Report NEP-CMP-2024-09-02

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:

• Chen Zhang & Giovanni Amici & Marco Morandotti, 2024, "Calibrating the Heston model with deep differential networks," Papers, arXiv.org, number 2407.15536, Jul, revised Jan 2025.
• Fernando Berzal & Alberto Garcia, 2024, "Beyond Trend Following: Deep Learning for Market Trend Prediction," Papers, arXiv.org, number 2407.13685, Jun.
• Melissa Dell, 2024, "Deep Learning for Economists," Papers, arXiv.org, number 2407.15339, Jul, revised Nov 2024.
• Hurlin, Christophe & Pérignon, Christophe, 2023, "Machine Learning and IRB Capital Requirements: Advantages, Risks, and Recommendations," HEC Research Papers Series, HEC Paris, number 1480, Jun, DOI: 10.2139/ssrn.4483793.
• Liyang Wang & Yu Cheng & Xingxin Gu & Zhizhong Wu, 2024, "Design and Optimization of Big Data and Machine Learning-Based Risk Monitoring System in Financial Markets," Papers, arXiv.org, number 2407.19352, Jul.
• Nacira Agram & Bernt {O}ksendal & Jan Rems, 2024, "Deep learning for quadratic hedging in incomplete jump market," Papers, arXiv.org, number 2407.13688, Jun.
• Chunhui Qiao & Xiangwei Wan, 2024, "Enhancing Black-Scholes Delta Hedging via Deep Learning," Papers, arXiv.org, number 2407.19367, Jul, revised Aug 2024.
• Han Gui, 2024, "Machine learning in weekly movement prediction," Papers, arXiv.org, number 2407.09831, Jul.
• Patrick Rehill, 2024, "Distilling interpretable causal trees from causal forests," Papers, arXiv.org, number 2408.01023, Aug.
• Cesare Carissimo & Marcin Korecki, 2024, "Capital as Artificial Intelligence," Papers, arXiv.org, number 2407.16314, Jul.
• Aditya Saxena & Dr Parizad Dungore, 2024, "Credit Risk Assessment Model for UAE Commercial Banks: A Machine Learning Approach," Papers, arXiv.org, number 2407.12044, Jul.
• Dangxing Chen & Yuan Gao, 2024, "Attribution Methods in Asset Pricing: Do They Account for Risk?," Papers, arXiv.org, number 2407.08953, Jul.
• Siqiao Zhao & Zhikang Dong & Zeyu Cao & Raphael Douady, 2024, "Hedge Fund Portfolio Construction Using PolyModel Theory and iTransformer," Papers, arXiv.org, number 2408.03320, Aug, revised Feb 2025.
• Alex Kim & Maximilian Muhn & Valeri Nikolaev, 2024, "Financial Statement Analysis with Large Language Models," Papers, arXiv.org, number 2407.17866, Jul, revised Feb 2025.
• Alireza Mohammadshafie & Akram Mirzaeinia & Haseebullah Jumakhan & Amir Mirzaeinia, 2024, "Deep Reinforcement Learning Strategies in Finance: Insights into Asset Holding, Trading Behavior, and Purchase Diversity," Papers, arXiv.org, number 2407.09557, Jun.
• Qiqin Zhou, 2024, "Explainable AI in Request-for-Quote," Papers, arXiv.org, number 2407.15038, Jul.
• Felix Drinkall & Janet B. Pierrehumbert & Stefan Zohren, 2024, "Forecasting Credit Ratings: A Case Study where Traditional Methods Outperform Generative LLMs," Papers, arXiv.org, number 2407.17624, Jul, revised Jan 2025.
• Simon Hirsch & Jonathan Berrisch & Florian Ziel, 2024, "Online Distributional Regression," Papers, arXiv.org, number 2407.08750, Jun, revised Aug 2025.
• Yuan Li & Bingqiao Luo & Qian Wang & Nuo Chen & Xu Liu & Bingsheng He, 2024, "A Reflective LLM-based Agent to Guide Zero-shot Cryptocurrency Trading," Papers, arXiv.org, number 2407.09546, Jun.
• Bonelli, Maxime, 2023, "Data-driven Investors," HEC Research Papers Series, HEC Paris, number 1470, Feb, DOI: 10.2139/ssrn.4362173.
• Hafsa Lemsieh & Ibtissam Abarar, 2024, "Artificial Intelligence: A Technological Tool to Manipulate the Psychology and Behavior of Consumers: Theoretical research," Post-Print, HAL, number hal-04629533, Jun, DOI: 10.5281/zenodo.12186137.
• Adam Bahelka & Harmen de Weerd, 2024, "Comparative analysis of Mixed-Data Sampling (MIDAS) model compared to Lag-Llama model for inflation nowcasting," Papers, arXiv.org, number 2407.08510, Jul.
• Xiaohui Victor Li & Francesco Sanna Passino, 2024, "FinDKG: Dynamic Knowledge Graphs with Large Language Models for Detecting Global Trends in Financial Markets," Papers, arXiv.org, number 2407.10909, Jul, revised Oct 2024.
• Atin Aboutorabi & Ga'etan de Rassenfosse, 2024, "Nowcasting R&D Expenditures: A Machine Learning Approach," Papers, arXiv.org, number 2407.11765, Jul.
• Si, Yafei & Yang, Yuyi & Wang, Xi & An, Ruopeng & Zu, Jiaqi & Chen, Xi & Fan, Xiaojing & Gong, Sen, 2024, "Quality and Accountability of Large Language Models (LLMs) in Healthcare in Low- and Middle-Income Countries (LMIC): A Simulated Patient Study using ChatGPT," GLO Discussion Paper Series, Global Labor Organization (GLO), number 1472.
• Vincent Ragel & Damien Challet, 2024, "Consistent time travel for realistic interactions with historical data: reinforcement learning for market making," Papers, arXiv.org, number 2408.02322, Aug, revised Jan 2025.
• Lionel Fontagn'e & Francesca Micocci & Armando Rungi, 2024, "The heterogeneous impact of the EU-Canada agreement with causal machine learning," Papers, arXiv.org, number 2407.07652, Jul, revised Apr 2025.
• Item repec:ags:cfcp15:344233 is not listed on IDEAS anymore

• Preetha Saha & Jingrao Lyu & Dhruv Desai & Rishab Chauhan & Jerinsh Jeyapaulraj & Philip Sommer & Dhagash Mehta, 2024, "Machine Learning-based Relative Valuation of Municipal Bonds," Papers, arXiv.org, number 2408.02273, Aug.
• Mingshu Li & Bhaskarjit Sarmah & Dhruv Desai & Joshua Rosaler & Snigdha Bhagat & Philip Sommer & Dhagash Mehta, 2024, "Quantile Regression using Random Forest Proximities," Papers, arXiv.org, number 2408.02355, Aug.
• Yoontae Hwang & Stefan Zohren & Yongjae Lee, 2024, "Temporal Representation Learning for Stock Similarities and Its Applications in Investment Management," Papers, arXiv.org, number 2407.13751, Jul.
• Flora Lutz & Yuanchen Yang & Chengyu Huang, 2024, "Public Perceptions of Canada’s Investment Climate," IMF Working Papers, International Monetary Fund, number 2024/165, Jul.
• Inacio F. Araujo & Kieran P. Donaghy & Eduardo A. Haddad & Geoffrey J.D. Hewings, 2024, "Geographical Propagation of the Economic Impacts of the ISIS Conflict in Iraq," TD NEREUS, Núcleo de Economia Regional e Urbana da Universidade de São Paulo (NEREUS), number 6-2024, Aug.
• Amirreza Talebi, 2024, "Simulation in discrete choice models evaluation: SDCM, a simulation tool for performance evaluation of DCMs," Papers, arXiv.org, number 2407.17014, Jul, revised Jan 2026.
• Pascal Franc{c}ois & Genevi`eve Gauthier & Fr'ed'eric Godin & Carlos Octavio P'erez Mendoza, 2024, "Is the difference between deep hedging and delta hedging a statistical arbitrage?," Papers, arXiv.org, number 2407.14736, Jul, revised Oct 2024.

Report NEP-CMP-2024-09-02

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: