Report NEP-CMP-2025-02-03

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:

• Kuan-Ming Liu & Ming-Chih Lo, 2025, "LLM-Based Routing in Mixture of Experts: A Novel Framework for Trading," Papers, arXiv.org, number 2501.09636, Jan, revised Jan 2025.
• Jingfeng Chen & Wanlin Deng & Dangxing Chen & Luyao Zhang, 2024, "FinML-Chain: A Blockchain-Integrated Dataset for Enhanced Financial Machine Learning," Papers, arXiv.org, number 2411.16277, Nov.
• Adamantios Ntakaris & Gbenga Ibikunle, 2024, "Online High-Frequency Trading Stock Forecasting with Automated Feature Clustering and Radial Basis Function Neural Networks," Papers, arXiv.org, number 2412.16160, Nov, revised Dec 2024.
• Suyeol Yun, 2024, "Pretrained LLM Adapted with LoRA as a Decision Transformer for Offline RL in Quantitative Trading," Papers, arXiv.org, number 2411.17900, Nov.
• Muhammad Arslan & Christophe Cruz, 2024, "Business-RAG: Information Extraction for Business Insights," Post-Print, HAL, number hal-04862172, Jul, DOI: 10.5220/0012812800003764.
• Fischer, Manfred M., 2025, "Convolutional Neural Networks:: Key Components and Architectures," Working Papers in Regional Science, WU Vienna University of Economics and Business, number 01.
• Gabriel Okasa & Alberto de Le'on & Michaela Strinzel & Anne Jorstad & Katrin Milzow & Matthias Egger & Stefan Muller, 2024, "A Supervised Machine Learning Approach for Assessing Grant Peer Review Reports," Papers, arXiv.org, number 2411.16662, Nov, revised Dec 2024.
• Filipovska, Elena & Mladenovska, Ana & Bajrami, Merxhan & Dobreva, Jovana & Hillman, Velislava & Lameski, Petre & Zdravevski, Eftim, 2024, "Benchmarking OpenAI's APIs and other Large Language Models for repeatable and efficient question answering across multiple documents," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 126674, Dec.
• Roberto-Rafael Maura-Rivero & Chirag Nagpal & Roma Patel & Francesco Visin, 2025, "Utility-inspired Reward Transformations Improve Reinforcement Learning Training of Language Models," Papers, arXiv.org, number 2501.06248, Jan, revised Feb 2025.
• Bo Yuan & Damiano Brigo & Antoine Jacquier & Nicola Pede, 2024, "Deep learning interpretability for rough volatility," Papers, arXiv.org, number 2411.19317, Nov.
• Qian Yu & Zhen Xu & Zong Ke, 2024, "Deep Learning for Cross-Border Transaction Anomaly Detection in Anti-Money Laundering Systems," Papers, arXiv.org, number 2412.07027, Nov.
• Hamza Bodor & Laurent Carlier, 2025, "Deep Learning Meets Queue-Reactive: A Framework for Realistic Limit Order Book Simulation," Papers, arXiv.org, number 2501.08822, Jan.
• Bryan T. Kelly & Boris Kuznetsov & Semyon Malamud & Teng Andrea Xu, 2025, "Artificial Intelligence Asset Pricing Models," NBER Working Papers, National Bureau of Economic Research, Inc, number 33351, Jan.
• Jerick Shi & Burton Hollifield, 2024, "Predictive Power of LLMs in Financial Markets," Papers, arXiv.org, number 2411.16569, Nov.
• Jebish Purbey & Siddhant Gupta & Nikhil Manali & Siddartha Pullakhandam & Drishti Sharma & Ashay Srivastava & Ram Mohan Rao Kadiyala, 2024, "SeQwen at the Financial Misinformation Detection Challenge Task: Sequential Learning for Claim Verification and Explanation Generation in Financial Domains," Papers, arXiv.org, number 2412.00549, Nov.
• Chi-Sheng Chen & Ying-Jung Chen, 2025, "Optimizing Supply Chain Networks with the Power of Graph Neural Networks," Papers, arXiv.org, number 2501.06221, Jan.
• Benjamin Patrick Evans & Sihan Zeng & Sumitra Ganesh & Leo Ardon, 2025, "ADAGE: A generic two-layer framework for adaptive agent based modelling," Papers, arXiv.org, number 2501.09429, Jan.
• Haonan Xu & Alessio Brini, 2025, "Improving DeFi Accessibility through Efficient Liquidity Provisioning with Deep Reinforcement Learning," Papers, arXiv.org, number 2501.07508, Jan.
• Eric Innocenti & Dominique Prunetti & Marielle Delhom & Corinne Idda, 2024, "Reusable Building Blocks for Agent-Based Simulations: Towards a Method for Composing and Building ABM/LUCC," Post-Print, HAL, number hal-04862884, Sep.
• Yonggai Zhuang & Haoran Chen & Kequan Wang & Teng Fei, 2024, "GRU-PFG: Extract Inter-Stock Correlation from Stock Factors with Graph Neural Network," Papers, arXiv.org, number 2411.18997, Nov.
• Jimmy Cheung & Smruthi Rangarajan & Amelia Maddocks & Xizhe Chen & Rohitash Chandra, 2024, "Quantile deep learning models for multi-step ahead time series prediction," Papers, arXiv.org, number 2411.15674, Nov.
• Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2025, "Large Language Models: An Applied Econometric Framework," NBER Working Papers, National Bureau of Economic Research, Inc, number 33344, Jan.
• Connor Douglas & Foster Provost & Arun Sundararajan, 2024, "Naive Algorithmic Collusion: When Do Bandit Learners Cooperate and When Do They Compete?," Papers, arXiv.org, number 2411.16574, Nov.
• Stella C. Dong & James R. Finlay, 2025, "A Hybrid Framework for Reinsurance Optimization: Integrating Generative Models and Reinforcement Learning," Papers, arXiv.org, number 2501.06404, Jan.
• Aaron Wheeler & Jeffrey D. Varner, 2024, "MarketGPT: Developing a Pre-trained transformer (GPT) for Modeling Financial Time Series," Papers, arXiv.org, number 2411.16585, Nov.
• Kun Liu & Jin Zhao, 2024, "KACDP: A Highly Interpretable Credit Default Prediction Model," Papers, arXiv.org, number 2411.17783, Nov.
• Ling Chen, 2024, "Risk Management with Feature-Enriched Generative Adversarial Networks (FE-GAN)," Papers, arXiv.org, number 2411.15519, Nov.
• Robert Novy-Marx & Mihail Z. Velikov, 2025, "AI-Powered (Finance) Scholarship," NBER Working Papers, National Bureau of Economic Research, Inc, number 33363, Jan.
• Augusto Gonzalez-Bonorino & Monica Capra & Emilio Pantoja, 2025, "LLMs Model Non-WEIRD Populations: Experiments with Synthetic Cultural Agents," Papers, arXiv.org, number 2501.06834, Jan.
• Ramshreyas Rao, 2025, "Agent-Based Simulation of a Perpetual Futures Market," Papers, arXiv.org, number 2501.09404, Jan.
• Briola, Antonio & Bartolucci, Silvia & Aste, Tomaso, 2025, "HLOB–Information persistence and structure in limit order books," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 126623, Mar.
• Andrew Lesniewski & Giulio Trigila, 2024, "Beyond Monte Carlo: Harnessing Diffusion Models to Simulate Financial Market Dynamics," Papers, arXiv.org, number 2412.00036, Nov, revised Feb 2025.
• Mahdi Salahshour & Amirahmad Shafiee & Mojtaba Tefagh, 2024, "Joint Combinatorial Node Selection and Resource Allocations in the Lightning Network using Attention-based Reinforcement Learning," Papers, arXiv.org, number 2411.17353, Nov.
• Xuesong Wang & Sharaf K. Magableh & Oraib Dawaghreh & Caisheng Wang & Jiaxuan Gong & Zhongyang Zhao & Michael H. Liao, 2024, "Deep Learning-Based Electricity Price Forecast for Virtual Bidding in Wholesale Electricity Market," Papers, arXiv.org, number 2412.00062, Nov.

Report NEP-CMP-2025-02-03

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: