Printed from https://ideas.repec.org/n/nep-big/2022-06-27.html
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 (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:
- 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, National Institute of Economic and Social Research, number 538, May.
- 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, DIW Berlin, German Institute for Economic Research, number 2000.
- Chenrui Zhang, 2022, "Research on the correlation between text emotion mining and stock market based on deep learning," Papers, arXiv.org, number 2205.06675, May.
- Rafael Reisenhofer & Xandro Bayer & Nikolaus Hautsch, 2022, "HARNet: A Convolutional Neural Network for Realized Volatility Forecasting," Papers, arXiv.org, number 2205.07719, May.
- Marc Schmitt, 2022, "Deep Learning vs. Gradient Boosting: Benchmarking state-of-the-art machine learning algorithms for credit scoring," Papers, arXiv.org, number 2205.10535, May.
- Item repec:dar:wpaper:132193 is not listed on IDEAS anymore
- Dongshuai Zhao & Zhongli Wang & Florian Schweizer-Gamborino & Didier Sornette, 2022, "Polytope Fraud Theory," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 22-41, May.
- Ziheng Chen, 2022, "RLOP: RL Methods in Option Pricing from a Mathematical Perspective," Papers, arXiv.org, number 2205.05600, May.
- Daniel Hopp, 2022, "Benchmarking Econometric and Machine Learning Methodologies in Nowcasting," Papers, arXiv.org, number 2205.03318, May.
- Toon Vanderschueren & Robert Boute & Tim Verdonck & Bart Baesens & Wouter Verbeke, 2022, "Prescriptive maintenance with causal machine learning," Papers, arXiv.org, number 2206.01562, Jun.
- 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, Centre for Economic Performance, LSE, number dp1776, Jun.
- 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, arXiv.org, number 2205.07334, May.
- A. Max Reppen & H. Mete Soner & Valentin Tissot-Daguette, 2022, "Neural Optimal Stopping Boundary," Papers, arXiv.org, number 2205.04595, May, revised May 2023.
- Lundberg, Ian & Brand, Jennie E. & Jeon, Nanum, 2022, "Researcher reasoning meets computational capacity: Machine learning for social science," SocArXiv, Center for Open Science, number s5zc8, May, DOI: 10.31219/osf.io/s5zc8.
- Adilson Sampaio & Paulo Figueiredo & Klarizze Anne Martin Puzon, 2022, "Anticompetitive practices on public procurement: Evidence from Brazilian electronic biddings," WIDER Working Paper Series, World Institute for Development Economic Research (UNU-WIDER), number wp-2022-42.