Report NEP-BIG-2021-08-16

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:

• Thomas Conlon & John Cotter & Iason Kynigakis, 2021, "Machine Learning and Factor-Based Portfolio Optimization," Papers, arXiv.org, number 2107.13866, Jul.
• Zihao Wang & Kun Li & Steve Q. Xia & Hongfu Liu, 2021, "Economic Recession Prediction Using Deep Neural Network," Papers, arXiv.org, number 2107.10980, Jul.
• Eghbal Rahimikia & Stefan Zohren & Ser-Huang Poon, 2021, "Realised Volatility Forecasting: Machine Learning via Financial Word Embedding," Papers, arXiv.org, number 2108.00480, Aug, revised Apr 2026.
• Yang Bai & Kuntara Pukthuanthong, 2021, "Machine Learning Classification and Portfolio Allocation: with Implications from Machine Uncertainty," Papers, arXiv.org, number 2108.02283, Aug, revised Jul 2025.
• Fabio Zambuto & Simona Arcuti & Roberto Sabatini & Daniele Zambuto, 2021, "Application of classification algorithms for the assessment of confirmation to quality remarks," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 631, Jul.
• Yunze Li & Yanan Xie & Chen Yu & Fangxing Yu & Bo Jiang & Matloob Khushi, 2021, "Feature importance recap and stacking models for forex price prediction," Papers, arXiv.org, number 2107.14092, Jul.
• Marta Bernardini & Paolo Massaro & Francesca Pepe & Francesco Tocco, 2021, "The market notices published by the Italian Stock Exchange: a machine learning approach for the selection of the relevant ones," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 632, Jul.
• Matthieu Garcin & Samuel Stéphan, 2023, "Credit scoring using neural networks and SURE posterior probability calibration," Working Papers, HAL, number hal-03286760, Oct.
• Hanol Lee & Jong-Wha Lee, 2021, "Why East Asian students perform better in mathematics than their peers: An investigation using a machine learning approach," CAMA Working Papers, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University, number 2021-66, Jul.
• Hugo Couture & Dalibor Stevanovic, 2021, "Analyse du marché du travail à l’aide des données de Google Trends," CIRANO Project Reports, CIRANO, number 2021rp-15, Aug.
• Ronald Richman & Mario V. Wuthrich, 2021, "LocalGLMnet: interpretable deep learning for tabular data," Papers, arXiv.org, number 2107.11059, Jul.
• Peter Akioyamen & Yi Zhou Tang & Hussien Hussien, 2021, "A Hybrid Learning Approach to Detecting Regime Switches in Financial Markets," Papers, arXiv.org, number 2108.05801, Aug.
• Zhaolu Dong & Shan Huang & Simiao Ma & Yining Qian, 2021, "Factor Representation and Decision Making in Stock Markets Using Deep Reinforcement Learning," Papers, arXiv.org, number 2108.01758, Aug.
• Nils Köbis & Jean-François Bonnefon & Iyad Rahwan, 2021, "Bad machines corrupt good morals," Post-Print, HAL, number hal-03292360, Jun, DOI: 10.1038/s41562-021-01128-2.
• Luna Yue Huang & Solomon M. Hsiang & Marco Gonzalez-Navarro, 2021, "Using Satellite Imagery and Deep Learning to Evaluate the Impact of Anti-Poverty Programs," NBER Working Papers, National Bureau of Economic Research, Inc, number 29105, Jul.
• Jaime D. Acevedo-Viloria & Luisa Roa & Soji Adeshina & Cesar Charalla Olazo & Andr'es Rodr'iguez-Rey & Jose Alberto Ramos & Alejandro Correa-Bahnsen, 2021, "Relational Graph Neural Networks for Fraud Detection in a Super-App environment," Papers, arXiv.org, number 2107.13673, Jul, revised Jul 2021.
• Mohammad Rafiqul Islam & Masud Alam & Munshi Naser .Ibne Afzal & Sakila Alam, 2021, "Nighttime Light Intensity and Child Health Outcomes in Bangladesh," Papers, arXiv.org, number 2108.00926, Aug, revised Sep 2022.
• Susan von Struensee, 2021, "The Role of Social Movements, Coalitions, and Workers in Resisting Harmful Artificial Intelligence and Contributing to the Development of Responsible AI," Papers, arXiv.org, number 2107.14052, Jul.
• Qinkai Chen & Christian-Yann Robert, 2021, "Graph-Based Learning for Stock Movement Prediction with Textual and Relational Data," Papers, arXiv.org, number 2107.10941, Jul, revised Dec 2021.
• Ruf, Johannes & Wang, Weiguan, 2022, "Hedging with linear regressions and neural networks," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 107811, Oct.
• Parle, Conor, 2021, "The financial market impact of ECB monetary policy press conferences - a text based approach," Research Technical Papers, Central Bank of Ireland, number 4/RT/21, May.
• Francesca Biagini & Lukas Gonon & Thomas Reitsam, 2021, "Neural network approximation for superhedging prices," Papers, arXiv.org, number 2107.14113, Jul.
• Alexandre Carbonneau & Fr'ed'eric Godin, 2021, "Deep equal risk pricing of financial derivatives with non-translation invariant risk measures," Papers, arXiv.org, number 2107.11340, Jul.
• Genz, Sabrina & Gregory, Terry & Janser, Markus & Lehmer, Florian & Matthes, Britta, 2021, "How Do Workers Adjust When Firms Adopt New Technologies?," IZA Discussion Papers, IZA Network @ LISER, number 14626, Aug.
• Chaoran Cui & Xiaojie Li & Juan Du & Chunyun Zhang & Xiushan Nie & Meng Wang & Yilong Yin, 2021, "Temporal-Relational Hypergraph Tri-Attention Networks for Stock Trend Prediction," Papers, arXiv.org, number 2107.14033, Jul, revised Mar 2022.
• David Friederich & Lynn H. Kaack & Alexandra Luccioni & Bjarne Steffen, 2021, "Automated Identification of Climate Risk Disclosures in Annual Corporate Reports," Papers, arXiv.org, number 2108.01415, Aug.
• Filip Premik, 2021, "Estimating the effects of universal transfers: new ML approach and application to labor supply reaction to child benefits," GRAPE Working Papers, GRAPE Group for Research in Applied Economics, number 54.
• Zhijun Chen & Chongwoo Choe & Jiajia Cong & Noriaki Matsushima, 2020, "Data-driven mergers and personalization," ISER Discussion Paper, Institute of Social and Economic Research, The University of Osaka, number 1108r, Nov, revised Aug 2021.
• Bachir El Murr & Genane Youness & Hala Gharib & Mayssaa Daher, 2021, "Financial literacy and individual success: Lebanese framework modeling," Post-Print, HAL, number hal-03295623, Jun.
• Serzo, Aiken Larisa O., 2020, "Cross-border Data Regulation for Digital Platforms: Data Privacy and Security," Discussion Papers, Philippine Institute for Development Studies, number DP 2020-47, DOI: https://doi.org/10.62986/dp2020.47.

Report NEP-BIG-2021-08-16

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: