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Report NEP-BIG-2022-01-03
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:
- Oecd, 2021, "Mapping data portability initiatives, opportunities and challenges," OECD Digital Economy Papers, OECD Publishing, number 321, Dec, DOI: 10.1787/a6edfab2-en.
- Siyuan Liu & Mehmet Orcun Yalcin & Hsuan Fu & Xiuyi Fan, 2021, "An Investigation of the Impact of COVID-19 Non-Pharmaceutical Interventions and Economic Support Policies on Foreign Exchange Markets with Explainable AI Techniques," Papers, arXiv.org, number 2111.14620, Nov.
- Christophe HURLIN & Christophe PERIGNON & Sébastien SAURIN, 2021, "The Fairness of Credit Scoring Models," LEO Working Papers / DR LEO, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans, number 2912.
- Maximilien Germain & Huyên Pham & Xavier Warin, 2021, "Neural networks-based algorithms for stochastic control and PDEs in finance ," Post-Print, HAL, number hal-03115503, DOI: 10.1017/9781009028943.023.
- Yizhuo Li & Peng Zhou & Fangyi Li & Xiao Yang, 2021, "An Improved Reinforcement Learning Model Based on Sentiment Analysis," Papers, arXiv.org, number 2111.15354, Nov.
- Cabras, Stefano & Sunhe, Flor, 2021, "A Bayesian Spatio-temporal model for predicting passengers' occupancy at Beijing Metro," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de EstadÃstica, number 33787, Dec.
- Blanka Horvath & Josef Teichmann & Zan Zuric, 2021, "Deep Hedging under Rough Volatility," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-88, Feb.
- Brunori, Paolo & Davillas, Apostolos & Jones, Andrew M. & Scarchilli, Giovanna, 2021, "Model-Based Recursive Partitioning to Estimate Unfair Health Inequalities in the United Kingdom Household Longitudinal Study," IZA Discussion Papers, IZA Network @ LISER, number 14925, Dec.
- Thieltges, Andree, 2020, "Machine Learning Anwendungen in der betrieblichen Praxis: Praktische Empfehlungen zur betrieblichen Mitbestimmung," Mitbestimmungspraxis, Hans Böckler Foundation, Institute for Codetermination and Corporate Governance (I.M.U.), number 33.
- David Gierten & Steffen Viete & Raphaela Andres & Thomas Niebel, 2021, "Firms going digital: Tapping into the potential of data for innovation," OECD Digital Economy Papers, OECD Publishing, number 320, Dec, DOI: 10.1787/ee8340c1-en.
- Tomoaki Mikami & Hiroaki Yamagata & Jouchi Nakajima, 2021, "Using Text Analysis to Gauge the Reasons for Respondents' Assessment in the Economy Watchers Survey," Bank of Japan Research Laboratory Series, Bank of Japan, number 21-E-2, Dec.
- Michael Mayer & Steven C. Bourassa & Martin Hoesli & Donato Scognamiglio, 2021, "Structured Additive Regression and Tree Boosting," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-83, Sep.
- Knapp, S. & van de Velden, M., 2021, "Exploration of machine learning algorithms for maritime risk applications," Econometric Institute Research Papers, Erasmus University Rotterdam, Erasmus School of Economics (ESE), Econometric Institute, number 2021-03, Dec.
- Catherine Robinson & Christian Siegel & Sisi Liao, 2021, "Technology Adoption and Skills A Pilot Study of Kent SMEs," Studies in Economics, School of Economics, University of Kent, number 2114, Dec.
- Erik Heilmann, 2021, "The impact of transparency policies on local flexibility markets in electrical distribution networks: A case study with artificial neural network forecasts," MAGKS Papers on Economics, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung), number 202141.