Report NEP-BIG-2020-11-16

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:

• Katsafados, Apostolos G. & Androutsopoulos, Ion & Chalkidis, Ilias & Fergadiotis, Manos & Leledakis, George N. & Pyrgiotakis, Emmanouil G., 2020, "Textual Information and IPO Underpricing: A Machine Learning Approach," MPRA Paper, University Library of Munich, Germany, number 103813, Oct.
• Michael Allan Ribers & Hannes Ullrich, 2020, "Machine Predictions and Human Decisions with Variation in Payoffs and Skills," Discussion Papers of DIW Berlin, DIW Berlin, German Institute for Economic Research, number 1911.
• Max H. Farrell & Tengyuan Liang & Sanjog Misra, 2020, "Deep Learning for Individual Heterogeneity," Papers, arXiv.org, number 2010.14694, Oct, revised Apr 2025.
• Amir Mukeri & Habibullah Shaikh & D. P. Gaikwad, 2020, "Financial Data Analysis Using Expert Bayesian Framework For Bankruptcy Prediction," Papers, arXiv.org, number 2010.13892, Oct, revised Oct 2020.
• Sidra Mehtab & Jaydip Sen, 2020, "Stock Price Prediction Using CNN and LSTM-Based Deep Learning Models," Papers, arXiv.org, number 2010.13891, Oct.
• Septimiu Szabo, 2020, "Transition to Industry 4.0 in the Visegrád Countries," European Economy - Economic Briefs, Directorate General Economic and Financial Affairs (DG ECFIN), European Commission, number 052, Jun.
• Andrés Alonso & José Manuel Carbó, 2020, "Machine learning in credit risk: measuring the dilemma between prediction and supervisory cost," Working Papers, Banco de España, number 2032, Oct.
• Stefan Penczynski & Konrad Maliszewski & Andrew Fearne, 2020, "The impact of data visualisation on the use of shopper insight in the marketing decisionmaking of small food producers," Working Paper series, University of East Anglia, Centre for Behavioural and Experimental Social Science (CBESS), School of Economics, University of East Anglia, Norwich, UK., number 20-05, Nov.
• Christiansen, T. & Weeks, M., 2020, "Distributional Aspects of Microcredit Expansions," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 20100, Nov.
• Elizabeth Fons & Paula Dawson & Xiao-jun Zeng & John Keane & Alexandros Iosifidis, 2020, "Evaluating data augmentation for financial time series classification," Papers, arXiv.org, number 2010.15111, Oct.
• Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020, "News Media vs. FRED-MD for Macroeconomic Forecasting," CESifo Working Paper Series, CESifo, number 8639.
• Richard Bluhm & Melanie Krause, 2020, "Top Lights: Bright cities and their contribution to economic development," SoDa Laboratories Working Paper Series, Monash University, SoDa Laboratories, number 2020-08, Nov.
• Sanghamitra Mukherjee, 2020, "Boosting Renewable Energy Technology Uptake in Ireland: A Machine Learning Approach," Working Papers, School of Economics, University College Dublin, number 202027, Sep.
• Waterschoot, Cedric, 2020, "The future of theory: should social protection board the big data train?," SocArXiv, Center for Open Science, number hmuva, Oct, DOI: 10.31219/osf.io/hmuva.
• Andrew B. Martinez, 2020, "Extracting Information from Different Expectations," Working Papers, The George Washington University, The Center for Economic Research, number 2020-008, Oct.
• Julia Cage & Nicolas Herve & Beatrice Mazoyer, 2020, "Social Media and Newsroom Production Decisions," Working Papers, NET Institute, number 20-14, Oct.
• Alvaro Gomez Losada & Montserrat Lopez-Cobo & Sofia Samoili & Georgios Alaveras & Miguel Vazquez-Prada Baillet & Melisande Cardona & Riccardo Righi & Lukasz Ziemba & Giuditta De-Prato, 2020, "Estimation of supply and demand of tertiary education places in advanced digital profiles in the EU: Focus on Artificial Intelligence, High Performance Computing, Cybersecurity and Data Science," JRC Research Reports, Joint Research Centre, number JRC121683, Sep.
• Perone, G., 2020, "Comparison of ARIMA, ETS, NNAR and hybrid models to forecast the second wave of COVID-19 hospitalizations in Italy," Health, Econometrics and Data Group (HEDG) Working Papers, HEDG, c/o Department of Economics, University of York, number 20/18, Nov.
• Item repec:iim:iimawp:14638 is not listed on IDEAS anymore

• Tae-Hwy Lee & Ekaterina Seregina, 2020, "Learning from Forecast Errors: A New Approach to Forecast Combination," Working Papers, University of California at Riverside, Department of Economics, number 202024, Sep.
• Nataliia Ostapenko, 2020, "Macroeconomic expectations: news sentiment analysis," Bank of Estonia Working Papers, Bank of Estonia, number wp2020-5, Aug, revised 13 Aug 2020, DOI: 10.23656/25045520/052020/0178.
• Stefan Kremsner & Alexander Steinicke & Michaela Szolgyenyi, 2020, "A deep neural network algorithm for semilinear elliptic PDEs with applications in insurance mathematics," Papers, arXiv.org, number 2010.15757, Oct, revised Dec 2020.
• Michael Ryan, 2020, "A Narrative Approach to Creating Instruments with Unstructured and Voluminous Text: An Application to Policy Uncertainty," Working Papers in Economics, University of Waikato, number 20/10, Nov.
• Faizaan Pervaiz & Christopher Goh & Ashley Pennington & Samuel Holt & James West & Shaun Ng, 2020, "Fear and Volatility in Digital Assets," Papers, arXiv.org, number 2010.15611, Oct.
• Xianchao Wu, 2020, "Event-Driven Learning of Systematic Behaviours in Stock Markets," Papers, arXiv.org, number 2010.15586, Oct.
• Antti J. Tanskanen, 2020, "Deep reinforced learning enables solving rich discrete-choice life cycle models to analyze social security reforms," Papers, arXiv.org, number 2010.13471, Oct, revised Feb 2022.
• Obradovich, Nick & Özak, Ömer & Martín, Ignacio & Ortuño-Ortín, Ignacio & Awad, Edmond & Cebrián, Manuel & Cuevas, Rubén & Desmet, Klaus & Rahwan, Iyad & Cuevas, Ángel, 2020, "Expanding the Measurement of Culture with a Sample of Two Billion Humans," GLO Discussion Paper Series, Global Labor Organization (GLO), number 696.
• Carmine De Franco & Johann Nicolle & Huy^en Pham, 2020, "Discrete-time portfolio optimization under maximum drawdown constraint with partial information and deep learning resolution," Papers, arXiv.org, number 2010.15779, Oct, revised Oct 2020.
• Alvarez, Santiago E. & Lein, Sarah M., 2020, "Tracking Inflation on a Daily Basis," Working papers, Faculty of Business and Economics - University of Basel, number 2020/16.
• Zhou, Alvin, 2020, "Communicating corporate LGBTQ advocacy: A computational comparison of the global CSR discourse," OSF Preprints, Center for Open Science, number gz7bw, Oct, DOI: 10.31219/osf.io/gz7bw.

Report NEP-BIG-2020-11-16

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: