Report NEP-CMP-2025-09-22

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:

• Patrick J. Laub & Tu Pho & Bernard Wong, 2025, "An Interpretable Deep Learning Model for General Insurance Pricing," Papers, arXiv.org, number 2509.08467, Sep.
• 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, arXiv.org, number 2509.05080, Sep, revised Sep 2025.
• Yiran Wan & Xinyu Ying & Shengzhen Xu, 2025, "Automated Trading System for Straddle-Option Based on Deep Q-Learning," Papers, arXiv.org, number 2509.07987, Aug.
• Feliks Ba'nka & Jaros{l}aw A. Chudziak, 2025, "DeltaHedge: A Multi-Agent Framework for Portfolio Options Optimization," Papers, arXiv.org, number 2509.12753, Sep.
• 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, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen, number 1163, DOI: 10.4419/96973348.
• Michael Monoyios & Olivia Pricilia, 2025, "Neural Functionally Generated Portfolios," Papers, arXiv.org, number 2506.19715, Jun.
• Adrian Iulian Cristescu & Matteo Giordano, 2025, "A comparative analysis of machine learning algorithms for predicting probabilities of default," Papers, arXiv.org, number 2506.19789, Jun.
• Kasymkhan Khubiev & Mikhail Semenov & Irina Podlipnova & Dinara Khubieva, 2025, "Finance-Grounded Optimization For Algorithmic Trading," Papers, arXiv.org, number 2509.04541, Sep, revised Jan 2026.
• Ruben Bontorno & Songyan Hou, 2025, "Nested Optimal Transport Distances," Papers, arXiv.org, number 2509.06702, Sep.
• Rafael Zimmer & Oswaldo Luiz do Valle Costa, 2025, "Reinforcement Learning-Based Market Making as a Stochastic Control on Non-Stationary Limit Order Book Dynamics," Papers, arXiv.org, number 2509.12456, Sep.
• Yimin Du, 2025, "Machine Learning Enhanced Multi-Factor Quantitative Trading: A Cross-Sectional Portfolio Optimization Approach with Bias Correction," Papers, arXiv.org, number 2507.07107, Jun.
• Alejandro Rodriguez Dominguez, 2025, "Causal PDE-Control for Adaptive Portfolio Optimization under Partial Information," Papers, arXiv.org, number 2509.09585, Sep, revised Nov 2025.
• Peilin Rao & Randall R. Rojas, 2025, "Predicting Market Troughs: A Machine Learning Approach with Causal Interpretation," Papers, arXiv.org, number 2509.05922, Sep.
• Lijie Ding & Egang Lu & Kin Cheung, 2025, "Deep Learning Option Pricing with Market Implied Volatility Surfaces," Papers, arXiv.org, number 2509.05911, Sep.
• Ayaan Qayyum, 2025, "News Sentiment Embeddings for Stock Price Forecasting," Papers, arXiv.org, number 2507.01970, Jun.
• Yuming Ma, 2025, "Myopic Optimality: why reinforcement learning portfolio management strategies lose money," Papers, arXiv.org, number 2509.12764, Sep.
• Hadi Keramati & Samaneh Jazayeri, 2025, "Accelerated Portfolio Optimization and Option Pricing with Reinforcement Learning," Papers, arXiv.org, number 2507.01972, Jun.
• 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, DIW Berlin, The German Socio-Economic Panel (SOEP), number 1230.
• Guo, Hongfei & Marín Díazaraque, Juan Miguel & Veiga, Helena, 2025, "Learning Volatility:A Bayesian Neural Stochastic Framework," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 47944, Sep.
• Guillaume Coqueret & Martial Laguerre, 2025, "Overparametrized models with posterior drift," Papers, arXiv.org, number 2506.23619, Jun.
• Arif Pathan, 2025, "Transformers Beyond Order: A Chaos-Markov-Gaussian Framework for Short-Term Sentiment Forecasting of Any Financial OHLC timeseries Data," Papers, arXiv.org, number 2506.17244, Jun.
• 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, arXiv.org, number 2509.06697, Sep.
• Amarendra Sharma, 2025, "P-CRE-DML: A Novel Approach for Causal Inference in Non-Linear Panel Data," Papers, arXiv.org, number 2506.23297, Jun.
• 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, arXiv.org, number 2507.01979, Jun.
• Spears, Taylor C. & Hansen, Kristian Bondo & Xu, Ruowen & Millo, Yuval, 2025, "Governing Synthetic Data in the Financial Sector," SocArXiv, Center for Open Science, number ruxkh_v1, Sep, DOI: 10.31219/osf.io/ruxkh_v1.
• Hongyi Liu, 2025, "Deep Learning for Conditional Asset Pricing Models," Papers, arXiv.org, number 2509.04812, Sep.
• Ruisi Li & Xinhui Gu, 2025, "Optimization Method of Multi-factor Investment Model Driven by Deep Learning for Risk Control," Papers, arXiv.org, number 2507.00332, Jun.
• Junjie Guo, 2025, "Integration of Wavelet Transform Convolution and Channel Attention with LSTM for Stock Price Prediction based Portfolio Allocation," Papers, arXiv.org, number 2507.01973, Jun, revised Jul 2025.
• Anton Korinek, 2025, "AI Agents for Economic Research," NBER Working Papers, National Bureau of Economic Research, Inc, number 34202, Sep.
• Luan, Qinmeng & Hamp, James, 2025, "Automated regime classification in multidimensional time series data using sliced Wasserstein k-means clustering," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 129537, Aug.
• Alfred Backhouse & Kang Li & Jakob Foerster & Anisoara Calinescu & Stefan Zohren, 2025, "Painting the market: generative diffusion models for financial limit order book simulation and forecasting," Papers, arXiv.org, number 2509.05107, Sep.
• Ross Koval & Nicholas Andrews & Xifeng Yan, 2025, "Context-Aware Language Models for Forecasting Market Impact from Sequences of Financial News," Papers, arXiv.org, number 2509.12519, Sep.
• Yuting Su & Taizhong Hu & Zhenfeng Zou, 2025, "Extreme-case Range Value-at-Risk under Increasing Failure Rate," Papers, arXiv.org, number 2506.23073, Jun.
• Emerson Melo, 2025, "Learning in Random Utility Models Via Online Decision Problems," Papers, arXiv.org, number 2506.16030, Jun.
• Andr'es Aradillas Fern'andez & Jos'e Blanchet & Jos'e Luis Montiel Olea & Chen Qiu & Jorg Stoye & Lezhi Tan, 2025, "Epsilon-Minimax Solutions of Statistical Decision Problems," Papers, arXiv.org, number 2509.08107, Sep, revised Jan 2026.

Report NEP-CMP-2025-09-22

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: