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Report NEP-BIG-2019-12-16
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:
- Peter C.B. Phillips & Zhentao Shi, 2019, "Boosting: Why you Can Use the HP Filter," Cowles Foundation Discussion Papers, Cowles Foundation for Research in Economics, Yale University, number 2212, Dec.
- Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019, "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers, arXiv.org, number 1911.13288, Nov.
- Guzman, Jorge & Li, Aishen, 2019, "Measuring Founding Strategy," SocArXiv, Center for Open Science, number 7cvge, Nov, DOI: 10.31219/osf.io/7cvge.
- Giuditta De Prato & Montserrat Lopez Cobo & Sofia Samoili & Riccardo Righi & Miguel Vazquez Prada Baillet & Melisande Cardona, 2019, "The AI Techno-Economic Segment Analysis," JRC Research Reports, Joint Research Centre, number JRC118071, Nov.
- Yinheng Li & Junhao Wang & Yijie Cao, 2019, "A General Framework on Enhancing Portfolio Management with Reinforcement Learning," Papers, arXiv.org, number 1911.11880, Nov, revised Oct 2023.
- Christophe Hurlin & Christophe Pérignon, 2019, "Machine Learning et nouvelles sources de données pour le scoring de crédit," Working Papers, HAL, number halshs-02377886, Nov.
- Tao Chen & Michael Ludkovski, 2019, "A Machine Learning Approach to Adaptive Robust Utility Maximization and Hedging," Papers, arXiv.org, number 1912.00244, Nov, revised May 2020.
- Elena Argentesi & Paolo Buccirossi & Emilio Calvano & Tomaso Duso & Alessia Marrazzo & Salvatore Nava, 2019, "Merger Policy in Digital Markets: An Ex-Post Assessment," Discussion Papers of DIW Berlin, DIW Berlin, German Institute for Economic Research, number 1836.
- Stephan Smeekes & Etienne Wijler, 2019, "High-Dimensional Forecasting in the Presence of Unit Roots and Cointegration," Papers, arXiv.org, number 1911.10552, Nov.
- Shaogao Lv & Yongchao Hou & Hongwei Zhou, 2019, "Financial Market Directional Forecasting With Stacked Denoising Autoencoder," Papers, arXiv.org, number 1912.00712, Dec.
- Schnaubelt, Matthias, 2019, "A comparison of machine learning model validation schemes for non-stationary time series data," FAU Discussion Papers in Economics, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics, number 11/2019.
- Item repec:rim:rimwps:19-18 is not listed on IDEAS anymore
- Levi, Eugenio & Patriarca, Fabrizio, 2019, "An exploratory study of populism: the municipality-level predictors of electoral outcomes in Italy," GLO Discussion Paper Series, Global Labor Organization (GLO), number 430.
- Bernhard Hientzsch, 2019, "Introduction to Solving Quant Finance Problems with Time-Stepped FBSDE and Deep Learning," Papers, arXiv.org, number 1911.12231, Nov.
- Ali Al-Aradi & Adolfo Correia & Danilo de Frietas Naiff & Gabriel Jardim & Yuri Saporito, 2019, "Extensions of the Deep Galerkin Method," Papers, arXiv.org, number 1912.01455, Nov, revised Apr 2022.
- Indranil SenGupta & William Nganje & Erik Hanson, 2019, "Refinements of Barndorff-Nielsen and Shephard model: an analysis of crude oil price with machine learning," Papers, arXiv.org, number 1911.13300, Nov, revised Mar 2020.
- Vikranth Lokeshwar & Vikram Bhardawaj & Shashi Jain, 2019, "Neural network for pricing and universal static hedging of contingent claims," Papers, arXiv.org, number 1911.11362, Nov.
- Bénédicte Apouey, 2019, "Intérêt des adhérents d'une mutuelle pour des services utilisant leurs données personnelles dans le cadre de la médecine personnalisée," Working Papers, HAL, number halshs-02295392, Sep.
- Dramsch, Jesper Sören & Christensen, Anders Nymark & MacBeth, Colin & Lüthje, Mikael, 2019, "Deep Unsupervised 4D Seismic 3D Time-Shift Estimation with Convolutional Neural Networks," Earth Arxiv, Center for Open Science, number 82bnj, Oct, DOI: 10.31219/osf.io/82bnj.
- Ehsan Hoseinzade & Saman Haratizadeh & Arash Khoeini, 2019, "U-CNNpred: A Universal CNN-based Predictor for Stock Markets," Papers, arXiv.org, number 1911.12540, Nov.