Report NEP-BIG-2022-06-27
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.Subscribe to this report: email, RSS, or Mastodon.
Other reports in NEP-BIG
The following items were announced in this report:
- Bhattacharjee, Arnab & Kohns, David, 2022. "Nowcasting Growth using Google Trends Data: A Bayesian Structural Time Series Model," National Institute of Economic and Social Research (NIESR) Discussion Papers 538, National Institute of Economic and Social Research.
- Maximilian Andres & Lisa Bruttel & Jana Friedrichsen, 2022. "How Communication Makes the Difference between a Cartel and Tacit Collusion: A Machine Learning Approach," Discussion Papers of DIW Berlin 2000, DIW Berlin, German Institute for Economic Research.
- Chenrui Zhang, 2022. "Research on the correlation between text emotion mining and stock market based on deep learning," Papers 2205.06675, arXiv.org.
- Rafael Reisenhofer & Xandro Bayer & Nikolaus Hautsch, 2022. "HARNet: A Convolutional Neural Network for Realized Volatility Forecasting," Papers 2205.07719, arXiv.org.
- Marc Schmitt, 2022. "Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring," Papers 2205.10535, arXiv.org.
- Vetter, Oliver A. & Hoffmann, Felix Sebastian & Pumplun, Luisa & Buxmann, Peter, 2022. "What constitutes a machine-learning-driven business model? A taxonomy of B2B start-ups with machine learning at their core," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 132193, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
- Dongshuai Zhao & Zhongli Wang & Florian Schweizer-Gamborino & Didier Sornette, 2022. "Polytope Fraud Theory," Swiss Finance Institute Research Paper Series 22-41, Swiss Finance Institute.
- Ziheng Chen, 2022. "RLOP: RL Methods in Option Pricing from a Mathematical Perspective," Papers 2205.05600, arXiv.org.
- Daniel Hopp, 2022. "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers 2205.03318, arXiv.org.
- Toon Vanderschueren & Robert Boute & Tim Verdonck & Bart Baesens & Wouter Verbeke, 2022. "Prescriptive maintenance with causal machine learning," Papers 2206.01562, arXiv.org.
- Holger Breinlich & Valentina Corradi & Nadia Rocha & Michele Ruta & J.M.C. Santos Silva & Tom Zylkin, 2021. "Machine learning in international trade research - evaluating the impact of trade agreements," CEP Discussion Papers dp1776, Centre for Economic Performance, LSE.
- Eduardo Ramos-P'erez & Pablo J. Alonso-Gonz'alez & Jos'e Javier N'u~nez-Vel'azquez, 2022. "Mack-Net model: Blending Mack's model with Recurrent Neural Networks," Papers 2205.07334, arXiv.org.
- A. Max Reppen & H. Mete Soner & Valentin Tissot-Daguette, 2022. "Neural Optimal Stopping Boundary," Papers 2205.04595, arXiv.org, revised May 2023.
- Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022. "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv s5zc8, Center for Open Science.
- Adilson Sampaio & Paulo Figueiredo & Klarizze Anne Martin Puzon, 2022. "Anticompetitive practices on public procurement: Evidence from Brazilian electronic biddings," WIDER Working Paper Series wp-2022-42, World Institute for Development Economic Research (UNU-WIDER).