Report NEP-BIG-2023-08-21

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé 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:

• Item repec:bea:wpaper:0209 is not listed on IDEAS anymore

• Apostolos Ampountolas, 2023. "Comparative Analysis of Machine Learning, Hybrid, and Deep Learning Forecasting Models Evidence from European Financial Markets and Bitcoins," Papers 2307.08853, arXiv.org.
• Robert Balkin & Hector D. Ceniceros & Ruimeng Hu, 2023. "Stochastic Delay Differential Games: Financial Modeling and Machine Learning Algorithms," Papers 2307.06450, arXiv.org.
• Laurens Van Mieghem & Antonis Papapantoleon & Jonas Papazoglou-Hennig, 2023. "Machine learning for option pricing: an empirical investigation of network architectures," Papers 2307.07657, arXiv.org.
• Valentin Lourme, 2023. "analysis of the predictor of a volatility surface by machine learning [Analyse de la prédiction d'une nappe de volatilité par Machine Learning]," Post-Print hal-04151604, HAL.
• Andrii Babii & Ryan T. Ball & Eric Ghysels & Jonas Striaukas, 2023. "Panel Data Nowcasting: The Case of Price-Earnings Ratios," Papers 2307.02673, arXiv.org.
• Ali Lashgari, 2023. "Harnessing the Potential of Volatility: Advancing GDP Prediction," Papers 2307.05391, arXiv.org.
• Johanna Deperi & Ludovic Dibiaggio & Mohamed Keita & Lionel Nesta, 2023. "Ideas Without Scale in French Artificial Intelligence Innovations," Post-Print hal-04144817, HAL.
• Yuanchen Yang & Chengyu Huang & Yuchen Zhang, 2023. "Decomposing Climate Risks in Stock Markets," IMF Working Papers 2023/141, International Monetary Fund.
• Zhu Bangyuan, 2023. "Critical comparisons on deep learning approaches for foreign exchange rate prediction," Papers 2307.06600, arXiv.org.
• Tom Liu & Stephen Roberts & Stefan Zohren, 2023. "Deep Inception Networks: A General End-to-End Framework for Multi-asset Quantitative Strategies," Papers 2307.05522, arXiv.org.
• Mu-Jeung Yang, 2023. "Patent news shocks help forecast establishment dynamics," CES Technical Notes Series 23-13, Center for Economic Studies, U.S. Census Bureau.
• Lukas Fervers, 2023. "Power boost or source of bias? Monte Carlo evidence on ML covariate adjustment in randomized trials in education," German Stata Conference 2023 10, Stata Users Group.
• Lucas Potin & Rosa Figueiredo & Vincent Labatut & Christine Largeron, 2023. "Pattern Mining for Anomaly Detection in Graphs: Application to Fraud in Public Procurement," Post-Print hal-04131485, HAL.
• Christopher Gerling, 2023. "Company2Vec -- German Company Embeddings based on Corporate Websites," Papers 2307.09332, arXiv.org.
• Marc Burri, 2023. "Do daily lead texts help nowcasting GDP growth?," IRENE Working Papers 23-02, IRENE Institute of Economic Research.
• Foltas, Alexander, 2023. "Quantifying priorities in business cycle reports: Analysis of recurring textual patterns around peaks and troughs," Working Papers 44, German Research Foundation's Priority Programme 1859 "Experience and Expectation. Historical Foundations of Economic Behaviour", Humboldt University Berlin.
• Christian Fieberg & Lars Hornuf & David J. Streich, 2023. "Using GPT-4 for Financial Advice," CESifo Working Paper Series 10529, CESifo.
• Chung I Lu, 2023. "Evaluation of Deep Reinforcement Learning Algorithms for Portfolio Optimisation," Papers 2307.07694, arXiv.org, revised Aug 2025.
• Zhou, Yunzhe & Shi, Chengchun & Li, Lexin & Yao, Qiwei, 2023. "Testing for the Markov property in time series via deep conditional generative learning," LSE Research Online Documents on Economics 119352, London School of Economics and Political Science, LSE Library.