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Report NEP-CMP-2022-06-20
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:
- Sonan Memon, 2021, "Machine Learning for Economists: An Introduction," PIDE Knowledge Brief, Pakistan Institute of Development Economics, number 2021:33.
- Andrew Caplin & Daniel Martin & Philip Marx, 2022, "Calibrating for Class Weights by Modeling Machine Learning," Papers, arXiv.org, number 2205.04613, May, revised Jul 2022.
- A. Max Reppen & H. Mete Soner & Valentin Tissot-Daguette, 2022, "Deep Stochastic Optimization in Finance," Papers, arXiv.org, number 2205.04604, May.
- Reisenhofer, Rafael & Bayer, Xandro & Hautsch, Nikolaus, 2022, "HARNet: A convolutional neural network for realized volatility forecasting," CFS Working Paper Series, Center for Financial Studies (CFS), number 680.
- Margherita Doria & Elisa Luciano & Patrizia Semeraro, 2022, "Machine learning techniques in joint default assessment," Papers, arXiv.org, number 2205.01524, May, revised Sep 2023.
- Florian Peters & Doris Neuberger & Oliver Reinhardt & Adelinde Uhrmacher, 2022, "A basic macroeconomic agent-based model for analyzing monetary regime shifts," Papers, arXiv.org, number 2205.00752, May.
- Ryan Defina, 2021, "Machine Learning Methods: Potential for Deposit Insurance," IADI Fintech Briefs, International Association of Deposit Insurers, number 3, Sep.
- Chenrui Zhang, 2022, "Deep learning based Chinese text sentiment mining and stock market correlation research," Papers, arXiv.org, number 2205.04743, May.
- Corrado Monti & Marco Pangallo & Gianmarco De Francisci Morales & Francesco Bonchi, 2022, "On learning agent-based models from data," Papers, arXiv.org, number 2205.05052, May, revised Nov 2022.
- Bouët, Antoine & Laborde Debucquet, David & Traoré, Fousseini, 2022, "MIRAGRODEP-AEZ 1.0: Documentation," AGRODEP technical notes, International Food Policy Research Institute (IFPRI), number TN-24.
- MARTINEZ PLUMED Fernando & CABALLERO BENÍTEZ Fernando & CASTELLANO FALCÓN David & FERNANDEZ LLORCA David & GOMEZ Emilia & HUPONT TORRES Isabelle & MERINO Luis & MONSERRAT Carlos & HERNÁNDEZ ORALLO Jos, 2022, "AI Watch: Revisiting Technology Readiness Levels for relevant Artificial Intelligence technologies," JRC Research Reports, Joint Research Centre, number JRC129399, May.
- Jay Cao & Jacky Chen & Soroush Farghadani & John Hull & Zissis Poulos & Zeyu Wang & Jun Yuan, 2022, "Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning," Papers, arXiv.org, number 2205.05614, May, revised Jan 2023.
- Andreas Haupt & Aroon Narayanan, 2022, "Risk Preferences of Learning Algorithms," Papers, arXiv.org, number 2205.04619, May, revised Dec 2023.
- Cyril Bachelard & Apostolos Chalkis & Vissarion Fisikopoulos & Elias Tsigaridas, 2022, "Randomized geometric tools for anomaly detection in stock markets," Papers, arXiv.org, number 2205.03852, May, revised May 2022.
- van Loon, Austin, 2022, "Three Families of Automated Text Analysis," SocArXiv, Center for Open Science, number htnej, May, DOI: 10.31219/osf.io/htnej.
- Chenrui Zhang & Xinyi Wu & Hailu Deng & Huiwei Zhang, 2022, "A time-varying study of Chinese investor sentiment, stock market liquidity and volatility: Based on deep learning BERT model and TVP-VAR model," Papers, arXiv.org, number 2205.05719, May, revised May 2022.
- Bratanova, Alexandra & Pham, Hien & Mason, Claire & Hajkowicz, Stefan & Naughtin, Claire & Schleiger, Emma & Sanderson, Conrad & Chen, Caron & Karimi, Sarvnaz, 2022, "Differentiating artificial intelligence capability clusters in Australia," MPRA Paper, University Library of Munich, Germany, number 113237, May.
- Mike Ludkovski & Glen Swindle & Eric Grannan, 2022, "Large Scale Probabilistic Simulation of Renewables Production," Papers, arXiv.org, number 2205.04736, May.
- Sarah A. Jacobson & Luyao Zhang & Jiasheng Zhu, 2022, "The Right Tool for the Job: Matching Active Learning Techniques to Learning Objectives," Papers, arXiv.org, number 2205.03393, May, revised Jul 2022.
- Tatjana Evas & Maikki Sipinen & Martin Ulbrich & Alessandro Dalla Benetta & Maciej Sobolewski & Daniel Nepelski, 2022, "AI Watch: Estimating AI investments in the European Union," JRC Research Reports, Joint Research Centre, number JRC129174, May.