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. Stan Miles issued this report. It is usually issued weekly.

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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 2207.01187, arXiv.org.
• Weilong Fu & Ali Hirsa, 2022. "Solving barrier options under stochastic volatility using deep learning," Papers 2207.00524, arXiv.org.
• Anthony Coache & Sebastian Jaimungal & 'Alvaro Cartea, 2022. "Conditionally Elicitable Dynamic Risk Measures for Deep Reinforcement Learning," Papers 2206.14666, arXiv.org, 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) 133080, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Mark Joseph Bennett, 2022. "Accelerating Machine Learning Training Time for Limit Order Book Prediction," Papers 2206.09041, arXiv.org.
• Jonghun Kwak & Jungyu Ahn & Jinho Lee & Sungwoo Park, 2022. "Shai-am: A Machine Learning Platform for Investment Strategies," Papers 2207.00436, arXiv.org.
• 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 322301, Agricultural and Applied Economics Association.
• 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 322472, Agricultural and Applied Economics Association.
• 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 322281, Agricultural and Applied Economics Association.
• 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 127, Graduate School of Economics and Management, Tohoku University.
• Luis Goncalves de Faria, 2022. "An Agent-Based Model With Realistic Financial Time Series: A Method for Agent-Based Models Validation," Papers 2206.09772, arXiv.org.
• Weronika Ormaniec & Marcin Pitera & Sajad Safarveisi & Thorsten Schmidt, 2022. "Estimating value at risk: LSTM vs. GARCH," Papers 2207.10539, arXiv.org.
• 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 2207.10071, arXiv.org.
• Bryan T. Kelly & Semyon Malamud & Kangying Zhou, 2022. "The Virtue of Complexity in Return Prediction," NBER Working Papers 30217, National Bureau of Economic Research, Inc.
• 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) 133079, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Yichen Feng & Ming Min & Jean-Pierre Fouque, 2022. "Deep Learning for Systemic Risk Measures," Papers 2207.00739, arXiv.org.
• Kenji Kubo & Koichi Miyamoto & Kosuke Mitarai & Keisuke Fujii, 2022. "Pricing multi-asset derivatives by variational quantum algorithms," Papers 2207.01277, arXiv.org.
• In-Koo Cho & Jonathan Libgober, 2022. "Learning Underspecified Models," Papers 2207.10140, arXiv.org.
• 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 143, Faculty of Economics and Business Administration, University of Tartu (Estonia).
• 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 2206.08938, arXiv.org.
• 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 2206.10721, arXiv.org, revised Jun 2023.
• Jimei Shen & Yihan Mo & Christopher Plimpton & Mustafa Kaan Basaran, 2022. "AI in Asset Management and Rebellion Research," Papers 2206.14876, arXiv.org.
• Yanwei Jia & Xun Yu Zhou, 2022. "q-Learning in Continuous Time," Papers 2207.00713, arXiv.org, revised Apr 2023.
• 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 2207.01137, arXiv.org, revised Aug 2022.
• Andrew Binning, 2022. "An Efficient Application of the Extended Path Algorithm in Matlab with Examples," Treasury Working Paper Series 22/02, New Zealand Treasury.
• Thibaut Plassot & Isidro Soloaga & Pedro J. Torres, 2022. "A Random Forest approach of the Evolution of Inequality of Opportunity in Mexico," Working Papers 614, ECINEQ, Society for the Study of Economic Inequality.
• Jeonggil Song, 2022. "Predicting Economic Welfare with Images on Wealth," Papers 2206.14810, arXiv.org.
• Nelson P. Rayl & Nitish R. Sinha, 2022. "Integrating Prediction and Attribution to Classify News," Finance and Economics Discussion Series 2022-042, Board of Governors of the Federal Reserve System (U.S.).
• Item repec:cte:wsrepe:35425 is not listed on IDEAS anymore

• Rasoul Jamshidi & Mohammad Ebrahim Sadeghi, 2022. "Neural network based human reliability analysis method in production systems," Papers 2206.11850, arXiv.org.
• Ferrari Minesso, Massimo & Pagliari, Maria Sole, 2022. "DSGE Nash: solving Nash games in macro models," Working Paper Series 2678, European Central Bank.