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Report NEP-BIG-2024-06-24
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:
- Jingyang Wu & Xinyi Zhang & Fangyixuan Huang & Haochen Zhou & Rohtiash Chandra, 2024, "Review of deep learning models for crypto price prediction: implementation and evaluation," Papers, arXiv.org, number 2405.11431, May, revised Jun 2024.
- Wolff, Dominik & Echterling, Fabian, 2024, "Stock picking with machine learning," Publications of Darmstadt Technical University, Institute for Business Studies (BWL), Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL), number 145491, May, DOI: 10.1002/for.3021.
- Tänzer, Alina, 2024, "Multivariate macroeconomic forecasting: From DSGE and BVAR to artificial neural networks," IMFS Working Paper Series, Goethe University Frankfurt, Institute for Monetary and Financial Stability (IMFS), number 205.
- Michael Lechner & Jana Mareckova, 2024, "Comprehensive Causal Machine Learning," Papers, arXiv.org, number 2405.10198, May, revised Feb 2025.
- Yuji Sakurai & Zhuohui Chen, 2024, "Forecasting Tail Risk via Neural Networks with Asymptotic Expansions," IMF Working Papers, International Monetary Fund, number 2024/099, May.
- Huiyu Li & Junhua Hu, 2024, "A Hybrid Deep Learning Framework for Stock Price Prediction Considering the Investor Sentiment of Online Forum Enhanced by Popularity," Papers, arXiv.org, number 2405.10584, May.
- Despotovic, Miroslav & Glatschke, Matthias, 2024, "Challenges and Opportunities of Artificial Intelligence and Machine Learning in Circular Economy," SocArXiv, Center for Open Science, number 6qmhf, May, DOI: 10.31219/osf.io/6qmhf.
- Raeid Saqur & Ken Kato & Nicholas Vinden & Frank Rudzicz, 2024, "NIFTY Financial News Headlines Dataset," Papers, arXiv.org, number 2405.09747, May.
- Jiahao Weng & Yan Xie, 2024, "Degree of Irrationality: Sentiment and Implied Volatility Surface," Papers, arXiv.org, number 2405.11730, May.
- Daniel Aromí & Daniel Heymann, 2024, "Talk to Fed: a Big Dive into FOMC Transcripts," Working Papers, Red Nacional de Investigadores en Economía (RedNIE), number 323, May.
- Buczak, Philip, 2024, "fabOF: A Novel Tree Ensemble Method for Ordinal Prediction," OSF Preprints, Center for Open Science, number h8t4p, May, DOI: 10.31219/osf.io/h8t4p.
- Kasy, Maximilian, 2024, "The Political Economy of AI: Towards Democratic Control of the Means of Prediction," IZA Discussion Papers, IZA Network @ LISER, number 16948, Apr.
- Krist'of N'emeth & D'aniel Hadh'azi, 2024, "Generating density nowcasts for U.S. GDP growth with deep learning: Bayes by Backprop and Monte Carlo dropout," Papers, arXiv.org, number 2405.15579, May.
- Colin D. Grab, 2024, "Exploiting Distributional Value Functions for Financial Market Valuation, Enhanced Feature Creation and Improvement of Trading Algorithms," Papers, arXiv.org, number 2405.11686, May.
- Theodoros Zafeiriou & Dimitris Kalles, 2024, "Off-the-Shelf Neural Network Architectures for Forex Time Series Prediction come at a Cost," Papers, arXiv.org, number 2405.10679, May.
- Kentaro Hoffman & Stephen Salerno & Jeff Leek & Tyler McCormick, 2024, "Some models are useful, but for how long?: A decision theoretic approach to choosing when to refit large-scale prediction models," Papers, arXiv.org, number 2405.13926, May, revised Jan 2025.
- Yu Cheng & Qin Yang & Liyang Wang & Ao Xiang & Jingyu Zhang, 2024, "Research on Credit Risk Early Warning Model of Commercial Banks Based on Neural Network Algorithm," Papers, arXiv.org, number 2405.10762, May, revised May 2024.
- Jordan G. Taqi-Eddin, 2024, "Impact Analysis of the Chesa Boudin Administration," Papers, arXiv.org, number 2405.11455, May.
- Lisa Bruttel & Maximilian Andres, 2024, "Communicating Cartel Intentions," CEPA Discussion Papers, Center for Economic Policy Analysis, number 77, May, DOI: 10.25932/publishup-63846.
- Tom Suhr & Samira Samadi & Chiara Farronato, 2024, "A Dynamic Model of Performative Human-ML Collaboration: Theory and Empirical Evidence," Papers, arXiv.org, number 2405.13753, May, revised Oct 2024.
- Pranjal Rawat, 2024, "A Deep Learning Approach to Heterogeneous Consumer Aesthetics in Fast Fashion," Papers, arXiv.org, number 2405.10498, May, revised Apr 2026.
- Yu Xia & Sriram Narayanamoorthy & Zhengyuan Zhou & Joshua Mabry, 2024, "Simulation-Based Benchmarking of Reinforcement Learning Agents for Personalized Retail Promotions," Papers, arXiv.org, number 2405.10469, May.
- Gang Hu & Ming Gu, 2024, "Markowitz Meets Bellman: Knowledge-distilled Reinforcement Learning for Portfolio Management," Papers, arXiv.org, number 2405.05449, May.
- Hongyang Yang & Boyu Zhang & Neng Wang & Cheng Guo & Xiaoli Zhang & Likun Lin & Junlin Wang & Tianyu Zhou & Mao Guan & Runjia Zhang & Christina Dan Wang, 2024, "FinRobot: An Open-Source AI Agent Platform for Financial Applications using Large Language Models," Papers, arXiv.org, number 2405.14767, May, revised May 2024.
- Gorny, Paul M. & Groos, Eva & Strobel, Christina, 2024, "Do Personalized AI Predictions Change Subsequent Decision-Outcomes? The Impact of Human Oversight," MPRA Paper, University Library of Munich, Germany, number 121065, May.
- David Ardia & Keven Bluteau, 2024, "Optimal Text-Based Time-Series Indices," Papers, arXiv.org, number 2405.10449, May.
- Yunfei Peng & Pengyu Wei & Wei Wei, 2024, "Deep Penalty Methods: A Class of Deep Learning Algorithms for Solving High Dimensional Optimal Stopping Problems," Papers, arXiv.org, number 2405.11392, May, revised Apr 2026.
- Maximilian Nagele & Jan Olle & Thomas Fosel & Remmy Zen & Florian Marquardt, 2024, "Tackling Decision Processes with Non-Cumulative Objectives using Reinforcement Learning," Papers, arXiv.org, number 2405.13609, May, revised May 2025.