Report NEP-CMP-2024-12-02

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:

• Alicia Vidler & Toby Walsh, 2024. "TraderTalk: An LLM Behavioural ABM applied to Simulating Human Bilateral Trading Interactions," Papers 2410.21280, arXiv.org.
• Saber Talazadeh & Dragan Perakovic, 2024. "SARF: Enhancing Stock Market Prediction with Sentiment-Augmented Random Forest," Papers 2410.07143, arXiv.org.
• Jesús Fernández-Villaverde & Galo Nuño & Jesse Perla, 2024. "Taming the Curse of Dimensionality: Quantitative Economics with Deep Learning," NBER Working Papers 33117, National Bureau of Economic Research, Inc.
• Yi Zheng & Zehao Li & Peng Jiang & Yijie Peng, 2024. "Dual-Agent Deep Reinforcement Learning for Dynamic Pricing and Replenishment," Papers 2410.21109, arXiv.org.
• Arya Chakraborty & Auhona Basu, 2024. "A Hierarchical conv-LSTM and LLM Integrated Model for Holistic Stock Forecasting," Papers 2410.12807, arXiv.org.
• Koen Ponse & Aske Plaat & Niki van Stein & Thomas M. Moerland, 2024. "EconoJax: A Fast & Scalable Economic Simulation in Jax," Papers 2410.22165, arXiv.org.
• Kei Nakagawa & Masanori Hirano & Yugo Fujimoto, 2024. "Evaluating Company-specific Biases in Financial Sentiment Analysis using Large Language Models," Papers 2411.00420, arXiv.org.
• Sotiris Tsolacos & Tatiana Franus, 2024. "Assessing the forecast performance of machine learning algorithms and econometric models in real estate," ERES eres2024-251, European Real Estate Society (ERES).
• Ji Ma, 2024. "Can Machines Think Like Humans? A Behavioral Evaluation of LLM-Agents in Dictator Games," Papers 2410.21359, arXiv.org.
• Yuan Gao & Dokyun Lee & Gordon Burtch & Sina Fazelpour, 2024. "Take Caution in Using LLMs as Human Surrogates: Scylla Ex Machina," Papers 2410.19599, arXiv.org, revised Nov 2024.
• Ruyi Tao & Kaiwei Liu & Xu Jing & Jiang Zhang, 2024. "Predicting Company Growth by Econophysics informed Machine Learning," Papers 2410.17587, arXiv.org.
• Michel F. C. Haddad & Martin Huber & Lucas Z. Zhang, 2024. "Difference-in-Differences with Time-varying Continuous Treatments using Double/Debiased Machine Learning," Papers 2410.21105, arXiv.org.