Report NEP-BIG-2024-08-26

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:

• Abdul Jabbar & Syed Qaisar Jalil, 2024, "A Comprehensive Analysis of Machine Learning Models for Algorithmic Trading of Bitcoin," Papers, arXiv.org, number 2407.18334, Jul.
• Wolff, Dominik & Echterling, Fabian, 2024, "Stock picking with machine learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL), Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), number 149079, Jan.
• Undral Byambadalai & Tatsushi Oka & Shota Yasui, 2024, "Estimating Distributional Treatment Effects in Randomized Experiments: Machine Learning for Variance Reduction," Papers, arXiv.org, number 2407.16037, Jul.
• Alejandra de la Rica Escudero & Eduardo C. Garrido-Merchan & Maria Coronado-Vaca, 2024, "Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent," Papers, arXiv.org, number 2407.14486, Jul.
• Höschle, Lisa & Yu, Xiaohua, 2023, "Food Price Dynamics in OECD Countries--Evidence on Clusters and Predictors from Machine Learning," GEWISOLA 63rd Annual Conference, Goettingen, Germany, September 20-22, 2023, GEWISOLA, number 344249, Sep, DOI: 10.22004/ag.econ.344249.
• Tiago Monteiro, 2024, "AI-Powered Energy Algorithmic Trading: Integrating Hidden Markov Models with Neural Networks," Papers, arXiv.org, number 2407.19858, Jul, revised Nov 2025.
• Andrew Green, 2024, "Artificial intelligence and the changing demand for skills in the labour market," OECD Artificial Intelligence Papers, OECD Publishing, number 14, Apr, DOI: 10.1787/88684e36-en.
• Andrew Green, 2024, "Artificial intelligence and the changing demand for skills in Canada: The increasing importance of social skills," OECD Artificial Intelligence Papers, OECD Publishing, number 17, May, DOI: 10.1787/1b20cdb6-en.
• Taha Barwahwala & Aprajit Mahajan & Shekhar Mittal & Ofir Reich, 2024, "Is Model Accuracy Enough? A Field Evaluation Of A Machine Learning Model To Catch Bogus Firms," NBER Working Papers, National Bureau of Economic Research, Inc, number 32705, Jul.
• Shi, Chengchun & Qi, Zhengling & Wang, Jianing & Zhou, Fan, 2023, "Value enhancement of reinforcement learning via efficient and robust trust region optimization," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 122756, Jul.
• Item repec:ags:cfcp15:344359 is not listed on IDEAS anymore

• Ludovic Goudenege & Andrea Molent & Antonino Zanette, 2024, "Leveraging Machine Learning for High-Dimensional Option Pricing within the Uncertain Volatility Model," Papers, arXiv.org, number 2407.13213, Jul, revised Jun 2025.
• Kerri Lu & Dan M. Kluger & Stephen Bates & Sherrie Wang, 2024, "Regression coefficient estimation from remote sensing maps," Papers, arXiv.org, number 2407.13659, Jul, revised Jul 2025.
• Francesca Borgonovi & Flavio Calvino & Chiara Criscuolo & Lea Samek & Helke Seitz & Julia Nania & Julia Nitschke & Layla O’Kane, 2023, "Emerging trends in AI skill demand across 14 OECD countries," OECD Artificial Intelligence Papers, OECD Publishing, number 2, Oct, DOI: 10.1787/7c691b9a-en.
• Wenbo Yan & Ying Tan, 2024, "TCGPN: Temporal-Correlation Graph Pre-trained Network for Stock Forecasting," Papers, arXiv.org, number 2407.18519, Jul.
• Kamesh Korangi & Christophe Mues & Cristi'an Bravo, 2024, "Large-scale Time-Varying Portfolio Optimisation using Graph Attention Networks," Papers, arXiv.org, number 2407.15532, Jul, revised Feb 2025.
• Tian Guo & Emmanuel Hauptmann, 2024, "Fine-Tuning Large Language Models for Stock Return Prediction Using Newsflow," Papers, arXiv.org, number 2407.18103, Jul, revised Aug 2024.
• St'ephane Cr'epey & Botao Li & Hoang Nguyen & Bouazza Saadeddine, 2024, "CVA Sensitivities, Hedging and Risk," Papers, arXiv.org, number 2407.18583, Jul.
• Joel P. Villarino & 'Alvaro Leitao, 2024, "On Deep Learning for computing the Dynamic Initial Margin and Margin Value Adjustment," Papers, arXiv.org, number 2407.16435, Jul.
• Arnone, Massimo & Leogrande, Angelo, 2024, "The Sustainability of the Factoring Chain in Europe in the Light of the Integration of ESG Factors," MPRA Paper, University Library of Munich, Germany, number 121342, Jun.
• Bonelli, Maxime & Foucault, Thierry, 2023, "Displaced by Big Data: Evidence from Active Fund Managers," HEC Research Papers Series, HEC Paris, number 1491, Aug, DOI: 10.2139/ssrn.4527672.
• Ma, Tao & Yang, Xuzhi & Szabo, Zoltan, 2024, "To switch or not to switch? Balanced policy switching in offline reinforcement learning," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 124144, Jul.
• Hongshen Yang & Avinash Malik, 2024, "Reinforcement Learning Pair Trading: A Dynamic Scaling approach," Papers, arXiv.org, number 2407.16103, Jul, revised Dec 2024.
• Chung I Lu & Julian Sester, 2024, "Generative modelling of financial time series with structured noise and MMD-based signature learning," Papers, arXiv.org, number 2407.19848, Jul, revised Nov 2025.
• Beckert, Jens & Arndt, H. Lukas R., 2024, "The Greek tragedy: Narratives and imagined futures in the Greek sovereign debt crisis," MPIfG Discussion Paper, Max Planck Institute for the Study of Societies, number 24/4.
• Li, Jie & Fearnhead, Paul & Fryzlewicz, Piotr & Wang, Tengyao, 2024, "Automatic change-point detection in time series via deep learning," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 120083, Apr.
• Mahdi Ebrahimi Kahou & Jesus Fernandez-Villaverde & Sebastian Gomez-Cardona & Jesse Perla & Jan Rosa, 2024, "Spooky Boundaries at a Distance: Inductive Bias, Dynamic Models, and Behavioral Macro," PIER Working Paper Archive, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, number 24-019, Aug.
• Foltas, Alexander, 2024, "Inefficient forecast narratives: A BERT-based approach," Working Papers, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin, number 45, DOI: 10.18452/29133.
• Abe C. Dunn & Eric English & Kyle K. Hood & Lowell Mason & Brian Quistorff, 2024, "Expanding the Frontier of Economic Statistics Using Big Data: A Case Study of Regional Employment," BEA Papers, Bureau of Economic Analysis, number 0128, Aug.
• Shi, Chengchun & Zhou, Yunzhe & Li, Lexin, 2024, "Testing directed acyclic graph via structural, supervised and generative adversarial learning," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 119446, Dec.
• Davillas, Apostolos & Jones, Andrew M., 2024, "Biological Age and Predicting Future Health Care Utilisation," IZA Discussion Papers, IZA Network @ LISER, number 17159, Jul.
• Adria Pop & Jan Sporer, 2024, "The Structure of Financial Equity Research Reports -- Identification of the Most Frequently Asked Questions in Financial Analyst Reports to Automate Equity Research Using Llama 3 and GPT-4," Papers, arXiv.org, number 2407.18327, Jul, revised Jun 2025.
• Shengkun Wang & Taoran Ji & Jianfeng He & Mariam Almutairi & Dan Wang & Linhan Wang & Min Zhang & Chang-Tien Lu, 2024, "AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction," Papers, arXiv.org, number 2407.18324, Jul.
• Gulfam Haider & Laiba Zubair & Aman Saleem, 2024, "Big Data Analytics-Enabled Dynamic Capabilities and Market Performance: Examining the Roles of Marketing Ambidexterity and Competitor Pressure," Papers, arXiv.org, number 2407.15522, Jul.
• Guan-Yuan Wang, 2022, "Churn Prediction for High-Value Players in Freemium Mobile Games: Using Random Under-Sampling," Post-Print, HAL, number hal-04632443, Dec, DOI: 10.54694/stat.2022.18.
• Yacine Aït-Sahalia & Chen Xu Li & Chenxu Li, 2024, "So Many Jumps, So Few News," NBER Working Papers, National Bureau of Economic Research, Inc, number 32746, Jul.

Report NEP-BIG-2024-08-26

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: