Report NEP-BIG-2023-02-06

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.

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Other reports in NEP-BIG

The following items were announced in this report:

• Pedro Garcia-del-Barrio & J. James Reade, 2023. "The Impact of Uncertainty on Fan Interest Surrounding Multiple Outcomes in Open European Football Leagues," Economics Discussion Papers em-dp2023-02, Department of Economics, University of Reading.
• Guijin Son & Hanwool Lee & Nahyeon Kang & Moonjeong Hahm, 2023. "Removing Non-Stationary Knowledge From Pre-Trained Language Models for Entity-Level Sentiment Classification in Finance," Papers 2301.03136, arXiv.org, revised Jan 2023.
• Jeremi Assael & Thibaut Heurtebize & Laurent Carlier & François Soupé, 2023. "Greenhouse gases emissions: estimating corporate non-reported emissions using interpretable machine learning," Working Papers hal-03905325, HAL.
• Thomas Wong & Mauricio Barahona, 2022. "Online learning techniques for prediction of temporal tabular datasets with regime changes," Papers 2301.00790, arXiv.org, revised Aug 2023.
• Gianandrea Lanzara & Sara Lazzaroni & Paolo Masella & Mara P. Squicciarini, 2023. "Do Bishops Matter for Politics? Evidence From Italy," Working Papers wp1179, Dipartimento Scienze Economiche, Universita' di Bologna.
• Rayane Hanifi & Klodiana Istrefi & Adrian Penalver, 2022. "Central Bank Communication of Uncertainty," Working papers 898, Banque de France.
• Xiaohong Chen & Yuan Liao & Weichen Wang, 2022. "Inference on Time Series Nonparametric Conditional Moment Restrictions Using General Sieves," Papers 2301.00092, arXiv.org, revised Jan 2023.
• Szabolcs Nagy & Noemi Hajdu, 2022. "Consumer acceptance of the use of artificial intelligence in online shopping: evidence from Hungary," Papers 2301.01277, arXiv.org.
• Huyên Pham & Xavier Warin, 2024. "Mean-field neural networks-based algorithms for McKean-Vlasov control problems ," Working Papers hal-03900810, HAL.
• Fateme Shahabi Nejad & Mohammad Mehdi Ebadzadeh, 2023. "Stock market forecasting using DRAGAN and feature matching," Papers 2301.05693, arXiv.org.
• Yves-C'edric Bauwelinckx & Jan Dhaene & Tim Verdonck & Milan van den Heuvel, 2023. "On the causality-preservation capabilities of generative modelling," Papers 2301.01109, arXiv.org.
• Jiwon Kim & Moon-Ju Kang & KangHun Lee & HyungJun Moon & Bo-Kwan Jeon, 2023. "Deep Reinforcement Learning for Asset Allocation: Reward Clipping," Papers 2301.05300, arXiv.org.
• Tanja Aue & Adam Jatowt & Michael Farber, 2022. "Predicting Companies' ESG Ratings from News Articles Using Multivariate Timeseries Analysis," Papers 2212.11765, arXiv.org.
• Eugenia Go & Kentaro Nakajima & Yasuyuki Sawada & Kiyoshi Taniguchi, 2023. "Satellite-Based Vehicle Flow Data to Assess Local Economic Activities," CIRJE F-Series CIRJE-F-1209, CIRJE, Faculty of Economics, University of Tokyo.
• Scoggins, Bermond & Robertson, Matthew P., 2023. "Measuring Transparency in the Social Sciences: Political Science and International Relations," I4R Discussion Paper Series 14, The Institute for Replication (I4R).
• Stéphane Goutte & Viet Hoang Le & Fei Liu & Hans-Jörg Mettenheim, Von, 2023. "Esg Investing: A Sentiment Analysis Approach," Working Papers halshs-03917335, HAL.
• Stéphane Goutte & Viet Hoang Le & Fei Liu & Hans-Jörg Mettenheim, Von, 2023. "Deep Learning And Technical Analysis In Cryptocurrency Market," Working Papers halshs-03917333, HAL.