Report NEP-CMP-2019-05-06

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:

• Yu Zheng & Yongxin Yang & Bowei Chen, 2019. "Incorporating prior financial domain knowledge into neural networks for implied volatility surface prediction," Papers 1904.12834, arXiv.org, revised May 2021.
• Allison Koenecke & Amita Gajewar, 2019. "Curriculum Learning in Deep Neural Networks for Financial Forecasting," Papers 1904.12887, arXiv.org, revised Jul 2019.
• Sen, Sugata, 2019. "Decomposition of intra-household disparity sensitive fuzzy multi-dimensional poverty index: A study of vulnerability through Machine Learning," MPRA Paper 93550, University Library of Munich, Germany.
• Zhou, Zhengyuan & Athey, Susan & Wager, Stefan, 2018. "Offline Multi-Action Policy Learning: Generalization and Optimization," Research Papers 3734, Stanford University, Graduate School of Business.
• Makoto Chiba & Mikari Kashima & Kenta Sekiguchi, 2019. "Legal Responsibility in Investment Decisions Using Algorithms and AI," Bank of Japan Research Laboratory Series 19-E-1, Bank of Japan.
• Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
• John A. Clithero & Jae Joon Lee & Joshua Tasoff, 2019. "Supervised Machine Learning for Eliciting Individual Demand," Papers 1904.13329, arXiv.org, revised Feb 2021.
• Jasdeep Kalsi & Terry Lyons & Imanol Perez Arribas, 2019. "Optimal execution with rough path signatures," Papers 1905.00728, arXiv.org.
• Badenes-Plá, Nuria & Gambau-Suelves, Borja & Navas Román, María, 2019. "Distributional and welfare effects of replacing monetary benefits with Universal Basic Income in Spain," EUROMOD Working Papers EM8/19, EUROMOD at the Institute for Social and Economic Research.
• Alessandro Balata & Michael Ludkovski & Aditya Maheshwari & Jan Palczewski, 2019. "Statistical Learning for Probability-Constrained Stochastic Optimal Control," Papers 1905.00107, arXiv.org, revised Aug 2020.
• Du, Ruihuan & Zhong, Yu & Nair, Harikesh S. & Cui, Bo & Shou, Ruyang, 2019. "Causally Driven Incremental Multi Touch Attribution Using a Recurrent Neural Network," Research Papers 3761, Stanford University, Graduate School of Business.
• Makarski, Krzysztof & Tyrowicz, Joanna & Malec, Magda, 2019. "Evaluating Welfare and Economic Effects of Raised Fertility," IZA Discussion Papers 12272, Institute of Labor Economics (IZA).
• Acemoglu, Daron & Restrepo, Pascual, 2019. "The Wrong Kind of AI? Artificial Intelligence and the Future of Labor Demand," IZA Discussion Papers 12292, Institute of Labor Economics (IZA).
• Pumplun, Luisa & Tauchert, Christoph & Heidt, Margareta, 2019. "A New Organizational Chassis for Artificial Intelligence - Exploring Organizational Readiness Factors," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 112582, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).