Report NEP-CMP-2022-05-02

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:

• Bernardo Alves Furtado & Gustavo Onofre Andre~ao, 2022, "Machine Learning Simulates Agent-Based Model Towards Policy," Papers, arXiv.org, number 2203.02576, Mar, revised Nov 2022.
• Fedor Zagumennov, 2021, "In-Firm Planning and Business Processes Management Using Deep Neural Networks," GATR Journals, Global Academy of Training and Research (GATR) Enterprise, number jber213, Dec, DOI: https://doi.org/10.35609/jber.2021..
• Martin Magris & Mostafa Shabani & Alexandros Iosifidis, 2022, "Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets," Papers, arXiv.org, number 2203.03613, Mar, revised Jan 2023.
• Cameron Fen & Samir Undavia, 2022, "Improving Macroeconomic Model Validity and Forecasting Performance with Pooled Country Data using Structural, Reduced Form, and Neural Network Model," Papers, arXiv.org, number 2203.06540, Mar.
• Gian Maria Campedelli, 2022, "Explainable Machine Learning for Predicting Homicide Clearance in the United States," Papers, arXiv.org, number 2203.04768, Mar.
• Ola Hall & Mattias Ohlsson & Thortseinn Rognvaldsson, 2022, "Satellite Image and Machine Learning based Knowledge Extraction in the Poverty and Welfare Domain," Papers, arXiv.org, number 2203.01068, Mar.
• Narayana Darapaneni & Anwesh Reddy Paduri & Himank Sharma & Milind Manjrekar & Nutan Hindlekar & Pranali Bhagat & Usha Aiyer & Yogesh Agarwal, 2022, "Stock Price Prediction using Sentiment Analysis and Deep Learning for Indian Markets," Papers, arXiv.org, number 2204.05783, Apr.
• Ariel Neufeld & Julian Sester & Daiying Yin, 2022, "Detecting data-driven robust statistical arbitrage strategies with deep neural networks," Papers, arXiv.org, number 2203.03179, Mar, revised Feb 2024.
• Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022, "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers, arXiv.org, number 2203.03342, Mar, revised Nov 2022.
• Raad Khraishi & Ramin Okhrati, 2022, "Offline Deep Reinforcement Learning for Dynamic Pricing of Consumer Credit," Papers, arXiv.org, number 2203.03003, Mar.
• Javad T. Firouzjaee & Pouriya Khaliliyan, 2022, "Machine learning model to project the impact of Ukraine crisis," Papers, arXiv.org, number 2203.01738, Mar.
• Philippe Cotte & Pierre Lagier & Vincent Margot & Christophe Geissler, 2022, "Making use of supercomputers in financial machine learning," Papers, arXiv.org, number 2203.00427, Mar.
• Jase Clarkson & Mihai Cucuringu & Andrew Elliott & Gesine Reinert, 2022, "DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series," Papers, arXiv.org, number 2203.15009, Mar, revised Oct 2023.
• Federico Cornalba & Constantin Disselkamp & Davide Scassola & Christopher Helf, 2022, "Multi-Objective reward generalization: Improving performance of Deep Reinforcement Learning for applications in single-asset trading," Papers, arXiv.org, number 2203.04579, Mar, revised Feb 2023.
• Jagoda Kaszowska-Mojsa & Przemyslaw Wlodarczyk, 2020, "To freeze or not to freeze? Epidemic prevention and control in the DSGE model with agent-based epidemic component," Lodz Economics Working Papers, University of Lodz, Faculty of Economics and Sociology, number 3/2020, Nov.
• Victor Chernozhukov & Whitney Newey & Rahul Singh & Vasilis Syrgkanis, 2022, "Automatic Debiased Machine Learning for Dynamic Treatment Effects and General Nested Functionals," Papers, arXiv.org, number 2203.13887, Mar, revised Jun 2023.
• Cameron Fen, 2022, "Fast Simulation-Based Bayesian Estimation of Heterogeneous and Representative Agent Models using Normalizing Flow Neural Networks," Papers, arXiv.org, number 2203.06537, Mar.
• Mufhumudzi Muthivhi & Terence L. van Zyl, 2022, "Fusion of Sentiment and Asset Price Predictions for Portfolio Optimization," Papers, arXiv.org, number 2203.05673, Mar.
• Yuanrong Wang & Tomaso Aste, 2022, "Sparsification and Filtering for Spatial-temporal GNN in Multivariate Time-series," Papers, arXiv.org, number 2203.03991, Mar.
• Resce, Giuliano & Vaquero-Pineiro, Cristina, 2022, "Predicting Agri-food Quality across Space: A Machine Learning Model for the Acknowledgment of Geographical Indications," Economics & Statistics Discussion Papers, University of Molise, Department of Economics, number esdp22082, Apr.
• Martin Vesel'y, 2022, "Application of Quantum Computers in Foreign Exchange Reserves Management," Papers, arXiv.org, number 2203.15716, Mar.
• Deborah Sulem & Henry Kenlay & Mihai Cucuringu & Xiaowen Dong, 2022, "Graph similarity learning for change-point detection in dynamic networks," Papers, arXiv.org, number 2203.15470, Mar.
• Pok Wah Chan, 2022, "DeepTrust: A Reliable Financial Knowledge Retrieval Framework For Explaining Extreme Pricing Anomalies," Papers, arXiv.org, number 2203.08144, Mar.
• Rama Cont & Mihai Cucuringu & Renyuan Xu & Chao Zhang, 2022, "Tail-GAN: Learning to Simulate Tail Risk Scenarios," Papers, arXiv.org, number 2203.01664, Mar, revised May 2025.
• Isaiah Hull & Anna Grodecka-Messi, 2022, "Measuring the Impact of Taxes and Public Services on Property Values: A Double Machine Learning Approach," Papers, arXiv.org, number 2203.14751, Mar.
• Mike Ludkovski, 2022, "Regression Monte Carlo for Impulse Control," Papers, arXiv.org, number 2203.06539, Mar.
• Lotfi Boudabsa & Damir Filipović, 2022, "Ensemble learning for portfolio valuation and risk management," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 22-30, Apr.
• Vasarhelyi, Orsolya & Brooke, Siân, 2022, "Computing Gender," SocArXiv, Center for Open Science, number admcs, Apr, DOI: 10.31219/osf.io/admcs.
• Nils Korber & Maximilian Rohrig & Andreas Ulbig, 2022, "A stakeholder-oriented multi-criteria optimization model for decentral multi-energy systems," Papers, arXiv.org, number 2204.06545, Apr.

Report NEP-CMP-2022-05-02

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: