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Report NEP-CMP-2023-10-23
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:
- Marra de Artiñano, Ignacio & Riottini Depetris, Franco & Volpe Martincus, Christian, 2023, "Automatic Product Classification in International Trade: Machine Learning and Large Language Models," IDB Publications (Working Papers), Inter-American Development Bank, number 12962, Jul, DOI: http://dx.doi.org/10.18235/0005012.
- Mark Potanin & Andrey Chertok & Konstantin Zorin & Cyril Shtabtsovsky, 2023, "Startup success prediction and VC portfolio simulation using CrunchBase data," Papers, arXiv.org, number 2309.15552, Sep.
- Dangxing Chen, 2023, "Can I Trust the Explanations? Investigating Explainable Machine Learning Methods for Monotonic Models," Papers, arXiv.org, number 2309.13246, Sep.
- Albert Wong & Steven Whang & Emilio Sagre & Niha Sachin & Gustavo Dutra & Yew-Wei Lim & Gaetan Hains & Youry Khmelevsky & Frank Zhang, 2023, "Short-Term Stock Price Forecasting using exogenous variables and Machine Learning Algorithms," Papers, arXiv.org, number 2309.00618, May.
- Shuyang Wang & Diego Klabjan, 2023, "An Ensemble Method of Deep Reinforcement Learning for Automated Cryptocurrency Trading," Papers, arXiv.org, number 2309.00626, Jul.
- Sergio Caprioli & Emanuele Cagliero & Riccardo Crupi, 2023, "Quantifying Credit Portfolio sensitivity to asset correlations with interpretable generative neural networks," Papers, arXiv.org, number 2309.08652, Sep, revised Nov 2023.
- Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023, "Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-23.
- Paul Bilokon & Yitao Qiu, 2023, "Transformers versus LSTMs for electronic trading," Papers, arXiv.org, number 2309.11400, Sep.
- Wei Jie Yeo & Wihan van der Heever & Rui Mao & Erik Cambria & Ranjan Satapathy & Gianmarco Mengaldo, 2023, "A Comprehensive Review on Financial Explainable AI," Papers, arXiv.org, number 2309.11960, Sep.
- Jakub Micha'nk'ow & Pawe{l} Sakowski & Robert 'Slepaczuk, 2023, "Mean Absolute Directional Loss as a New Loss Function for Machine Learning Problems in Algorithmic Investment Strategies," Papers, arXiv.org, number 2309.10546, Sep.
- Axelsson, Birger & Song, Han-Suck, 2023, "Univariate Forecasting for REITs with Deep Learning: A Comparative Analysis with an ARIMA Model," Working Paper Series, Royal Institute of Technology, Department of Real Estate and Construction Management & Banking and Finance, number 23/10, Sep, revised 14 Nov 2023.
- Takanobu Mizuta & Isao Yagi, 2023, "Comparing effects of price limit and circuit breaker in stock exchanges by an agent-based model," Papers, arXiv.org, number 2309.10220, Sep.
- Francisco Castro & Jian Gao & S'ebastien Martin, 2023, "Human-AI Interactions and Societal Pitfalls," Papers, arXiv.org, number 2309.10448, Sep, revised Jul 2025.
- Léonard Tschora & Erwan Pierre & Marc Plantevit & Céline Robardet, 2022, "Electricity price forecasting on the day-ahead market using machine learning," Post-Print, HAL, number hal-03621974, May, DOI: 10.1016/j.apenergy.2022.118752.
- Francesca Biagini & Lukas Gonon & Niklas Walter, 2023, "Approximation Rates for Deep Calibration of (Rough) Stochastic Volatility Models," Papers, arXiv.org, number 2309.14784, Sep.
- Paul Glasserman & Harry Mamaysky & Jimmy Qin, 2023, "New News is Bad News," Papers, arXiv.org, number 2309.05560, Sep.
- Rotem Zelingher & David Makowski, 2022, "Forecasting Global Maize Prices From Regional Productions
[Prévision des prix mondiaux du maïs à partir des productions régionales]," Post-Print, HAL, number hal-03764942, Apr, DOI: 10.3389/fsufs.2022.836437. - Felix Chopra & Ingar Haaland & Ingar K. Haaland, 2023, "Conducting Qualitative Interviews with AI," CESifo Working Paper Series, CESifo, number 10666.
- Harrison H. Li & Art B. Owen, 2023, "Double machine learning and design in batch adaptive experiments," Papers, arXiv.org, number 2309.15297, Sep.
- Olivier de Bandt & Jean-Charles Bricongne & Julien Denes & Alexandre Dhenin & Annabelle De Gaye & Pierre-Antoine Robert, 2023, "Using the Press to Construct a New Indicator of Inflation Perceptions in France," Working papers, Banque de France, number 921.
- Kristina McElheran & J. Frank Li & Erik Brynjolfsson & Zachary Krof & Emin Dinlersoz & Lucia Foster & Nikolas Zolas, 2023, "AI Adoption in America: Who, What, and Where," Working Papers, Center for Economic Studies, U.S. Census Bureau, number 23-48, Sep.
- Jiashu Lou, 2023, "Stock Market Sentiment Classification and Backtesting via Fine-tuned BERT," Papers, arXiv.org, number 2309.11979, Sep.
- Harashima, Taiji, 2023, "Numerical Simulations of How Economic Inequality Increases in Democratic Countries," MPRA Paper, University Library of Munich, Germany, number 118710, Sep.
- Plantinga, Paul & Shilongo, Kristophina & Mudongo, Oarabile & Umubyeyi, Angelique & Gastrow, Michael & Razzano, Gabriella, 2023, "Responsible artificial intelligence in Africa: Towards policy learning," SocArXiv, Center for Open Science, number jyhae, Sep, DOI: 10.31219/osf.io/jyhae.