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Report NEP-CMP-2022-05-30
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:
- Pasquale Maddaloni & Davide Nicola Continanza & Andrea del Monaco & Daniele Figoli & Marco di Lucido & Filippo Quarta & Giuseppe Turturiello, 2022, "Stacking machine-learning models for anomaly detection: comparing AnaCredit to other banking datasets," Questioni di Economia e Finanza (Occasional Papers), Bank of Italy, Economic Research and International Relations Area, number 689, Apr.
- Alexander Brunhuemer & Lukas Larcher & Philipp Seidl & Sascha Desmettre & Johannes Kofler & Gerhard Larcher, 2022, "Supervised machine learning classification for short straddles on the S&P500," Papers, arXiv.org, number 2204.13587, Apr.
- Francois Mercier & Makesh Narsimhan, 2022, "Discovering material information using hierarchical Reformer model on financial regulatory filings," Papers, arXiv.org, number 2204.05979, Mar.
- Francis de Véricourt & Huseyin Gurkan, 2022, "Is your machine better than you? You may never know," ESMT Research Working Papers, ESMT European School of Management and Technology, number ESMT-22-02, May.
- Mikkel Bennedsen & Eric Hillebrand & Sebastian Jensen, 2022, "A Neural Network Approach to the Environmental Kuznets Curve," CREATES Research Papers, Department of Economics and Business Economics, Aarhus University, number 2022-09, May.
- Jakub Drahokoupil, 2022, "Application of the XGBoost algorithm and Bayesian optimization for the Bitcoin price prediction during the COVID-19 period," FFA Working Papers, Prague University of Economics and Business, number 4.006, Mar, revised 09 May 2022.
- Masanori Hirano & Hiroki Sakaji & Kiyoshi Izumi, 2022, "Policy Gradient Stock GAN for Realistic Discrete Order Data Generation in Financial Markets," Papers, arXiv.org, number 2204.13338, Apr.
- Ms. Natasha X Che & Xuege Zhang, 2022, "High Performance Export Portfolio: Design Growth-Enhancing Export Structure with Machine Learning," IMF Working Papers, International Monetary Fund, number 2022/075, Apr.
- Xianfei Hui & Baiqing Sun & Hui Jiang & Yan Zhou, 2022, "Modeling dynamic volatility under uncertain environment with fuzziness and randomness," Papers, arXiv.org, number 2204.12657, Apr, revised Oct 2022.
- Yoan Hermstrüwer & Pascal Langenbach, 2022, "Fair Governance with Humans and Machines," Discussion Paper Series of the Max Planck Institute for Behavioral Economics, Max Planck Institute for Behavioral Economics, number 2022_04, May, revised 01 Mar 2023.
- Karim Amzile & Mohamed Habachi, 2022, "Assessment of Support Vector Machine performance for default prediction and credit rating," Post-Print, HAL, number halshs-03643738, DOI: 10.21511/bbs.17(1).2022.14.
- Sihao Hu & Zhen Zhang & Shengliang Lu & Bingsheng He & Zhao Li, 2022, "Sequence-Based Target Coin Prediction for Cryptocurrency Pump-and-Dump," Papers, arXiv.org, number 2204.12929, Apr, revised Apr 2023.
- Gour Sundar Mitra Thakur & Rupak Bhattacharyya & Seema Sarkar, 2022, "Fuzzy Expert System for Stock Portfolio Selection: An Application to Bombay Stock Exchange," Papers, arXiv.org, number 2204.13385, Apr, revised May 2022.
- Ali Raheman & Anton Kolonin & Alexey Glushchenko & Arseniy Fokin & Ikram Ansari, 2022, "Adaptive Multi-Strategy Market-Making Agent For Volatile Markets," Papers, arXiv.org, number 2204.13265, Apr.
- Matilde Marcolli, 2022, "Pareto Optimization in Categories," Papers, arXiv.org, number 2204.11931, Apr.