Report NEP-CMP-2024-08-26

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.

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Other reports in NEP-CMP

The following items were announced in this report:

• Shi, Chengchun & Qi, Zhengling & Wang, Jianing & Zhou, Fan, 2023, "Value enhancement of reinforcement learning via efficient and robust trust region optimization," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 122756, Jul.
• Tiago Monteiro, 2024, "AI-Powered Energy Algorithmic Trading: Integrating Hidden Markov Models with Neural Networks," Papers, arXiv.org, number 2407.19858, Jul, revised Nov 2025.
• Alejandra de la Rica Escudero & Eduardo C. Garrido-Merchan & Maria Coronado-Vaca, 2024, "Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent," Papers, arXiv.org, number 2407.14486, Jul.
• Abdul Jabbar & Syed Qaisar Jalil, 2024, "A Comprehensive Analysis of Machine Learning Models for Algorithmic Trading of Bitcoin," Papers, arXiv.org, number 2407.18334, Jul.
• Joel P. Villarino & 'Alvaro Leitao, 2024, "On Deep Learning for computing the Dynamic Initial Margin and Margin Value Adjustment," Papers, arXiv.org, number 2407.16435, Jul.
• Chung I Lu & Julian Sester, 2024, "Generative modelling of financial time series with structured noise and MMD-based signature learning," Papers, arXiv.org, number 2407.19848, Jul, revised Nov 2025.
• Undral Byambadalai & Tatsushi Oka & Shota Yasui, 2024, "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction," Papers, arXiv.org, number 2407.16037, Jul.
• Wolff, Dominik & Echterling, Fabian, 2024, "Stock picking with machine learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL), Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), number 149079, Jan.
• Kamila Zaman & Alberto Marchisio & Muhammad Kashif & Muhammad Shafique, 2024, "PO-QA: A Framework for Portfolio Optimization using Quantum Algorithms," Papers, arXiv.org, number 2407.19857, Jul.
• Gavin Ugale & Cameron Hall, 2024, "Generative AI for anti-corruption and integrity in government: Taking stock of promise, perils and practice," OECD Artificial Intelligence Papers, OECD Publishing, number 12, Mar, DOI: 10.1787/657a185a-en.
• Hongshen Yang & Avinash Malik, 2024, "Reinforcement Learning Pair Trading: A Dynamic Scaling approach," Papers, arXiv.org, number 2407.16103, Jul, revised Dec 2024.
• Höschle, Lisa & Yu, Xiaohua, 2023, "Food Price Dynamics in OECD Countries--Evidence on Clusters and Predictors from Machine Learning," GEWISOLA 63rd Annual Conference, Goettingen, Germany, September 20-22, 2023, GEWISOLA, number 344249, Sep, DOI: 10.22004/ag.econ.344249.
• Sabhi Rajae & Abdelbaki Jamal Eddine & Taouab Omar & Eddaoudi Faissal & Abdelbaki Noureddine, 2024, "Managing emotions and algorithms: the delicate equilibrium between artificial intelligence and behavioral finance
  [Gérer les émotions et les algorithmes : l'équilibre délicat entre l'intelligence artificielle et la finance comportementale]," Post-Print, HAL, number hal-04644322, Jul, DOI: 10.5281/zenodo.12705888.
• Ma, Tao & Yang, Xuzhi & Szabo, Zoltan, 2024, "To switch or not to switch? Balanced policy switching in offline reinforcement learning," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 124144, Jul.
• Taha Barwahwala & Aprajit Mahajan & Shekhar Mittal & Ofir Reich, 2024, "Is Model Accuracy Enough? A Field Evaluation Of A Machine Learning Model To Catch Bogus Firms," NBER Working Papers, National Bureau of Economic Research, Inc, number 32705, Jul.
• Chong Zhang & Xinyi Liu & Zhongmou Zhang & Mingyu Jin & Lingyao Li & Zhenting Wang & Wenyue Hua & Dong Shu & Suiyuan Zhu & Xiaobo Jin & Sujian Li & Mengnan Du & Yongfeng Zhang, 2024, "When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments," Papers, arXiv.org, number 2407.18957, Jul, revised Sep 2024.
• Li, Jie & Fearnhead, Paul & Fryzlewicz, Piotr & Wang, Tengyao, 2024, "Automatic change-point detection in time series via deep learning," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 120083, Apr.
• Philippe Lorenz & Karine Perset & Jamie Berryhill, 2023, "Initial policy considerations for generative artificial intelligence," OECD Artificial Intelligence Papers, OECD Publishing, number 1, Sep, DOI: 10.1787/fae2d1e6-en.
• Ludovic Goudenege & Andrea Molent & Antonino Zanette, 2024, "Leveraging Machine Learning for High-Dimensional Option Pricing within the Uncertain Volatility Model," Papers, arXiv.org, number 2407.13213, Jul, revised Jun 2025.
• St'ephane Cr'epey & Botao Li & Hoang Nguyen & Bouazza Saadeddine, 2024, "CVA Sensitivities, Hedging and Risk," Papers, arXiv.org, number 2407.18583, Jul.
• Oecd, 2023, "Generative artificial intelligence in finance," OECD Artificial Intelligence Papers, OECD Publishing, number 9, Dec, DOI: 10.1787/ac7149cc-en.
• Oecd, 2024, "Artificial intelligence, data and competition," OECD Artificial Intelligence Papers, OECD Publishing, number 18, May, DOI: 10.1787/e7e88884-en.
• Mahdi Ebrahimi Kahou & Jesus Fernandez-Villaverde & Sebastian Gomez-Cardona & Jesse Perla & Jan Rosa, 2024, "Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and Behavioral Macro," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, number 24-019, Aug.
• Natalia Roszyk & Robert 'Slepaczuk, 2024, "The Hybrid Forecast of S&P 500 Volatility ensembled from VIX, GARCH and LSTM models," Papers, arXiv.org, number 2407.16780, Jul.
• Wenbo Yan & Ying Tan, 2024, "TCGPN: Temporal-Correlation Graph Pre-trained Network for Stock Forecasting," Papers, arXiv.org, number 2407.18519, Jul.
• Shi, Chengchun & Zhou, Yunzhe & Li, Lexin, 2024, "Testing directed acyclic graph via structural, supervised and generative adversarial learning," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 119446, Dec.
• Tian Guo & Emmanuel Hauptmann, 2024, "Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow," Papers, arXiv.org, number 2407.18103, Jul, revised Aug 2024.
• Oecd, 2024, "Governing with Artificial Intelligence: Are governments ready?," OECD Artificial Intelligence Papers, OECD Publishing, number 20, Jun, DOI: 10.1787/26324bc2-en.
• Kamesh Korangi & Christophe Mues & Cristi'an Bravo, 2024, "Large-scale Time-Varying Portfolio Optimisation using Graph Attention Networks," Papers, arXiv.org, number 2407.15532, Jul, revised Feb 2025.
• Oecd, 2024, "Explanatory memorandum on the updated OECD definition of an AI system," OECD Artificial Intelligence Papers, OECD Publishing, number 8, Mar, DOI: 10.1787/623da898-en.
• Zi Wang & Xingcheng Xu & Yanqing Yang & Xiaodong Zhu, 2024, "Optimal Trade and Industrial Policies in the Global Economy: A Deep Learning Framework," Papers, arXiv.org, number 2407.17731, Jul.
• Flavio Calvino & Chiara Criscuolo & Hélène Dernis & Lea Samek, 2023, "What technologies are at the core of AI?: An exploration based on patent data," OECD Artificial Intelligence Papers, OECD Publishing, number 6, Nov, DOI: 10.1787/32406765-en.
• Foltas, Alexander, 2024, "Inefficient forecast narratives: A BERT-based approach," Working Papers, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin, number 45, DOI: 10.18452/29133.
• Kerri Lu & Dan M. Kluger & Stephen Bates & Sherrie Wang, 2024, "Regression coefficient estimation from remote sensing maps," Papers, arXiv.org, number 2407.13659, Jul, revised Jul 2025.
• Annelore Verhagen, 2024, "Using AI to manage minimum income benefits and unemployment assistance: Opportunities, risks and possible policy directions," OECD Artificial Intelligence Papers, OECD Publishing, number 21, Jun, DOI: 10.1787/718c93a1-en.
• Beckert, Jens & Arndt, H. Lukas R., 2024, "The Greek tragedy: Narratives and imagined futures in the Greek sovereign debt crisis," MPIfG Discussion Paper, Max Planck Institute for the Study of Societies, number 24/4.
• Joshua S. Gans, 2024, "Will User-Contributed AI Training Data Eat Its Own Tail?," NBER Working Papers, National Bureau of Economic Research, Inc, number 32686, Jul.
• Item repec:ags:cfcp15:344315 is not listed on IDEAS anymore

• Debuque-Gonzales,Margarita & Corpus,JohnPaulP., 2024, "Let’s Get Fiscal: Extending the Small Macroeconometric Model of the Philippine Economy," Research Paper Series, Philippine Institute for Development Studies, number RPS 2024-05, DOI: https://doi.org/10.62986/rps2024.05.
• Guan-Yuan Wang, 2022, "Churn Prediction for High-Value Players in Freemium Mobile Games: Using Random Under-Sampling," Post-Print, HAL, number hal-04632443, Dec, DOI: 10.54694/stat.2022.18.
• Marcello Monga, 2024, "Automated Market Making and Decentralized Finance," Papers, arXiv.org, number 2407.16885, Jul.
• Oecd, 2024, "Defining AI incidents and related terms," OECD Artificial Intelligence Papers, OECD Publishing, number 16, May, DOI: 10.1787/d1a8d965-en.
• Davillas, Apostolos & Jones, Andrew M., 2024, "Biological Age and Predicting Future Health Care Utilisation," IZA Discussion Papers, Institute of Labor Economics (IZA), number 17159, Jul.
• Arnone, Massimo & Leogrande, Angelo, 2024, "The Sustainability of the Factoring Chain in Europe in the Light of the Integration of ESG Factors," MPRA Paper, University Library of Munich, Germany, number 121342, Jun.
• Devang Sinha & Siddhartha P. Chakrabarty, 2024, "Multilevel Monte Carlo in Sample Average Approximation: Convergence, Complexity and Application," Papers, arXiv.org, number 2407.18504, Jul.
• Carlsson, John G, 2024, "Applying Topological Data Analysis to Logistics Systems Analysis," Institute of Transportation Studies, Working Paper Series, Institute of Transportation Studies, UC Davis, number qt7m0347nd, Jul.

Report NEP-CMP-2024-08-26

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: