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Report NEP-BIG-2024-09-02
This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé (Tom Coupe) issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-BIG
The following items were announced in this report:
- Bonelli, Maxime, 2023, "Data-driven Investors," HEC Research Papers Series, HEC Paris, number 1470, Feb, DOI: 10.2139/ssrn.4362173.
- 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.
- Melissa Dell, 2024, "Deep Learning for Economists," Papers, arXiv.org, number 2407.15339, Jul, revised Nov 2024.
- 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.
- 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.
- Patrick Rehill, 2024, "Distilling interpretable causal trees from causal forests," Papers, arXiv.org, number 2408.01023, Aug.
- Fernando Berzal & Alberto Garcia, 2024, "Beyond Trend Following: Deep Learning for Market Trend Prediction," Papers, arXiv.org, number 2407.13685, Jun.
- Han Gui, 2024, "Machine learning in weekly movement prediction," Papers, arXiv.org, number 2407.09831, Jul.
- 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.
- Chunhui Qiao & Xiangwei Wan, 2024, "Enhancing Black-Scholes Delta Hedging via Deep Learning," Papers, arXiv.org, number 2407.19367, Jul, revised Aug 2024.
- 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.
- 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.
- Atin Aboutorabi & Ga'etan de Rassenfosse, 2024, "Nowcasting R&D Expenditures: A Machine Learning Approach," Papers, arXiv.org, number 2407.11765, Jul.
- A Samuel Pottinger & Lawson Connor & Brookie Guzder-Williams & Maya Weltman-Fahs & Nick Gondek & Timothy Bowles, 2024, "Climate-Driven Doubling of U.S. Maize Loss Probability: Interactive Simulation with Neural Network Monte Carlo," Papers, arXiv.org, number 2408.02217, Aug, revised Dec 2024.
- Nacira Agram & Bernt {O}ksendal & Jan Rems, 2024, "Deep learning for quadratic hedging in incomplete jump market," Papers, arXiv.org, number 2407.13688, Jun.
- Yuxin Liu & Jimin Lin & Achintya Gopal, 2024, "NeuralBeta: Estimating Beta Using Deep Learning," Papers, arXiv.org, number 2408.01387, Aug, revised Oct 2024.
- Zijie Pan & Stepan Gordeev & Jiahui Zhao & Ziyi Meng & Caiwen Ding & Sandro Steinbach & Dongjin Song, 2024, "International Trade Flow Prediction with Bilateral Trade Provisions," Papers, arXiv.org, number 2407.13698, Jun.
- Flora Lutz & Yuanchen Yang & Chengyu Huang, 2024, "Public Perceptions of Canada’s Investment Climate," IMF Working Papers, International Monetary Fund, number 2024/165, Jul.
- 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.
- 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.
- 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.
- 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.
- C'esar Pedrosa Soares, 2024, "Leveraging Natural Language and Item Response Theory Models for ESG Scoring," Papers, arXiv.org, number 2407.20377, Jul.
- 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.
- Qiqin Zhou, 2024, "Explainable AI in Request-for-Quote," Papers, arXiv.org, number 2407.15038, Jul.
- Andres, Raphaela & Niebel, Thomas & Sack, Robin, 2024, "Big data and firm-level productivity: A cross-country comparison," ZEW Discussion Papers, ZEW - Leibniz Centre for European Economic Research, number 24-053.
- 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.
- 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.