Report NEP-CMP-2024-03-11

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:

• Mestiri, Sami, 2024, "Financial applications of machine learning using R software," MPRA Paper, University Library of Munich, Germany, number 119998.
• Magassouba, Aboubacar Sidiki & Diallo, Abdourahmane & Nkurunziza, Armel & Tchole, Ali Issakou Malam & Touré, Almamy Amara & Magassouba, Mamoudou & Sylla, Younoussa & diallo, Mamadou Abdoulaye R & Nabé, 2024, "Implementing a Machine Learning Model to Predict Continuation of Contraception Among Women Aged 15-49: Secondary Analysis of the Last 4 Demographic and Health Surveys in West African Country," SocArXiv, Center for Open Science, number u38sh, Jan, DOI: 10.31219/osf.io/u38sh.
• Yulu Gong & Mengran Zhu & Shuning Huo & Yafei Xiang & Hanyi Yu, 2024, "Utilizing Deep Learning for Enhancing Network Resilience in Finance," Papers, arXiv.org, number 2402.09820, Feb, revised Feb 2024.
• Kelvin J. L. Koa & Yunshan Ma & Ritchie Ng & Tat-Seng Chua, 2024, "Learning to Generate Explainable Stock Predictions using Self-Reflective Large Language Models," Papers, arXiv.org, number 2402.03659, Feb, revised Feb 2024.
• Phoebe Koundouri & Panagiotis Stavros Aslanidis & Konstantinos Dellis & Georgios Feretzakis & Angelos Plataniotis, 2024, "Uncovering the SDG content of Human Security Policies through a Machine Learning web application," DEOS Working Papers, Athens University of Economics and Business, number 2406, Feb.
• Sven Klaassen & Jan Teichert-Kluge & Philipp Bach & Victor Chernozhukov & Martin Spindler & Suhas Vijaykumar, 2024, "DoubleMLDeep: Estimation of Causal Effects with Multimodal Data," Papers, arXiv.org, number 2402.01785, Feb.
• Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024, "Modelling crypto markets by multi-agent reinforcement learning," Papers, arXiv.org, number 2402.10803, Feb.
• Benjamin Patrick Evans & Sumitra Ganesh, 2024, "Learning and Calibrating Heterogeneous Bounded Rational Market Behaviour with Multi-Agent Reinforcement Learning," Papers, arXiv.org, number 2402.00787, Feb.
• Teodoro Baldazzi & Luigi Bellomarini & Stefano Ceri & Andrea Colombo & Andrea Gentili & Emanuel Sallinger, 2024, "Fine-tuning large language models for financial markets via ontological reasoning," Mercati, infrastrutture, sistemi di pagamento (Markets, Infrastructures, Payment Systems), Bank of Italy, Directorate General for Markets and Payment System, number 44, Jan.
• Cai, Hengrui & Shi, Chengchun & Song, Rui & Lu, Wenbin, 2023, "Jump interval-learning for individualized decision making with continuous treatments," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 118231, Feb.
• Ozili, Peterson K, 2024, "Artificial intelligence in central banking: benefits and risks of AI for central banks," MPRA Paper, University Library of Munich, Germany, number 120151.
• J. van den Berg, Gerard & Kunaschk, Max & Lang, Julia & Stephan, Gesine & Uhlendorff, Arne, 2023, "Predicting re-employment: machine learning versus assessments by unemployed workers and by their caseworkers," Working Paper Series, IFAU - Institute for Evaluation of Labour Market and Education Policy, number 2023:22, Nov.
• Joshua S. Gans, 2024, "Copyright Policy Options for Generative Artificial Intelligence," NBER Working Papers, National Bureau of Economic Research, Inc, number 32106, Feb.
• Jean Lee & Nicholas Stevens & Soyeon Caren Han & Minseok Song, 2024, "A Survey of Large Language Models in Finance (FinLLMs)," Papers, arXiv.org, number 2402.02315, Feb.
• Takuji Arai & Yuto Imai, 2024, "Option pricing for Barndorff-Nielsen and Shephard model by supervised deep learning," Papers, arXiv.org, number 2402.00445, Feb.
• Zhenglong Li & Vincent Tam & Kwan L. Yeung, 2024, "Developing A Multi-Agent and Self-Adaptive Framework with Deep Reinforcement Learning for Dynamic Portfolio Risk Management," Papers, arXiv.org, number 2402.00515, Feb, revised Sep 2024.
• Bernhard Hientzsch, 2023, "Reinforcement Learning and Deep Stochastic Optimal Control for Final Quadratic Hedging," Papers, arXiv.org, number 2401.08600, Nov.
• Saizhuo Wang & Hang Yuan & Lionel M. Ni & Jian Guo, 2024, "QuantAgent: Seeking Holy Grail in Trading by Self-Improving Large Language Model," Papers, arXiv.org, number 2402.03755, Feb.
• Matteo Coronese & Martina Occelli & Francesco Lamperti & Andrea Roventini, 2024, "Towards sustainable agriculture: behaviors, spatial dynamics and policy in an evolutionary agent-based model," LEM Papers Series, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy, number 2024/05, Feb.
• Daniel Borup & Philippe Goulet Coulombe & Erik Christian Montes Schütte & David E. Rapach & Sander Schwenk-Nebbe, 2024, "The Anatomy of Out-of-Sample Forecasting Accuracy," FRB Atlanta Working Paper, Federal Reserve Bank of Atlanta, number 2022-16b, Feb, DOI: 10.29338/wp2022-16b.
• Joshua S. Gans, 2024, "How Learning About Harms Impacts the Optimal Rate of Artificial Intelligence Adoption," NBER Working Papers, National Bureau of Economic Research, Inc, number 32105, Feb.
• Yike Wang & Chris Gu & Taisuke Otsu, 2024, "Graph Neural Networks: Theory for Estimation with Application on Network Heterogeneity," Papers, arXiv.org, number 2401.16275, Jan.
• Marcos Lacasa Cazcarra, 2024, "Machine Learning Analysis of the Impact of Increasing the Minimum Wage on Income Inequality in Spain from 2001 to 2021," Papers, arXiv.org, number 2402.02402, Feb.
• Johannes Carow & Niklas M. Witzig, 2024, "Time Pressure and Strategic Risk-Taking in Professional Chess," Working Papers, Gutenberg School of Management and Economics, Johannes Gutenberg-Universität Mainz, number 2404, Feb.
• Xiaorui Zuo & Yao-Tsung Chen & Wolfgang Karl Hardle, 2024, "Emoji Driven Crypto Assets Market Reactions," Papers, arXiv.org, number 2402.10481, Feb, revised May 2024.
• Mateo Seré, 2023, "Don´t Stop Me Now: Gender Attitudes in Academic Seminars Through Machine Learning," Working Papers, Herman Deleeck Centre for Social Policy, University of Antwerp, number 2309, Jul.
• Matthews, Ben, 2024, "Simulating Victimization Divides," SocArXiv, Center for Open Science, number k8j9e, Feb, DOI: 10.31219/osf.io/k8j9e.
• Leogrande, Angelo & Costantiello, Alberto & Leogrande, Domenico, 2023, "The Socio-Economic Determinants Of The Number Of Physicians In Italian Regions," SocArXiv, Center for Open Science, number 92wnh, Nov, DOI: 10.31219/osf.io/92wnh.

Report NEP-CMP-2024-03-11

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: