paudel & Tapas K. Das, 2024, "Tacit algorithmic collusion in deep reinforcement learning guided price competition: A study using EV charge pricing game," Papers, arXiv.org, number 2401.15108, Jan, revised May 2024.
- Mario Sanz-Guerrero & Javier Arroyo, 2024, "Credit Risk Meets Large Language Models: Building a Risk Indicator from Loan Descriptions in P2P Lending," Papers, arXiv.org, number 2401.16458, Jan, revised Mar 2025.
- Bartosz Bieganowski & Robert Ślepaczuk, 2024, "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2024-03.
- Almeida, Derick & Naudé, Wim & Sequeira, Tiago Neves, 2024, "Artificial Intelligence and the Discovery of New Ideas: Is an Economic Growth Explosion Imminent?," IZA Discussion Papers, Institute of Labor Economics (IZA), number 16766, Jan.
- Dengxin Huang, 2023, "Application of Machine Learning in Stock Market Forecasting: A Case Study of Disney Stock," Papers, arXiv.org, number 2401.10903, Dec.
- Churchill, Alexander & Pichika, Shamitha & Xu, Chengxin, 2024, "Using Generative Pre-Trained Transformers (GPT) for Supervised Content Encoding: An Application in Corresponding Experiments," SocArXiv, Center for Open Science, number 6fpgj, Jan, DOI: 10.31219/osf.io/6fpgj.
- Zhiyu Quan & Changyue Hu & Panyi Dong & Emiliano A. Valdez, 2024, "Improving Business Insurance Loss Models by Leveraging InsurTech Innovation," Papers, arXiv.org, number 2401.16723, Jan.
- Helena Chuliá & Sabuhi Khalili & Jorge M. Uribe, 2024, "Monitoring time-varying systemic risk in sovereign debt and currency markets with generative AI," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 202402, Feb, revised Feb 2024.
- M. Shabani & M. Magris & George Tzagkarakis & J. Kanniainen & A. Iosifidis, 2023, "Predicting the state of synchronization of financial time series using cross recurrence plots," Post-Print, HAL, number hal-04415269, Jun, DOI: 10.1007/s00521-023-08674-y.
- Hauke Licht & Ronja Sczepanski, 2024, "Who are They Talking About? Detecting Mentions of Social Groups in Political Texts with Supervised Learning," ECONtribute Discussion Papers Series, University of Bonn and University of Cologne, Germany, number 277, Feb.
- Roberto Baviera & Pietro Manzoni, 2024, "Fast and General Simulation of L\'evy-driven OU processes for Energy Derivatives," Papers, arXiv.org, number 2401.15483, Jan, revised Sep 2024.
- Wesley H. Holliday & Alexander Kristoffersen & Eric Pacuit, 2024, "Learning to Manipulate under Limited Information," Papers, arXiv.org, number 2401.16412, Jan, revised Feb 2025.
- Greiner, Ben & Grünwald, Philipp & Lindner, Thomas & Lintner, Georg & Wiernsperger, Martin, 2024, "Incentives, Framing, and Reliance on Algorithmic Advice: An Experimental Study," Department for Strategy and Innovation Working Paper Series, WU Vienna University of Economics and Business, number 01/2024, Jan.
- Tin Cheuk Leung & Koleman Strumpf, 2024, "Disentangling Demand and Supply of Media Bias: The Case of Newspaper Homepages," CESifo Working Paper Series, CESifo, number 10890.