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 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:

• Peter C.B. Phillips & Zhentao Shi, 2019. "Boosting: Why you Can Use the HP Filter," Cowles Foundation Discussion Papers 2212, Cowles Foundation for Research in Economics, Yale University.
• Omer Berat Sezer & Mehmet Ugur Gudelek & Ahmet Murat Ozbayoglu, 2019. "Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019," Papers 1911.13288, arXiv.org.
• Guzman, Jorge & Li, Aishen, 2019. "Measuring Founding Strategy," SocArXiv 7cvge, Center for Open Science.
• 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 JRC118071, Joint Research Centre.
• Yinheng Li & Junhao Wang & Yijie Cao, 2019. "A General Framework on Enhancing Portfolio Management with Reinforcement Learning," Papers 1911.11880, arXiv.org, revised Oct 2023.
• Christophe Hurlin & Christophe Pérignon, 2019. "Machine Learning et nouvelles sources de données pour le scoring de crédit," Working Papers halshs-02377886, HAL.
• Tao Chen & Michael Ludkovski, 2019. "A Machine Learning Approach to Adaptive Robust Utility Maximization and Hedging," Papers 1912.00244, arXiv.org, 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 1836, DIW Berlin, German Institute for Economic Research.
• Stephan Smeekes & Etienne Wijler, 2019. "High-Dimensional Forecasting in the Presence of Unit Roots and Cointegration," Papers 1911.10552, arXiv.org.
• Shaogao Lv & Yongchao Hou & Hongwei Zhou, 2019. "Financial Market Directional Forecasting With Stacked Denoising Autoencoder," Papers 1912.00712, arXiv.org.
• Schnaubelt, Matthias, 2019. "A comparison of machine learning model validation schemes for non-stationary time series data," FAU Discussion Papers in Economics 11/2019, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
• Jakub Growiec, 2019. "The Hardware–Software Model: A New Conceptual Framework of Production, R&D, and Growth with AI," Working Paper series 19-18, Rimini Centre for Economic Analysis.
• Levi, Eugenio & Patriarca, Fabrizio, 2019. "An exploratory study of populism: the municipality-level predictors of electoral outcomes in Italy," GLO Discussion Paper Series 430, Global Labor Organization (GLO).
• Bernhard Hientzsch, 2019. "Introduction to Solving Quant Finance Problems with Time-Stepped FBSDE and Deep Learning," Papers 1911.12231, arXiv.org.
• Ali Al-Aradi & Adolfo Correia & Danilo de Frietas Naiff & Gabriel Jardim & Yuri Saporito, 2019. "Extensions of the Deep Galerkin Method," Papers 1912.01455, arXiv.org, 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 1911.13300, arXiv.org, revised Mar 2020.
• Vikranth Lokeshwar & Vikram Bhardawaj & Shashi Jain, 2019. "Neural network for pricing and universal static hedging of contingent claims," Papers 1911.11362, arXiv.org.
• 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 halshs-02295392, HAL.
• 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 82bnj, Center for Open Science.
• Ehsan Hoseinzade & Saman Haratizadeh & Arash Khoeini, 2019. "U-CNNpred: A Universal CNN-based Predictor for Stock Markets," Papers 1911.12540, arXiv.org.