Report NEP-CMP-2022-08-15

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:

• Jinho Lee & Sungwoo Park & Jungyu Ahn & Jonghun Kwak, 2022, "ETF Portfolio Construction via Neural Network trained on Financial Statement Data," Papers, arXiv.org, number 2207.01187, Jul.
• Weilong Fu & Ali Hirsa, 2022, "Solving barrier options under stochastic volatility using deep learning," Papers, arXiv.org, number 2207.00524, Jul.
• Anthony Coache & Sebastian Jaimungal & 'Alvaro Cartea, 2022, "Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning," Papers, arXiv.org, number 2206.14666, Jun, revised May 2023.
• Vetter, Oliver A. & Hoffmann, Felix & Pumplun, Luisa & Buxmann, Peter, 2022, "What constitutes a machine-learning-driven business model? A taxonomy of B2B start-ups with machine learning at their core," 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 133080, Jun.
• Mark Joseph Bennett, 2022, "Accelerating Machine Learning Training Time for Limit Order Book Prediction," Papers, arXiv.org, number 2206.09041, Jun.
• Jonghun Kwak & Jungyu Ahn & Jinho Lee & Sungwoo Park, 2022, "Shai-am: A Machine Learning Platform for Investment Strategies," Papers, arXiv.org, number 2207.00436, Jul.
• Lee, Seungyub & Heberling, Matthew T. & Nietch, Christopher & Safwat, Amr, 2022, "Assessing a pay-for-performance conservation program using an agent-based modeling framework," 2022 Annual Meeting, July 31-August 2, Anaheim, California, Agricultural and Applied Economics Association, number 322301, Aug, DOI: 10.22004/ag.econ.322301.
• Villacis, Alexis & Badruddoza, Syed & Mayorga, Joaquin & Mishra, Ashok K., 2022, "Using Machine Learning to Test the Consistency of Food Insecurity Measures," 2022 Annual Meeting, July 31-August 2, Anaheim, California, Agricultural and Applied Economics Association, number 322472, Aug, DOI: 10.22004/ag.econ.322472.
• Cao, An N.Q. & Gebrekidan, Bisrat Haile & Heckelei, Thomas & Robe, Michel A., 2022, "County-level USDA Crop Progress and Condition data, machine learning, and commodity market surprises," 2022 Annual Meeting, July 31-August 2, Anaheim, California, Agricultural and Applied Economics Association, number 322281, Aug, DOI: 10.22004/ag.econ.322281.
• Item repec:hal:wpaper:hal-03716692 is not listed on IDEAS anymore

• Zhijie Zhang, 2022, "Identify Arbitrage Using Machine Learning on Multi-stock Pair Trading Price Forecasting," DSSR Discussion Papers, Graduate School of Economics and Management, Tohoku University, number 127, Jul.
• Luis Goncalves de Faria, 2022, "An Agent-Based Model With Realistic Financial Time Series: A Method for Agent-Based Models Validation," Papers, arXiv.org, number 2206.09772, Jun.
• Weronika Ormaniec & Marcin Pitera & Sajad Safarveisi & Thorsten Schmidt, 2022, "Estimating value at risk: LSTM vs. GARCH," Papers, arXiv.org, number 2207.10539, Jul.
• Jun-Cheng Chen & Cong-Xiao Chen & Li-Juan Duan & Zhi Cai, 2022, "DDPG based on multi-scale strokes for financial time series trading strategy," Papers, arXiv.org, number 2207.10071, Jun.
• Bryan T. Kelly & Semyon Malamud & Kangying Zhou, 2022, "The Virtue of Complexity in Return Prediction," NBER Working Papers, National Bureau of Economic Research, Inc, number 30217, Jul.
• Zöll, Anne & Eitle, Verena & Buxmann, Peter, 2022, "Machine Learning Adoption based on the TOE Framework: A Quantitative Study," 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 133079, Jul.
• Yichen Feng & Ming Min & Jean-Pierre Fouque, 2022, "Deep Learning for Systemic Risk Measures," Papers, arXiv.org, number 2207.00739, Jul.
• Kenji Kubo & Koichi Miyamoto & Kosuke Mitarai & Keisuke Fujii, 2022, "Pricing multi-asset derivatives by variational quantum algorithms," Papers, arXiv.org, number 2207.01277, Jul.
• In-Koo Cho & Jonathan Libgober, 2022, "Learning Underspecified Models," Papers, arXiv.org, number 2207.10140, Jul.
• Sander Sõna & Jaan Masso & Shakshi Sharma & Priit Vahter & Rajesh Sharma, 2022, "Predicting Company Innovativeness By Analysing The Website Data Of Firms: A Comparison Across Different Types Of Innovation," University of Tartu - Faculty of Economics and Business Administration Working Paper Series, Faculty of Economics and Business Administration, University of Tartu (Estonia), number 143.
• Alessandro Danovi & Marzio Roma & Davide Meloni & Stefano Olgiati & Fernando Metelli, 2022, "Baseline validation of a bias-mitigated loan screening model based on the European Banking Authority's trust elements of Big Data & Advanced Analytics applications using Artificial Intelligence," Papers, arXiv.org, number 2206.08938, Jun.
• Francis X. Diebold & Maximilian Goebel & Philippe Goulet Coulombe, 2022, "Assessing and Comparing Fixed-Target Forecasts of Arctic Sea Ice: Glide Charts for Feature-Engineered Linear Regression and Machine Learning Models," Papers, arXiv.org, number 2206.10721, Jun, revised Jun 2023.
• Jimei Shen & Yihan Mo & Christopher Plimpton & Mustafa Kaan Basaran, 2022, "AI in Asset Management and Rebellion Research," Papers, arXiv.org, number 2206.14876, Jun.
• Yanwei Jia & Xun Yu Zhou, 2022, "q-Learning in Continuous Time," Papers, arXiv.org, number 2207.00713, Jul, revised May 2025.
• Eleanor Loh & Jalaj Khandelwal & Brian Regan & Duncan A. Little, 2022, "Promotheus: An End-to-End Machine Learning Framework for Optimizing Markdown in Online Fashion E-commerce," Papers, arXiv.org, number 2207.01137, Jul, revised Aug 2022.
• Andrew Binning, 2022, "An Efficient Application of the Extended Path Algorithm in Matlab with Examples," Treasury Working Paper Series, New Zealand Treasury, number 22/02, Jul.
• Thibaut Plassot & Isidro Soloaga & Pedro J. Torres, 2022, "A Random Forest approach of the Evolution of Inequality of Opportunity in Mexico," Working Papers, ECINEQ, Society for the Study of Economic Inequality, number 614, Jun.
• Jeonggil Song, 2022, "Predicting Economic Welfare with Images on Wealth," Papers, arXiv.org, number 2206.14810, Jun.
• Nelson P. Rayl & Nitish R. Sinha, 2022, "Integrating Prediction and Attribution to Classify News," Finance and Economics Discussion Series, Board of Governors of the Federal Reserve System (U.S.), number 2022-042, Jul, DOI: 10.17016/FEDS.2022.042.
• Alcántara Mata, Antonio & Ruiz Mora, Carlos, 2022, "On data-driven chance constraint learning for mixed-integer optimization problems," DES - Working Papers. Statistics and Econometrics. WS, Universidad Carlos III de Madrid. Departamento de Estadística, number 35425, Jul.
• Rasoul Jamshidi & Mohammad Ebrahim Sadeghi, 2022, "Neural network based human reliability analysis method in production systems," Papers, arXiv.org, number 2206.11850, Jun.
• Ferrari Minesso, Massimo & Pagliari, Maria Sole, 2022, "DSGE Nash: solving Nash games in macro models," Working Paper Series, European Central Bank, number 2678, Jul.

Report NEP-CMP-2022-08-15

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: