Report NEP-CMP-2023-11-06

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:

• Zhengyong Jiang & Jeyan Thiayagalingam & Jionglong Su & Jinjun Liang, 2023, "CAD: Clustering And Deep Reinforcement Learning Based Multi-Period Portfolio Management Strategy," Papers, arXiv.org, number 2310.01319, Oct.
• Stephanie Houle & Ryan Macdonald, 2023, "Identifying Nascent High-Growth Firms Using Machine Learning," Staff Working Papers, Bank of Canada, number 23-53, Oct, DOI: 10.34989/swp-2023-53.
• Fung, Esabella, 2023, "A machine learning approach for assessing labor supply to the online labor market," MPRA Paper, University Library of Munich, Germany, number 118844, Oct.
• Walter Sosa Escudero, 2023, "Big Data y Algoritmos para la Medición de la Pobreza y el Desarrollo," CEDLAS, Working Papers, CEDLAS, Universidad Nacional de La Plata, number 0319, Oct.
• Leon Bremer, 2023, "Fuzzy firm name matching: Merging Amadeus firm data to PATSTAT," Tinbergen Institute Discussion Papers, Tinbergen Institute, number 23-055/VIII, Oct.
• Mercedes de Luis & Emilio Rodríguez & Diego Torres, 2023, "Machine learning applied to active fixed-income portfolio management: a Lasso logit approach," Working Papers, Banco de España, number 2324, Sep, DOI: https://doi.org/10.53479/33560.
• Rim, Maria J. & Kwon, Youngsun, 2023, "Collecting, generating and analyzing national statistics with AI: what benefits and costs?," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done?, International Telecommunications Society (ITS), number 278015.
• Eitle, Verena, 2023, "Adoption of Artificial Intelligence in an Organizational Context: Analysis of the Factors Influencing the Adoption and Decision-Making Process," 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 140616, Oct.
• Jakub Michańków & Paweł Sakowski & Robert Ślepaczuk, 2023, "Hedging Properties of Algorithmic Investment Strategies using Long Short-Term Memory and Time Series models for Equity Indices," Working Papers, Faculty of Economic Sciences, University of Warsaw, number 2023-25.
• Nicolas Fanta & Roman Horvath, 2023, "Artificial Intelligence and Central Bank Communication: The Case of the ECB," Working Papers IES, Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies, number 2023/29, Sep, revised Sep 2023.
• Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023, "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print, HAL, number hal-04219546, Sep, DOI: 10.1016/j.ejor.2023.09.026.
• Billio, Monica & Casarin, Roberto & Costola, Michele & Veggente, Veronica, 2023, "Learning from experts: Energy efficiency in residential buildings," SAFE Working Paper Series, Leibniz Institute for Financial Research SAFE, number 403, DOI: 10.2139/ssrn.4596682.
• Lisa D. Cook, 2023, "Generative AI, Productivity, the Labor Market, and Choice Behavior: A speech at the National Bureau of Economic Research Economics of Artificial Intelligence Conference, Fall 2023, Toronto, Canada, Sept. 22, 2023," Speech, Board of Governors of the Federal Reserve System (U.S.), number 96966, Sep.
• Lechardoy, Lucie & López Forés, Laura & Codagnone, Cristiano, 2023, "Artificial intelligence at the workplace and the impacts on work organisation, working conditions and ethics," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done?, International Telecommunications Society (ITS), number 277997.
• Jakub Micha'nk'ow & {L}ukasz Kwiatkowski & Janusz Morajda, 2023, "Combining Deep Learning and GARCH Models for Financial Volatility and Risk Forecasting," Papers, arXiv.org, number 2310.01063, Oct.
• Sukwoong Choi & Hyo Kang & Namil Kim & Junsik Kim, 2023, "How Does Artificial Intelligence Improve Human Decision-Making? Evidence from the AI-Powered Go Program," Papers, arXiv.org, number 2310.08704, Oct, revised Jan 2025.
• Nam, Jinyoung & Kim, Junghwan & Jung, Yoonhyuk, 2023, "Understandings of the AI business ecosystem in South Korea: AI startups' perspective," 32nd European Regional ITS Conference, Madrid 2023: Realising the digital decade in the European Union – Easier said than done?, International Telecommunications Society (ITS), number 278005.
• Hsiang-Hui Liu & Han-Jay Shu & Wei-Ning Chiu, 2023, "NoxTrader: LSTM-Based Stock Return Momentum Prediction for Quantitative Trading," Papers, arXiv.org, number 2310.00747, Oct, revised Oct 2023.
• Jameel, Alaa S. & Harjan, Sinan Abdullah & Ahmad, Abd Rahman, 2023, "Behavioral Intentions to use Artificial Intelligence Among Managers in Small and Medium Enterprises," OSF Preprints, Center for Open Science, number w69yh, Jul, DOI: 10.31219/osf.io/w69yh.
• Ethan Callanan & Amarachi Mbakwe & Antony Papadimitriou & Yulong Pei & Mathieu Sibue & Xiaodan Zhu & Zhiqiang Ma & Xiaomo Liu & Sameena Shah, 2023, "Can GPT models be Financial Analysts? An Evaluation of ChatGPT and GPT-4 on mock CFA Exams," Papers, arXiv.org, number 2310.08678, Oct.
• Guarascio, Dario & Reljic, Jelena & Stöllinger, Roman, 2023, "Artificial Intelligence and Employment: A Look into the Crystal Ball," GLO Discussion Paper Series, Global Labor Organization (GLO), number 1333.
• Ajay K. Agrawal & Joshua S. Gans & Avi Goldfarb, 2023, "The Turing Transformation: Artificial Intelligence, Intelligence Augmentation, and Skill Premiums," NBER Working Papers, National Bureau of Economic Research, Inc, number 31767, Oct.
• Susan Athey & Niall Keleher & Jann Spiess, 2023, "Machine Learning Who to Nudge: Causal vs Predictive Targeting in a Field Experiment on Student Financial Aid Renewal," Papers, arXiv.org, number 2310.08672, Oct, revised May 2024.
• Qinmeng Luan & James Hamp, 2023, "Automated regime detection in multidimensional time series data using sliced Wasserstein k-means clustering," Papers, arXiv.org, number 2310.01285, Oct.

Report NEP-CMP-2023-11-06

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: