Printed from https://ideas.repec.org/n/nep-cmp/2024-06-24.html
Report NEP-CMP-2024-06-24
This is the archive for NEP-CMP, a report on new working papers in the area of Computational Economics. Stanley Miles issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
Other reports in NEP-CMP
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.
- 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.
- 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.
- Yuji Sakurai & Zhuohui Chen, 2024, "Forecasting Tail Risk via Neural Networks with Asymptotic Expansions," IMF Working Papers, International Monetary Fund, number 2024/099, May.
- 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.
- 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.
- 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.
- 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.
- Michael Lechner & Jana Mareckova, 2024, "Comprehensive Causal Machine Learning," Papers, arXiv.org, number 2405.10198, May, revised Feb 2025.
- Gang Hu & Ming Gu, 2024, "Markowitz Meets Bellman: Knowledge-distilled Reinforcement Learning for Portfolio Management," Papers, arXiv.org, number 2405.05449, 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.
- 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.
- Raeid Saqur & Ken Kato & Nicholas Vinden & Frank Rudzicz, 2024, "NIFTY Financial News Headlines Dataset," Papers, arXiv.org, number 2405.09747, May.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.