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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. 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:
- 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, arXiv.org, number 2112.03946, Nov.
- Carl Remlinger & Bri`ere Marie & Alasseur Cl'emence & Joseph Mikael, 2021, "Expert Aggregation for Financial Forecasting," Papers, arXiv.org, number 2111.15365, Nov, revised Jul 2023.
- Ben Hambly & Renyuan Xu & Huining Yang, 2021, "Recent Advances in Reinforcement Learning in Finance," Papers, arXiv.org, number 2112.04553, Dec, revised Feb 2023.
- Uta Pigorsch & Sebastian Schafer, 2021, "High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning," Papers, arXiv.org, number 2112.04755, Dec.
- 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, University Library of Munich, Germany, number 110824, Nov.
- Leonardo Perotti & Lech A. Grzelak, 2021, "Fast Sampling from Time-Integrated Bridges using Deep Learning," Papers, arXiv.org, number 2111.13901, Nov.
- Peng Zhou & Jingling Tang, 2021, "Improved Method of Stock Trading under Reinforcement Learning Based on DRQN and Sentiment Indicators ARBR," Papers, arXiv.org, number 2111.15356, Nov.
- Chris Redl & Sandile Hlatshwayo, 2021, "Forecasting Social Unrest: A Machine Learning Approach," IMF Working Papers, International Monetary Fund, number 2021/263, Nov.
- 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, Cardiff University, Cardiff Business School, Economics Section, number E2021/34, Dec.
- 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, ECINEQ, Society for the Study of Economic Inequality, number 596, Dec.
- Shujian Liao & Jian Chen & Hao Ni, 2021, "Forex Trading Volatility Prediction using Neural Network Models," Papers, arXiv.org, number 2112.01166, Dec, revised Dec 2021.
- Jianian Wang & Sheng Zhang & Yanghua Xiao & Rui Song, 2021, "A Review on Graph Neural Network Methods in Financial Applications," Papers, arXiv.org, number 2111.15367, Nov, 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, National Bureau of Economic Research, Inc, number 29559, Dec.
- 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, arXiv.org, number 2111.15332, Nov, revised Jul 2023.
- Klügl, Franziska & Kyvik Nordås, Hildegunn, 2021, "AI-enabled Automation, Trade, and the Future of Engineering Services," Working Papers, Örebro University, School of Business, number 2021:16, Dec.
- Konrad Menzel, 2021, "Structural Sieves," Papers, arXiv.org, number 2112.01377, Dec, 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, University of Pretoria, Department of Economics, number 202201, Jan.
- Roberto Daluiso & Emanuele Nastasi & Andrea Pallavicini & Stefano Polo, 2021, "Reinforcement learning for options on target volatility funds," Papers, arXiv.org, number 2112.01841, Dec.
- 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., Universidad del CEMA, number 820, Dec.
- Jean-David Fermanian & Dominique Guégan, 2021, "Fair learning with bagging," Documents de travail du Centre d'Economie de la Sorbonne, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne, number 21034, Nov.