Report NEP-BIG-2025-09-22

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé issued this report. It is usually issued weekly.

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Other reports in NEP-BIG

The following items were announced in this report:

• Patrick J. Laub & Tu Pho & Bernard Wong, 2025. "An Interpretable Deep Learning Model for General Insurance Pricing," Papers 2509.08467, arXiv.org.
• Schmidt, Tobias & Lange, Kai-Robin & Reccius, Matthias & Müller, Henrik & Roos, Michael W. M. & Jentsch, Carsten, 2025. "Identifying economic narratives in large text corpora: An integrated approach using large language models," Ruhr Economic Papers 1163, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
• Adrian Iulian Cristescu & Matteo Giordano, 2025. "A comparative analysis of machine learning algorithms for predicting probabilities of default," Papers 2506.19789, arXiv.org.
• Ayaan Qayyum, 2025. "News Sentiment Embeddings for Stock Price Forecasting," Papers 2507.01970, arXiv.org.
• Peilin Rao & Randall R. Rojas, 2025. "Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation," Papers 2509.05922, arXiv.org.
• Guo, Hongfei & Marín Díazaraque, Juan Miguel & Veiga, Helena, 2025. "Beyond GARCH: Bayesian Neural Stochastic Volatility," DES - Working Papers. Statistics and Econometrics. WS 47944, Universidad Carlos III de Madrid. Departamento de Estadística.
• Lijie Ding & Egang Lu & Kin Cheung, 2025. "Deep Learning Option Pricing with Market Implied Volatility Surfaces," Papers 2509.05911, arXiv.org.
• Daniel Graeber & Lorenz Meister & Carsten Schröder & Sabine Zinn, 2025. "Random Forests for Labor Market Analysis: Balancing Precision and Interpretability," SOEPpapers on Multidisciplinary Panel Data Research 1230, DIW Berlin, The German Socio-Economic Panel (SOEP).
• Kasymkhan Khubiev & Mikhail Semenov & Irina Podlipnova, 2025. "Finance-Grounded Optimization For Algorithmic Trading," Papers 2509.04541, arXiv.org.
• Yimin Du, 2025. "Machine Learning Enhanced Multi-Factor Quantitative Trading: A Cross-Sectional Portfolio Optimization Approach with Bias Correction," Papers 2507.07107, arXiv.org.
• Yiran Wan & Xinyu Ying & Shengzhen Xu, 2025. "Automated Trading System for Straddle-Option Based on Deep Q-Learning," Papers 2509.07987, arXiv.org.
• Guillaume Coqueret & Martial Laguerre, 2025. "Overparametrized models with posterior drift," Papers 2506.23619, arXiv.org.
• Hongyi Liu, 2025. "Deep Learning for Conditional Asset Pricing Models," Papers 2509.04812, arXiv.org.
• Yang Chen & Yueheng Jiang & Zhaozhao Ma & Yuchen Cao & Jacky Keung & Kun Kuang & Leilei Gan & Yiquan Wu & Fei Wu, 2025. "MM-DREX: Multimodal-Driven Dynamic Routing of LLM Experts for Financial Trading," Papers 2509.05080, arXiv.org, revised Sep 2025.
• Arif Pathan, 2025. "Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data," Papers 2506.17244, arXiv.org.
• Tanujit Chakraborty & Donia Besher & Madhurima Panja & Shovon Sengupta, 2025. "Neural ARFIMA model for forecasting BRIC exchange rates with long memory under oil shocks and policy uncertainties," Papers 2509.06697, arXiv.org.
• Feliks Ba'nka & Jaros{l}aw A. Chudziak, 2025. "DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization," Papers 2509.12753, arXiv.org.
• Thomas R. Cook & Sophia Kazinnik, 2025. "Social Group Bias in AI Finance," Papers 2506.17490, arXiv.org.
• Ruisi Li & Xinhui Gu, 2025. "Optimization Method of Multi-factor Investment Model Driven by Deep Learning for Risk Control," Papers 2507.00332, arXiv.org.
• Aakash Kalyani & Serdar Ozkan, 2025. "Theory Meets Textual Analysis: Measuring Firm-Level Labor Cost Pressures and Inflation Pass-Through," Working Papers 2025-021, Federal Reserve Bank of St. Louis.
• Lin William Cong & Stephen Q. Yang, 2025. "Understanding Patenting Disparities via Causal Human+Machine Learning," NBER Working Papers 34197, National Bureau of Economic Research, Inc.
• Spears, Taylor C. & Hansen, Kristian Bondo & Xu, Ruowen & Millo, Yuval, 2025. "Governing Synthetic Data in the Financial Sector," SocArXiv ruxkh_v1, Center for Open Science.
• Jeff Dominitz & Charles F. Manski, 2025. "A Decision Theoretic Perspective on Artificial Superintelligence: Coping with Missing Data Problems in Prediction and Treatment Choice," Papers 2509.12388, arXiv.org.
• Adam Nelson-Archer & Aleia Sen & Meena Al Hasani & Sofia Davila & Jessica Le & Omar Abbouchi, 2025. "Forecasting Labor Markets with LSTNet: A Multi-Scale Deep Learning Approach," Papers 2507.01979, arXiv.org.
• Michael Monoyios & Olivia Pricilia, 2025. "Neural Functionally Generated Portfolios," Papers 2506.19715, arXiv.org.
• Amarendra Sharma, 2025. "P-CRE-DML: A Novel Approach for Causal Inference in Non-Linear Panel Data," Papers 2506.23297, arXiv.org.
• Tennant, Elizabeth J. & Michuda, Aleksandr & Upton, Joanna B. & Chamorro, Andres & Engstrom, Ryan & Mann, Michael L. & Newhouse, David & Weber, Michael & Barrett, Christopher B., 2025. "Nowcasting Disruptions to Human Capital Formation : Evidence from High-Frequency Household and Geospatial Data in Rural Malawi," Policy Research Working Paper Series 11202, The World Bank.
• Yuming Ma, 2025. "Myopic Optimality: why reinforcement learning portfolio management strategies lose money," Papers 2509.12764, arXiv.org.
• Item repec:bge:wpaper:1505 is not listed on IDEAS anymore