Report NEP-CMP-2020-11-09

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:

• Miquel Noguer i Alonso & Sonam Srivastava, 2020. "Deep Reinforcement Learning for Asset Allocation in US Equities," Papers 2010.04404, arXiv.org.
• Dan Wang & Tianrui Wang & Ionuc{t} Florescu, 2020. "Is Image Encoding Beneficial for Deep Learning in Finance? An Analysis of Image Encoding Methods for the Application of Convolutional Neural Networks in Finance," Papers 2010.08698, arXiv.org.
• Yucheng Yang & Zhong Zheng & Weinan E, 2020. "Interpretable Neural Networks for Panel Data Analysis in Economics," Papers 2010.05311, arXiv.org, revised Nov 2020.
• Ollech, Daniel & Webel, Karsten, 2020. "A random forest-based approach to identifying the most informative seasonality tests," Discussion Papers 55/2020, Deutsche Bundesbank.
• Sean Cao & Wei Jiang & Baozhong Yang & Alan L. Zhang, 2020. "How to Talk When a Machine is Listening?: Corporate Disclosure in the Age of AI," NBER Working Papers 27950, National Bureau of Economic Research, Inc.
• P.B. Dixon & J.A. Giesecke & J. Nassios & M.T. Rimmer, 2020. "The Effects of Financial Decoupling of the U.S. and China: Simulations with a Global Financial CGE Model," Centre of Policy Studies/IMPACT Centre Working Papers g-309, Victoria University, Centre of Policy Studies/IMPACT Centre.
• Isao Yagi & Mahiro Hoshino & Takanobu Mizuta, 2020. "Analysis of the impact of maker-taker fees on the stock market using agent-based simulation," Papers 2010.08992, arXiv.org.
• David Byrd & Antigoni Polychroniadou, 2020. "Differentially Private Secure Multi-Party Computation for Federated Learning in Financial Applications," Papers 2010.05867, arXiv.org.
• Nosratabadi, Saeed & Mosavi, Amir & Duan, Puhong & Ghamisi, Pedram & Filip, Ferdinand & Band, Shahab S. & Reuter, Uwe & Gama, Joao & Gandomi, Amir H., 2020. "Data science in economics: comprehensive review of advanced machine learning and deep learning methods," OSF Preprints yc6e2, Center for Open Science.
• Eric Benhamou & David Saltiel & Sandrine Ungari & Abhishek Mukhopadhyay, 2020. "Bridging the gap between Markowitz planning and deep reinforcement learning," Papers 2010.09108, arXiv.org.
• Shenhao Wang & Baichuan Mo & Jinhua Zhao, 2020. "Theory-based residual neural networks: A synergy of discrete choice models and deep neural networks," Papers 2010.11644, arXiv.org.
• Rangan Gupta & Christian Pierdzioch & Afees A. Salisu, 2020. "Oil-Price Uncertainty and the U.K. Unemployment Rate: A Forecasting Experiment with Random Forests Using 150 Years of Data," Working Papers 202095, University of Pretoria, Department of Economics.
• Elior Nehemya & Yael Mathov & Asaf Shabtai & Yuval Elovici, 2020. "Taking Over the Stock Market: Adversarial Perturbations Against Algorithmic Traders," Papers 2010.09246, arXiv.org, revised Sep 2021.
• Huber, Martin & Imhof, David, 2020. "Transnational machine learning with screens for flagging bid-rigging cartels," FSES Working Papers 519, Faculty of Economics and Social Sciences, University of Freiburg/Fribourg Switzerland.
• Ma, Ji, 2020. "Automated coding using machine-learning and remapping the U.S. nonprofit sector: A guide and benchmark," OSF Preprints pt3q9, Center for Open Science.
• Taeyoung Doh & Dongho Song & Shu-Kuei X. Yang, 2020. "Deciphering Federal Reserve Communication via Text Analysis of Alternative FOMC Statements," Research Working Paper RWP 20-14, Federal Reserve Bank of Kansas City.
• Patrick T. Harker, 2020. "The Economy, the Pandemic, and Machine Learning," Speech 88805, Federal Reserve Bank of Philadelphia.
• Pradipta Banerjee & Subhrabrata Choudhury, 2020. "Pandemic Lessons -- Devising an assessment framework to analyse policies for sustainability," Papers 2010.04833, arXiv.org, revised May 2021.
• Hamzawi, Salah G., 2020. "Management and Planning of Airport Gate Capacity: A New Microcomputer Based Gate Assignment Simulation Model," 21st Annual Canadian Transportation Research Forum, Vancouver, British Columbia, May 28-30, 1986 305954, Canadian Transportation Research Forum (CTRF).
• Peiwan Wang & Lu Zong, 2020. "Are Crises Predictable? A Review of the Early Warning Systems in Currency and Stock Markets," Papers 2010.10132, arXiv.org.
• Huseyin Gurkan & Francis de Véricourt, 2020. "Contracting, pricing, and data collection under the AI flywheel effect," ESMT Research Working Papers ESMT-20-01_R1, ESMT European School of Management and Technology, revised 19 Oct 2020.
• Eren Kurshan & Hongda Shen & Jiahao Chen, 2020. "Towards Self-Regulating AI: Challenges and Opportunities of AI Model Governance in Financial Services," Papers 2010.04827, arXiv.org.
• Feng Zhang & Ruite Guo & Honggao Cao, 2020. "Information Coefficient as a Performance Measure of Stock Selection Models," Papers 2010.08601, arXiv.org.
• Tien Ha Duong, My & Van Nguyen, Diep & Thanh Nguyen, Phong, 2019. "Using Fuzzy Approach to Model Skill Shortage in Vietnam’s Labor Market in the Context of Industry 4.0," MPRA Paper 103512, University Library of Munich, Germany, revised 07 May 2020.
• S. Borağan Aruoba & Pablo Cuba-Borda & Kenji Higa-Flores & Frank Schorfheide & Sergio Villalvazo, 2020. "Piecewise-Linear Approximations and Filtering for DSGE Models with Occasionally Binding Constraints," NBER Working Papers 27991, National Bureau of Economic Research, Inc.