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Report NEP-CMP-2022-08-08
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:
- Fengyu Han & Yue Wang, 2022, "Predicting Stock Price Movement after Disclosure of Corporate Annual Reports: A Case Study of 2021 China CSI 300 Stocks," Papers, arXiv.org, number 2206.12528, Jun, revised Jul 2022.
- Vladimir Skavysh & Sofia Priazhkina & Diego Guala & Thomas Bromley, 2022, "Quantum Monte Carlo for Economics: Stress Testing and Macroeconomic Deep Learning," Staff Working Papers, Bank of Canada, number 22-29, Jun, DOI: 10.34989/swp-2022-29.
- Frensi Zejnullahu & Maurice Moser & Joerg Osterrieder, 2022, "Applications of Reinforcement Learning in Finance -- Trading with a Double Deep Q-Network," Papers, arXiv.org, number 2206.14267, Jun.
- Emanuele Ciola & Enrico Turco & Andrea Gurgone & Davide Bazzana & Sergio Vergalli & Francesco Menoncin, 2022, "Charging the macroeconomy with an energy sector: an agent-based model," Working Papers, Fondazione Eni Enrico Mattei, number 2022.09, Mar.
- Elivelto Ebermam & Helder Knidel & Renato A. Krohling, 2022, "Development of a hybrid method for stock trading based on TOPSIS, EMD and ELM," Papers, arXiv.org, number 2206.06723, Jun.
- Yiqi Deng & Siu Ming Yiu, 2022, "Deep Multiple Instance Learning For Forecasting Stock Trends Using Financial News," Papers, arXiv.org, number 2206.14452, Jun.
- Zitao Song & Xuyang Jin & Chenliang Li, 2022, "Safe-FinRL: A Low Bias and Variance Deep Reinforcement Learning Implementation for High-Freq Stock Trading," Papers, arXiv.org, number 2206.05910, Jun.
- Mathieu Rosenbaum & Jianfei Zhang, 2022, "On the universality of the volatility formation process: when machine learning and rough volatility agree," Papers, arXiv.org, number 2206.14114, Jun.
- Luyao Zhang & Tianyu Wu & Saad Lahrichi & Carlos-Gustavo Salas-Flores & Jiayi Li, 2022, "A Data Science Pipeline for Algorithmic Trading: A Comparative Study of Applications for Finance and Cryptoeconomics," Papers, arXiv.org, number 2206.14932, Jun.
- Angelica Salvi del Pero & Peter Wyckoff & Ann Vourc'h, 2022, "Using Artificial Intelligence in the workplace: What are the main ethical risks?," OECD Social, Employment and Migration Working Papers, OECD Publishing, number 273, Jul, DOI: 10.1787/840a2d9f-en.