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Report NEP-CMP-2022-08-22
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:
- Zarak Jamal Khan, 2021, "Machine Learning: An Introduction for Economists," PIDE Webinar Brief, Pakistan Institute of Development Economics, number 2021:62.
- Badruddoza, Syed & Fuad, Syed M. & Amin, Modhurima, 2022, "Comparative Effectiveness of Machine Learning Methods for Causal Inference in Agricultural Economics," 2022 Annual Meeting, July 31-August 2, Anaheim, California, Agricultural and Applied Economics Association, number 322452, Aug, DOI: 10.22004/ag.econ.322452.
- Jerinsh Jeyapaulraj & Dhruv Desai & Peter Chu & Dhagash Mehta & Stefano Pasquali & Philip Sommer, 2022, "Supervised similarity learning for corporate bonds using Random Forest proximities," Papers, arXiv.org, number 2207.04368, Jul, revised Oct 2022.
- Jimei Shen & Zhehu Yuan & Yifan Jin, 2022, "AlphaMLDigger: A Novel Machine Learning Solution to Explore Excess Return on Investment," Papers, arXiv.org, number 2206.11072, Jun, revised Dec 2022.
- Jungyu Ahn & Sungwoo Park & Jiwoon Kim & Ju-hong Lee, 2022, "Reinforcement Learning Portfolio Manager Framework with Monte Carlo Simulation," Papers, arXiv.org, number 2207.02458, Jul.
- Federico Mecchia & Marcellino Gaudenzi, 2022, "The dynamics of the prices of the companies of the STOXX Europe 600 Index through the logit model and neural network," Papers, arXiv.org, number 2206.09899, Jun.
- Charl Maree & Christian W. Omlin, 2022, "Balancing Profit, Risk, and Sustainability for Portfolio Management," Papers, arXiv.org, number 2207.02134, Jun.
- Emanuel Kohlscheen & Richhild Moessner, 2022, "Changing Electricity Markets: Quantifying the Price Effects of Greening the Energy Matrix," CESifo Working Paper Series, CESifo, number 9807.
- Emily Aiken & Guadalupe Bedoya & Joshua Blumenstock & Aidan Coville, 2022, "Program Targeting with Machine Learning and Mobile Phone Data: Evidence from an Anti-Poverty Intervention in Afghanistan," Papers, arXiv.org, number 2206.11400, Jun.
- Hans Buehler & Phillip Murray & Ben Wood, 2022, "Deep Bellman Hedging," Papers, arXiv.org, number 2207.00932, Jul, revised Jun 2024.
- Sylvia Klosin & Max Vilgalys, 2022, "Estimating Continuous Treatment Effects in Panel Data using Machine Learning with a Climate Application," Papers, arXiv.org, number 2207.08789, Jul, revised Oct 2025.
- Man-, ZuyiKeunZuyi Wang & Takagi, Chifumi & Kim, Man-Keun & Chung, Anh, 2022, "Uncover Drivers Influencing Consumers' WTP Using Machine Learning: Case of Organic Coffee in Taiwan," 2022 Annual Meeting, July 31-August 2, Anaheim, California, Agricultural and Applied Economics Association, number 322150, Aug, DOI: 10.22004/ag.econ.322150.
- Alexey Kushnir & James Michelson, 2022, "Optimal Multi-Dimensional Auctions: Conjectures and Simulations," Papers, arXiv.org, number 2207.01664, Jul.
- Menna Hassan & Nourhan Sakr & Arthur Charpentier, 2022, "Government Intervention in Catastrophe Insurance Markets: A Reinforcement Learning Approach," Papers, arXiv.org, number 2207.01010, Jul.
- Bryan T. Kelly & Semyon Malamud & Kangying Zhou, 2022, "The Virtue of Complexity Everywhere," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 22-57, Jul.
- Dimitrios Vamvourellis & Mate Attila Toth & Dhruv Desai & Dhagash Mehta & Stefano Pasquali, 2022, "Learning Mutual Fund Categorization using Natural Language Processing," Papers, arXiv.org, number 2207.04959, Jul.