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Report NEP-CMP-2024-05-06
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:
- Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023, "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print, HAL, number hal-04223161, DOI: 10.1007/s11156-023-01192-x.
- Javier Mancilla & Andr'e Sequeira & Tomas Tagliani & Francisco Llaneza & Claudio Beiza, 2024, "Empowering Credit Scoring Systems with Quantum-Enhanced Machine Learning," Papers, arXiv.org, number 2404.00015, Mar, revised Apr 2024.
- Jai Pal, 2024, "Long Short-Term Memory Pattern Recognition in Currency Trading," Papers, arXiv.org, number 2403.18839, Feb.
- Eva Lutkebohmert & Julian Sester, 2024, "Measuring Name Concentrations through Deep Learning," Papers, arXiv.org, number 2403.16525, Mar, revised Nov 2024.
- Denis Levchenko & Efstratios Rappos & Shabnam Ataee & Biagio Nigro & Stephan Robert-Nicoud, 2024, "Chain-structured neural architecture search for financial time series forecasting," Papers, arXiv.org, number 2403.14695, Mar, revised Dec 2024.
- Silvia Garc'ia-M'endez & Francisco de Arriba-P'erez & Ana Barros-Vila & Francisco J. Gonz'alez-Casta~no, 2024, "Detection of Temporality at Discourse Level on Financial News by Combining Natural Language Processing and Machine Learning," Papers, arXiv.org, number 2404.01337, Mar.
- Enmin Zhu & Jerome Yen, 2024, "BERTopic-Driven Stock Market Predictions: Unraveling Sentiment Insights," Papers, arXiv.org, number 2404.02053, Apr, revised Apr 2024.
- Gyungbae Park, 2024, "Debiased Machine Learning when Nuisance Parameters Appear in Indicator Functions," Papers, arXiv.org, number 2403.15934, Mar, revised Mar 2025.
- Masanori Hirano, 2024, "Construction of a Japanese Financial Benchmark for Large Language Models," Papers, arXiv.org, number 2403.15062, Mar.
- Michele Costola & Bertrand Maillet & Zhining Yuan & Xiang Zhang, 2024, "Mean-Variance Efficient Large Portfolios : A Simple Machine Learning Heuristic Technique based on the Two-Fund Separation Theorem," Post-Print, HAL, number hal-04514343, Mar.
- Bartosz Bieganowski & Robert Slepaczuk, 2024, "Supervised Autoencoder MLP for Financial Time Series Forecasting," Papers, arXiv.org, number 2404.01866, Apr, revised Jun 2024.
- Nolan Alexander & William Scherer, 2024, "Using Machine Learning to Forecast Market Direction with Efficient Frontier Coefficients," Papers, arXiv.org, number 2404.00825, Mar.
- Lei Bill Wang & Zhenbang Jiao & Om Prakash Bedant & Haoran Wang, 2024, "Balancing Efficiency and Equity in Classroom Assignment under Endogenous Peer Effects," Papers, arXiv.org, number 2404.02497, Apr, revised Jun 2025.
- Stijn De Backer & Luis E. C. Rocha & Jan Ryckebusch & Koen Schoors, 2024, "On the potential of quantum walks for modeling financial return distributions," Papers, arXiv.org, number 2403.19502, Mar, revised Dec 2024.
- Francesco Catalano & Laura Nasello & Daniel Guterding, 2024, "Quantum computing approach to realistic ESG-friendly stock portfolios," Papers, arXiv.org, number 2404.02582, Apr.