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Report NEP-BIG-2020-03-09
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:
- de Cornière, Alexandre & Taylor, Greg, 2020, "Data and Competition: a General Framework with Applications to Mergers, Market Structure, and Privacy Policy," TSE Working Papers, Toulouse School of Economics (TSE), number 20-1076, Feb.
- Daniel Guterding, 2020, "Inventory effects on the price dynamics of VSTOXX futures quantified via machine learning," Papers, arXiv.org, number 2002.08207, Feb.
- Tesi Aliaj & Aris Anagnostopoulos & Stefano Piersanti, 2020, "Firms Default Prediction with Machine Learning," Papers, arXiv.org, number 2002.11705, Feb.
- Masaya Abe & Kei Nakagawa, 2020, "Cross-sectional Stock Price Prediction using Deep Learning for Actual Investment Management," Papers, arXiv.org, number 2002.06975, Feb.
- Yusuke Uchiyama & Kei Nakagawa, 2020, "TPLVM: Portfolio Construction by Student's $t$-process Latent Variable Model," Papers, arXiv.org, number 2002.06243, Jan.
- Born, Andreas & Janssen, Aljoscha, 2020, "Does a District-Vote Matter for the Behavior of Politicians? A Textual Analysis of Parliamentary Speeches," Working Paper Series, Research Institute of Industrial Economics, number 1320, Feb.
- Oksana Bashchenko & Alexis Marchal, 2020, "Deep Learning for Asset Bubbles Detection," Papers, arXiv.org, number 2002.06405, Feb.
- Greg Lewis & Vasilis Syrgkanis, 2020, "Double/Debiased Machine Learning for Dynamic Treatment Effects via g-Estimation," Papers, arXiv.org, number 2002.07285, Feb, revised Jun 2021.
- Matthew Dixon & Igor Halperin, 2020, "G-Learner and GIRL: Goal Based Wealth Management with Reinforcement Learning," Papers, arXiv.org, number 2002.10990, Feb.
- Evgeny Ponomarev & Ivan Oseledets & Andrzej Cichocki, 2020, "Using Reinforcement Learning in the Algorithmic Trading Problem," Papers, arXiv.org, number 2002.11523, Feb.
- Yusuke Narita & Shota Yasui & Kohei Yata, 2020, "Debiased Off-Policy Evaluation for Recommendation Systems," Papers, arXiv.org, number 2002.08536, Feb, revised Aug 2021.
- Chi Chen & Li Zhao & Wei Cao & Jiang Bian & Chunxiao Xing, 2020, "Trimming the Sail: A Second-order Learning Paradigm for Stock Prediction," Papers, arXiv.org, number 2002.06878, Feb.
- Shaolong Sun & Yanzhao Li & Ju-e Guo & Shouyang Wang, 2020, "Tourism Demand Forecasting: An Ensemble Deep Learning Approach," Papers, arXiv.org, number 2002.07964, Feb, revised Jan 2021.
- Carmine de Franco & Christophe Geissler & Vincent Margot & Bruno Monnier, 2020, "ESG investments: Filtering versus machine learning approaches," Papers, arXiv.org, number 2002.07477, Feb, revised Apr 2020.
- Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2020, "The interconnectedness of the economic content in the speeches of the US Presidents," Papers, arXiv.org, number 2002.07880, Feb.