Report NEP-CMP-2025-01-27

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:

• Weizhe Ren & Yichen Qin & Yang Li, 2024, "Alpha Mining and Enhancing via Warm Start Genetic Programming for Quantitative Investment," Papers, arXiv.org, number 2412.00896, Dec.
• Mengming Michael Dong & Theophanis C. Stratopoulos & Victor Xiaoqi Wang, 2024, "A Scoping Review of ChatGPT Research in Accounting and Finance," Papers, arXiv.org, number 2412.05731, Dec.
• Zhuohuan Hu & Richard Yu & Zizhou Zhang & Haoran Zheng & Qianying Liu & Yining Zhou, 2024, "Developing Cryptocurrency Trading Strategy Based on Autoencoder-CNN-GANs Algorithms," Papers, arXiv.org, number 2412.18202, Dec, revised Jul 2025.
• Gavin Kader & Dongwoo Lee, 2024, "The Emergence of Strategic Reasoning of Large Language Models," Papers, arXiv.org, number 2412.13013, Dec, revised Oct 2025.
• Tommaso Di Francesco & Daniel Torren Peraire, 2024, "(Mis)information diffusion and the financial market," Papers, arXiv.org, number 2412.16269, Dec.
• Jiajun Gu & Zichen Yang & Xintong Lin & Sixun Chen & YuTing Lu, 2024, "AI-Enhanced Factor Analysis for Predicting S&P 500 Stock Dynamics," Papers, arXiv.org, number 2412.12438, Dec.
• Hortense Fong & George Gui, 2024, "Modeling Story Expectations to Understand Engagement: A Generative Framework Using LLMs," Papers, arXiv.org, number 2412.15239, Dec, revised Jul 2025.
• Pengbin Feng & Yankaiqi Li & Yijiashun Qi & Xiaojun Guo & Zhenghao Lin, 2024, "Collaborative Optimization in Financial Data Mining Through Deep Learning and ResNeXt," Papers, arXiv.org, number 2412.17314, Dec.
• Zong Ke & Jingyu Xu & Zizhou Zhang & Yu Cheng & Wenjun Wu, 2024, "A Consolidated Volatility Prediction with Back Propagation Neural Network and Genetic Algorithm," Papers, arXiv.org, number 2412.07223, Dec, revised Aug 2025.
• Akash Deep & Abootaleb Shirvani & Chris Monico & Svetlozar Rachev & Frank J. Fabozzi, 2024, "Risk-Adjusted Performance of Random Forest Models in High-Frequency Trading," Papers, arXiv.org, number 2412.15448, Dec, revised Feb 2025.
• Gang Huang & Xiaohua Zhou & Qingyang Song, 2024, "A Deep Reinforcement Learning Framework for Dynamic Portfolio Optimization: Evidence from China's Stock Market," Papers, arXiv.org, number 2412.18563, Dec, revised Feb 2025.
• Fengpei Li & Haoxian Chen & Jiahe Lin & Arkin Gupta & Xiaowei Tan & Honglei Zhao & Gang Xu & Yuriy Nevmyvaka & Agostino Capponi & Henry Lam, 2024, "Prediction-Enhanced Monte Carlo: A Machine Learning View on Control Variate," Papers, arXiv.org, number 2412.11257, Dec, revised Jun 2025.
• Filip Wójcik, 2024, "An Analysis of Novel Money Laundering Data Using Heterogeneous Graph Isomorphism Networks. FinCEN Files Case Study," Post-Print, HAL, number hal-04839757, DOI: 10.15611/eada.2024.2.03.
• Anton Korinek, 2024, "Generative AI for Economic Research: LLMs Learn to Collaborate and Reason," NBER Working Papers, National Bureau of Economic Research, Inc, number 33198, Nov.
• Jens Ludwig & Sendhil Mullainathan & Ashesh Rambachan, 2024, "Large Language Models: An Applied Econometric Framework," Papers, arXiv.org, number 2412.07031, Dec, revised Dec 2025.
• Niko Hauzenberger & Florian Huber & Karin Klieber & Massimiliano Marcellino, 2024, "Machine Learning the Macroeconomic Effects of Financial Shocks," Papers, arXiv.org, number 2412.07649, Dec.
• Yuxin Fan & Zhuohuan Hu & Lei Fu & Yu Cheng & Liyang Wang & Yuxiang Wang, 2024, "Research on Optimizing Real-Time Data Processing in High-Frequency Trading Algorithms using Machine Learning," Papers, arXiv.org, number 2412.01062, Dec.
• Qilong Wu & Xiaoneng Xiang & Hejia Huang & Xuan Wang & Yeo Wei Jie & Ranjan Satapathy & Ricardo Shirota Filho & Bharadwaj Veeravalli, 2024, "SusGen-GPT: A Data-Centric LLM for Financial NLP and Sustainability Report Generation," Papers, arXiv.org, number 2412.10906, Dec.
• Jianhua Yao & Yuxin Dong & Jiajing Wang & Bingxing Wang & Hongye Zheng & Honglin Qin, 2024, "Stock Type Prediction Model Based on Hierarchical Graph Neural Network," Papers, arXiv.org, number 2412.06862, Dec.
• Tarek Alexander Hassan & Stephan Hollander & Aakash Kalyani & Laurence van Lent & Markus Schwedeler & Ahmed Tahoun, 2024, "Economic Surveillance using Corporate Text," NBER Working Papers, National Bureau of Economic Research, Inc, number 33158, Nov.
• Pengjie Zhou & Haoyu Wei & Huiming Zhang, 2024, "Selective Reviews of Bandit Problems in AI via a Statistical View," Papers, arXiv.org, number 2412.02251, Dec, revised Feb 2025.
• Yuhan Wang & Zhen Xu & Yue Yao & Jinsong Liu & Jiating Lin, 2024, "Leveraging Convolutional Neural Network-Transformer Synergy for Predictive Modeling in Risk-Based Applications," Papers, arXiv.org, number 2412.18222, Dec.
• Vidhi Agrawal & Eesha Khalid & Tianyu Tan & Doris Xu, 2024, "Hunting Tomorrow's Leaders: Using Machine Learning to Forecast S&P 500 Additions & Removal," Papers, arXiv.org, number 2412.12539, Dec.
• Andrew Holmes & Matt Jensen & Sarah Coffland & Hidemi Mitani Shen & Logan Sizemore & Seth Bassetti & Brenna Nieva & Claudia Tebaldi & Abigail Snyder & Brian Hutchinson, 2024, "Emulating the Global Change Analysis Model with Deep Learning," Papers, arXiv.org, number 2412.08850, Dec.
• Shasha Yu & Qinchen Zhang & Yuwei Zhao, 2024, "S&P 500 Trend Prediction," Papers, arXiv.org, number 2412.11462, Dec.
• Sebastien Valeyre & Sofiane Aboura, 2024, "LLMs for Time Series: an Application for Single Stocks and Statistical Arbitrage," Papers, arXiv.org, number 2412.09394, Dec, revised Nov 2025.
• Vincent Lee Wai Seng & Shariff Abu Bakar Sarip Abidinsa, 2024, "Machine learning for anomaly detection in money services business outlets using data by geolocation," IFC Working Papers, Bank for International Settlements, number 23, Nov.
• Yixuan Liang & Yuncong Liu & Neng Wang & Hongyang Yang & Boyu Zhang & Christina Dan Wang, 2024, "FinGPT: Enhancing Sentiment-Based Stock Movement Prediction with Dissemination-Aware and Context-Enriched LLMs," Papers, arXiv.org, number 2412.10823, Dec, revised Jun 2025.
• Eduardo Abi Jaber, 2024, "Simulation of square-root processes made simple: applications to the Heston model," Papers, arXiv.org, number 2412.11264, Dec, revised Jun 2025.
• Lino Galiana & Lionel Wilner, 2024, "Private Wealth Over the Life Cycle: A Meeting Between Microsimulation and Structural Approaches," Post-Print, HAL, number hal-04799408, May, DOI: 10.1111/roiw.12697.
• Gero Junike & Hauke Stier, 2024, "Enhancing Fourier pricing with machine learning," Papers, arXiv.org, number 2412.05070, Dec.
• Zong Ke & Yuchen Yin, 2024, "Tail Risk Alert Based on Conditional Autoregressive VaR by Regression Quantiles and Machine Learning Algorithms," Papers, arXiv.org, number 2412.06193, Dec, revised Aug 2025.
• Edward Li & Min Shen & Zhiyuan Tu & Dexin Zhou, 2024, "The Promise and Peril of Generative AI: Evidence from GPT as Sell-Side Analysts," Papers, arXiv.org, number 2412.01069, Dec, revised Oct 2025.
• Kirill Safonov, 2024, "Neural Network Approach to Demand Estimation and Dynamic Pricing in Retail," Papers, arXiv.org, number 2412.00920, Dec, revised Dec 2024.
• Thomas Krause & Steffen Otterbach & Johannes Singer, 2024, "Delving into Youth Perspectives on In-game Gambling-like Elements: A Proof-of-Concept Study Utilising Large Language Models for Analysing User-Generated Text Data," Papers, arXiv.org, number 2412.09345, Dec.
• Haohang Li & Yupeng Cao & Yangyang Yu & Shashidhar Reddy Javaji & Zhiyang Deng & Yueru He & Yuechen Jiang & Zining Zhu & Koduvayur Subbalakshmi & Guojun Xiong & Jimin Huang & Lingfei Qian & Xueqing Pe, 2024, "INVESTORBENCH: A Benchmark for Financial Decision-Making Tasks with LLM-based Agent," Papers, arXiv.org, number 2412.18174, Dec.
• Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024, "Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study," Post-Print, HAL, number hal-04790290, Apr, DOI: 10.1371/journal.pone.0301141.
• Bhaskarjit Sarmah & Mingshu Li & Jingrao Lyu & Sebastian Frank & Nathalia Castellanos & Stefano Pasquali & Dhagash Mehta, 2024, "How to Choose a Threshold for an Evaluation Metric for Large Language Models," Papers, arXiv.org, number 2412.12148, Dec.
• Igor L. R. Azevedo & Toyotaro Suzumura, 2024, "From Votes to Volatility Predicting the Stock Market on Election Day," Papers, arXiv.org, number 2412.11192, Dec.
• Jiti Gao & Fei Liu & Bin Peng & Yanrong Yang, 2024, "Localized Neural Network Modelling of Time Series: A Case Study on US Monetary Policy," Monash Econometrics and Business Statistics Working Papers, Monash University, Department of Econometrics and Business Statistics, number 14/24.
• Cléo Chassonnery-Zaïgouche & Aurélien Goutsmedt, 2024, "Modelling intervention: Barbara Bergmann’s micro-to-macro simulation projects," Post-Print, HAL, number hal-04208686, Dec, DOI: 10.1080/09672567.2024.2433971.
• Stéphane Goutte & Klemens Klotzner & Hoang Viet Le & Hans Jörg von Mettenheim, 2024, "Forecasting photovoltaic production with neural networks and weather features," Post-Print, HAL, number hal-04779953, Sep, DOI: 10.1016/j.eneco.2024.107884.
• You Wu & Mengfang Sun & Hongye Zheng & Jinxin Hu & Yingbin Liang & Zhenghao Lin, 2024, "Integrative Analysis of Financial Market Sentiment Using CNN and GRU for Risk Prediction and Alert Systems," Papers, arXiv.org, number 2412.10199, Dec.
• Siqiao Zhao & Dan Wang & Raphael Douady, 2024, "PolyModel for Hedge Funds' Portfolio Construction Using Machine Learning," Papers, arXiv.org, number 2412.11019, Dec.
• Nick Huntington-Klein & Eleanor J. Murray, 2024, "Do LLMs Act as Repositories of Causal Knowledge?," Papers, arXiv.org, number 2412.10635, Dec.
• Sumit Nawathe & Ravi Panguluri & James Zhang & Sashwat Venkatesh, 2024, "Multimodal Deep Reinforcement Learning for Portfolio Optimization," Papers, arXiv.org, number 2412.17293, Dec.
• Olamilekan Shobayo & Sidikat Adeyemi-Longe & Olusogo Popoola & Bayode Ogunleye, 2024, "Innovative Sentiment Analysis and Prediction of Stock Price Using FinBERT, GPT-4 and Logistic Regression: A Data-Driven Approach," Papers, arXiv.org, number 2412.06837, Dec.
• Amine M. Aboussalah & Xuanze Li & Cheng Chi & Raj Patel, 2024, "The AI Black-Scholes: Finance-Informed Neural Network," Papers, arXiv.org, number 2412.12213, Dec.
• Marco Pangallo & R. Maria del Rio-Chanona, 2024, "Data-Driven Economic Agent-Based Models," Papers, arXiv.org, number 2412.16591, Dec.
• Hai-Thien To & Tien-Cuong Bui & Van-Duc Le, 2024, "RAG-IT: Retrieval-Augmented Instruction Tuning for Automated Financial Analysis -- A Case Study for the Semiconductor Sector," Papers, arXiv.org, number 2412.08179, Dec, revised Dec 2025.
• Julian Junyan Wang & Victor Xiaoqi Wang, 2024, "Leveraging Large Language Models to Democratize Access to Costly Datasets for Academic Research," Papers, arXiv.org, number 2412.02065, Dec, revised Sep 2025.
• Philippe Goulet Coulombe & Maximilian Goebel & Karin Klieber, 2024, "Dual Interpretation of Machine Learning Forecasts," Papers, arXiv.org, number 2412.13076, Dec.
• Bhaskarjit Sarmah & Kriti Dutta & Anna Grigoryan & Sachin Tiwari & Stefano Pasquali & Dhagash Mehta, 2024, "A Comparative Study of DSPy Teleprompter Algorithms for Aligning Large Language Models Evaluation Metrics to Human Evaluation," Papers, arXiv.org, number 2412.15298, Dec.

Report NEP-CMP-2025-01-27

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: