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.

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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.
• 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 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).