Report NEP-CMP-2022-01-10

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:

• Ashish Kumar & Abeer Alsadoon & P. W. C. Prasad & Salma Abdullah & Tarik A. Rashid & Duong Thu Hang Pham & Tran Quoc Vinh Nguyen, 2021. "Generative Adversarial Network (GAN) and Enhanced Root Mean Square Error (ERMSE): Deep Learning for Stock Price Movement Prediction," Papers 2112.03946, arXiv.org.
• Carl Remlinger & Bri`ere Marie & Alasseur Cl'emence & Joseph Mikael, 2021. "Expert Aggregation for Financial Forecasting," Papers 2111.15365, arXiv.org, revised Jul 2023.
• Ben Hambly & Renyuan Xu & Huining Yang, 2021. "Recent Advances in Reinforcement Learning in Finance," Papers 2112.04553, arXiv.org, revised Feb 2023.
• Uta Pigorsch & Sebastian Schafer, 2021. "High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning," Papers 2112.04755, arXiv.org.
• Kitova, Olga & Savinova, Victoria, 2021. "Development of an Ensemble of Models for Predicting Socio-Economic Indicators of the Russian Federation using IRT-Theory and Bagging Methods," MPRA Paper 110824, University Library of Munich, Germany.
• Leonardo Perotti & Lech A. Grzelak, 2021. "Fast Sampling from Time-Integrated Bridges using Deep Learning," Papers 2111.13901, arXiv.org.
• Peng Zhou & Jingling Tang, 2021. "Improved Method of Stock Trading under Reinforcement Learning Based on DRQN and Sentiment Indicators ARBR," Papers 2111.15356, arXiv.org.
• Chris Redl & Sandile Hlatshwayo, 2021. "Forecasting Social Unrest: A Machine Learning Approach," IMF Working Papers 2021/263, International Monetary Fund.
• Easaw, Joshy & Fang, Yongmei & Heravi, Saeed, 2021. "Using Polls to Forecast Popular Vote Share for US Presidential Elections 2016 and 2020: An Optimal Forecast Combination Based on Ensemble Empirical Model," Cardiff Economics Working Papers E2021/34, Cardiff University, Cardiff Business School, Economics Section.
• Paolo Brunori & Apostolos Davillas & Andrew Jones & Giovanna Scarchilli, 2021. "Model-based Recursive Partitioning to Estimate Unfair Health Inequalities in the United Kingdom Household Longitudinal Study," Working Papers 596, ECINEQ, Society for the Study of Economic Inequality.
• Shujian Liao & Jian Chen & Hao Ni, 2021. "Forex Trading Volatility Prediction using Neural Network Models," Papers 2112.01166, arXiv.org, revised Dec 2021.
• Jianian Wang & Sheng Zhang & Yanghua Xiao & Rui Song, 2021. "A Review on Graph Neural Network Methods in Financial Applications," Papers 2111.15367, arXiv.org, revised Apr 2022.
• Victor Duarte & Julia Fonseca & Aaron S. Goodman & Jonathan A. Parker, 2021. "Simple Allocation Rules and Optimal Portfolio Choice Over the Lifecycle," NBER Working Papers 29559, National Bureau of Economic Research, Inc.
• Jo~ao F. Doriguello & Alessandro Luongo & Jinge Bao & Patrick Rebentrost & Miklos Santha, 2021. "Quantum algorithm for stochastic optimal stopping problems with applications in finance," Papers 2111.15332, arXiv.org, revised Jul 2023.
• Klügl, Franziska & Kyvik Nordås, Hildegunn, 2021. "AI-enabled Automation, Trade, and the Future of Engineering Services," Working Papers 2021:16, Örebro University, School of Business.
• Konrad Menzel, 2021. "Structural Sieves," Papers 2112.01377, arXiv.org, revised Apr 2022.
• Rangan Gupta & Sayar Karmakar & Christian Pierdzioch, 2022. "Safe Havens, Machine Learning, and the Sources of Geopolitical Risk: A Forecasting Analysis Using Over a Century of Data," Working Papers 202201, University of Pretoria, Department of Economics.
• Roberto Daluiso & Emanuele Nastasi & Andrea Pallavicini & Stefano Polo, 2021. "Reinforcement learning for options on target volatility funds," Papers 2112.01841, arXiv.org.
• Tomás Marinozzi & Leandro Nallar & Sergio Pernice, 2021. "Intuitive Mathematical Economics Series. General Equilibrium Models and the Gradient Field Method," CEMA Working Papers: Serie Documentos de Trabajo. 820, Universidad del CEMA.
• Jean-David Fermanian & Dominique Guégan, 2021. "Fair learning with bagging," Documents de travail du Centre d'Economie de la Sorbonne 21034, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.