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Report NEP-BIG-2022-06-20
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:
- Chenrui Zhang, 2022, "Deep learning based Chinese text sentiment mining and stock market correlation research," Papers, arXiv.org, number 2205.04743, May.
- Sonan Memon, 2021, "Machine Learning for Economists: An Introduction," PIDE Knowledge Brief, Pakistan Institute of Development Economics, number 2021:33.
- Margherita Doria & Elisa Luciano & Patrizia Semeraro, 2022, "Machine learning techniques in joint default assessment," Papers, arXiv.org, number 2205.01524, May, revised Sep 2023.
- MARTINEZ PLUMED Fernando & CABALLERO BENÍTEZ Fernando & CASTELLANO FALCÓN David & FERNANDEZ LLORCA David & GOMEZ Emilia & HUPONT TORRES Isabelle & MERINO Luis & MONSERRAT Carlos & HERNÁNDEZ ORALLO Jos, 2022, "AI Watch: Revisiting Technology Readiness Levels for relevant Artificial Intelligence technologies," JRC Research Reports, Joint Research Centre, number JRC129399, May.
- Andrew Caplin & Daniel Martin & Philip Marx, 2022, "Calibrating for Class Weights by Modeling Machine Learning," Papers, arXiv.org, number 2205.04613, May, revised Jul 2022.
- Yong Xie & Dakuo Wang & Pin-Yu Chen & Jinjun Xiong & Sijia Liu & Sanmi Koyejo, 2022, "A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Predictions," Papers, arXiv.org, number 2205.01094, May, revised Jul 2022.
- Ryan Defina, 2021, "Machine Learning Methods: Potential for Deposit Insurance," IADI Fintech Briefs, International Association of Deposit Insurers, number 3, Sep.
- Reisenhofer, Rafael & Bayer, Xandro & Hautsch, Nikolaus, 2022, "HARNet: A convolutional neural network for realized volatility forecasting," CFS Working Paper Series, Center for Financial Studies (CFS), number 680.
- Sun, Maoran & Han, Changyu & Nie, Quan & Xu, Jingying & Zhang, Fan & Zhao, Qunshan, 2022, "Understanding Building Energy Efficiency with Administrative and Emerging Urban Big Data by Deep Learning in Glasgow," OSF Preprints, Center for Open Science, number g8p4f, May, DOI: 10.31219/osf.io/g8p4f.
- Li, Yanmin & Zhong, Ziqi & Zhang, Fengrui & Zhao, Xinjie, 2022, "Artificial intelligence-based human–computer interaction technology applied in consumer behavior analysis and experiential education," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 115047, Apr.
- Lyonnet, Victor & Stern, Lea H., 2022, "Venture Capital (Mis)allocation in the Age of AI," Working Paper Series, Ohio State University, Charles A. Dice Center for Research in Financial Economics, number 2022-02, Feb.
- Massaro, Alessandro & Giardinelli, Vito O. M. & Cosoli, Gabriele & Magaletti, Nicola & Leogrande, Angelo, 2022, "The Prediction of Hypertension Risk," MPRA Paper, University Library of Munich, Germany, number 113242, May.
- Bratanova, Alexandra & Pham, Hien & Mason, Claire & Hajkowicz, Stefan & Naughtin, Claire & Schleiger, Emma & Sanderson, Conrad & Chen, Caron & Karimi, Sarvnaz, 2022, "Differentiating artificial intelligence capability clusters in Australia," MPRA Paper, University Library of Munich, Germany, number 113237, May.
- Leogrande, Angelo & Costantiello, Alberto & Laureti, Lucio & Matarrese, Marco Maria, 2022, "Innovative SMEs Collaborating with Others in Europe," MPRA Paper, University Library of Munich, Germany, number 113008, May.
- Martin Beraja & Andrew Kao & David Y. Yang & Noam Yuchtman, 2021, "AI-tocracy," CEP Discussion Papers, Centre for Economic Performance, LSE, number dp1811, Nov.
- Will, Paris & Krpan, Dario & Lordan, Grace, 2023, "People versus machines: introducing the HIRE framework," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 115006, Feb.
- Josten, Cecily & Lordan, Grace, 2022, "Automation and the changing nature of work," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 115117, May.
- van Loon, Austin, 2022, "Three Families of Automated Text Analysis," SocArXiv, Center for Open Science, number htnej, May, DOI: 10.31219/osf.io/htnej.
- Nomaler, Önder & Verspagen, Bart, 2022, "The canonical correlation complexity method," MERIT Working Papers, United Nations University - Maastricht Economic and Social Research Institute on Innovation and Technology (MERIT), number 2022-015, Apr.
- Chenrui Zhang & Xinyi Wu & Hailu Deng & Huiwei Zhang, 2022, "A time-varying study of Chinese investor sentiment, stock market liquidity and volatility: Based on deep learning BERT model and TVP-VAR model," Papers, arXiv.org, number 2205.05719, May, revised May 2022.
- Tatjana Evas & Maikki Sipinen & Martin Ulbrich & Alessandro Dalla Benetta & Maciej Sobolewski & Daniel Nepelski, 2022, "AI Watch: Estimating AI investments in the European Union," JRC Research Reports, Joint Research Centre, number JRC129174, May.
- Anton Korinek & Avital Balwit, 2022, "Aligned with Whom? Direct and Social Goals for AI Systems," NBER Working Papers, National Bureau of Economic Research, Inc, number 30017, May.
- Margaryta Klymak & Stuart Baumann, 2022, "Paying over the odds at the end of the fiscal year. Evidence from Ukraine," Economics Series Working Papers, University of Oxford, Department of Economics, number 968, Apr.
- Emilio Soria-Olivas & Jos'e E. Vila Gisbert & Regino Barranquero Carde~nosa & Yolanda Gomez, 2022, "Integration of Behavioral Economic Models to Optimize ML performance and interpretability: a sandbox example," Papers, arXiv.org, number 2205.01387, May.
- Helena Chuliá & Ignacio Garrón & Jorge M. Uribe, 2022, ""Daily Growth at Risk: financial or real drivers? The answer is not always the same"," IREA Working Papers, University of Barcelona, Research Institute of Applied Economics, number 202208, Jun, revised Jun 2022.