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Report NEP-BIG-2024-06-10
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:
- Hulusi Mehmet Tanrikulu & Hakan Pabuccu, 2024, "The Effect of Data Types' on the Performance of Machine Learning Algorithms for Financial Prediction," Papers, arXiv.org, number 2404.19324, Apr.
- Pedro Duarte Gomes, 2024, "Mathematics of Differential Machine Learning in Derivative Pricing and Hedging," Papers, arXiv.org, number 2405.01233, May.
- Pierre Beck & Pablo Garcia Sanchez & Alban Moura & Julien Pascal & Olivier Pierrard, 2024, "Deep learning solutions of DSGE models: A technical report," BCL working papers, Central Bank of Luxembourg, number 184, May.
- Minwu Kim & Sidahmed Benabderrahmane & Talal Rahwan, 2024, "Interpretable Machine Learning Models for Predicting the Next Targets of Activist Funds," Papers, arXiv.org, number 2404.16169, Apr, revised Sep 2025.
- Silvia Garc'ia-M'endez & Francisco de Arriba-P'erez & Ana Barros-Vila & Francisco J. Gonz'alez-Casta~no, 2024, "Targeted aspect-based emotion analysis to detect opportunities and precaution in financial Twitter messages," Papers, arXiv.org, number 2404.08665, Mar.
- Shuochen Bi & Wenqing Bao & Jue Xiao & Jiangshan Wang & Tingting Deng, 2024, "Application and practice of AI technology in quantitative investment," Papers, arXiv.org, number 2404.18184, Apr.
- Prabhu Prasad Panda & Maysam Khodayari Gharanchaei & Xilin Chen & Haoshu Lyu, 2024, "Application of Deep Learning for Factor Timing in Asset Management," Papers, arXiv.org, number 2404.18017, Apr.
- Yupeng Cao & Zhi Chen & Qingyun Pei & Nathan Jinseok Lee & K. P. Subbalakshmi & Papa Momar Ndiaye, 2024, "ECC Analyzer: Extract Trading Signal from Earnings Conference Calls using Large Language Model for Stock Performance Prediction," Papers, arXiv.org, number 2404.18470, Apr, revised Aug 2024.
- Shuochen Bi & Wenqing Bao, 2024, "Innovative Application of Artificial Intelligence Technology in Bank Credit Risk Management," Papers, arXiv.org, number 2404.18183, Apr.
- Patrick Rehill, 2024, "How do applied researchers use the Causal Forest? A methodological review of a method," Papers, arXiv.org, number 2404.13356, Apr, revised Dec 2024.
- Yihang Fu & Mingyu Zhou & Luyao Zhang, 2024, "DAM: A Universal Dual Attention Mechanism for Multimodal Timeseries Cryptocurrency Trend Forecasting," Papers, arXiv.org, number 2405.00522, May.
- Kina, Mehmet Fuat, 2024, "Exploring Recent Ideological Divides in Turkey: Political and Cultural Axes," SocArXiv, Center for Open Science, number kp7s2, May, DOI: 10.31219/osf.io/kp7s2.
- Huan-Yi Su & Ke Wu & Yu-Hao Huang & Wu-Jun Li, 2024, "NumLLM: Numeric-Sensitive Large Language Model for Chinese Finance," Papers, arXiv.org, number 2405.00566, May.
- Jacob Fein-Ashley, 2024, "A Comparison of Traditional and Deep Learning Methods for Parameter Estimation of the Ornstein-Uhlenbeck Process," Papers, arXiv.org, number 2404.11526, Apr, revised Apr 2024.
- Claudio Bellei & Muhua Xu & Ross Phillips & Tom Robinson & Mark Weber & Tim Kaler & Charles E. Leiserson & Arvind & Jie Chen, 2024, "The Shape of Money Laundering: Subgraph Representation Learning on the Blockchain with the Elliptic2 Dataset," Papers, arXiv.org, number 2404.19109, Apr, revised Jul 2024.
- Camilo Umana Dajud, 2024, "Nowcasting the growth rate of the ICT sector," OECD Digital Economy Papers, OECD Publishing, number 362, May, DOI: 10.1787/eb4938a0-en.
- Riccardo Di Francesco, 2024, "Ordered Correlation Forest," CEIS Research Paper, Tor Vergata University, CEIS, number 577, May, revised 06 May 2024.
- Kasy, Maximilian, 2024, "Algorithmic Bias and Racial Inequality: A Critical Review," IZA Discussion Papers, IZA Network @ LISER, number 16944, Apr.
- Gabriele Ciminelli & Antton Haramboure & Lea Samek & Cyrille Schwellnus & Allison Shrivastava & Tara Sinclair, 2024, "Occupational reallocation and mismatch in the wake of the Covid-19 pandemic: Cross-country evidence from an online job site," OECD Productivity Working Papers, OECD Publishing, number 35, May, DOI: 10.1787/128b92aa-en.