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. 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:

• Franck Ramaharo & Gerzhino Rasolofomanana, 2023. "Nowcasting Madagascar's real GDP using machine learning algorithms," Papers 2401.10255, arXiv.org.
• Pierre Renucci, 2023. "Optimal Linear Signal: An Unsupervised Machine Learning Framework to Optimize PnL with Linear Signals," Papers 2401.05337, arXiv.org.
• Weidong Lin & Abderrahim Taamouti, 2023. "Portfolio Selection Under Non-Gaussianity And Systemic Risk: A Machine Learning Based Forecasting Approach," Working Papers 202310, University of Liverpool, Department of Economics.
• 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 339469, International Agricultural Trade Research Consortium.
• 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 339533, International Agricultural Trade Research Consortium.
• David Almog & Romain Gauriot & Lionel Page & Daniel Martin, 2024. "AI Oversight and Human Mistakes: Evidence from Centre Court," Papers 2401.16754, arXiv.org, revised Feb 2024.
• Diwas Paudel & Tapas K. Das, 2024. "Tacit algorithmic collusion in deep reinforcement learning guided price competition: A study using EV charge pricing game," Papers 2401.15108, arXiv.org, 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 2401.16458, arXiv.org, revised Aug 2024.
• Bartosz Bieganowski & Robert Ślepaczuk, 2024. "Supervised Autoencoder MLP for Financial Time Series Forecasting," Working Papers 2024-03, Faculty of Economic Sciences, University of Warsaw.
• 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 16766, Institute of Labor Economics (IZA).
• Dengxin Huang, 2023. "Application of Machine Learning in Stock Market Forecasting: A Case Study of Disney Stock," Papers 2401.10903, arXiv.org.
• Churchill, Alexander & Pichika, Shamitha & Xu, Chengxin, 2024. "Using Generative Pre-Trained Transformers (GPT) for Supervised Content Encoding: An Application in Corresponding Experiments," SocArXiv 6fpgj, Center for Open Science.
• Zhiyu Quan & Changyue Hu & Panyi Dong & Emiliano A. Valdez, 2024. "Improving Business Insurance Loss Models by Leveraging InsurTech Innovation," Papers 2401.16723, arXiv.org.
• 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 202402, University of Barcelona, Research Institute of Applied Economics, 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-04415269, HAL.
• Hauke Licht & Ronja Sczepanksi, 2024. "Who are They Talking About? Detecting Mentions of Social Groups in Political Texts with Supervised Learning," ECONtribute Discussion Papers Series 277, University of Bonn and University of Cologne, Germany.
• Roberto Baviera & Pietro Manzoni, 2024. "Fast and General Simulation of L\'evy-driven OU processes for Energy Derivatives," Papers 2401.15483, arXiv.org, revised Sep 2024.
• Wesley H. Holliday & Alexander Kristoffersen & Eric Pacuit, 2024. "Learning to Manipulate under Limited Information," Papers 2401.16412, arXiv.org, revised Dec 2024.
• 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 01/2024, WU Vienna University of Economics and Business.
• Tin Cheuk Leung & Koleman Strumpf, 2024. "Disentangling Demand and Supply of Media Bias: The Case of Newspaper Homepages," CESifo Working Paper Series 10890, CESifo.