Report NEP-CMP-2021-04-19

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:

• Peter B. Dixon & Maureen T. Rimmer, 2021, "Who will pay for improved health standards in U.S. meat-processing plants? Simulation results from the USAGE model," Centre of Policy Studies/IMPACT Centre Working Papers, Victoria University, Centre of Policy Studies/IMPACT Centre, number g-314, Mar.
• Przemys{l}aw Biecek & Marcin Chlebus & Janusz Gajda & Alicja Gosiewska & Anna Kozak & Dominik Ogonowski & Jakub Sztachelski & Piotr Wojewnik, 2021, "Enabling Machine Learning Algorithms for Credit Scoring -- Explainable Artificial Intelligence (XAI) methods for clear understanding complex predictive models," Papers, arXiv.org, number 2104.06735, Apr.
• Ling Qi & Matloob Khushi & Josiah Poon, 2021, "Event-Driven LSTM For Forex Price Prediction," Papers, arXiv.org, number 2102.01499, Jan.
• Jia Wang & Tong Sun & Benyuan Liu & Yu Cao & Degang Wang, 2021, "Financial Markets Prediction with Deep Learning," Papers, arXiv.org, number 2104.05413, Apr.
• Geminiani, Elena & Marra, Giampiero & Moustaki, Irini, 2021, "Single and multiple-group penalized factor analysis: a trust-region algorithm approach with integrated automatic multiple tuning parameter selection," LSE Research Online Documents on Economics, London School of Economics and Political Science, LSE Library, number 108873, Mar.
• Matias Selser & Javier Kreiner & Manuel Maurette, 2021, "Optimal Market Making by Reinforcement Learning," Papers, arXiv.org, number 2104.04036, Apr.
• Nazish Ashfaq & Zubair Nawaz & Muhammad Ilyas, 2021, "A comparative study of Different Machine Learning Regressors For Stock Market Prediction," Papers, arXiv.org, number 2104.07469, Apr.
• Christiane B. Haubitz & Cedric A. Lehmann & Andreas Fügener & Ulrich W. Thonemann, 2021, "The Risk of Algorithm Transparency: How Algorithm Complexity Drives the Effects on Use of Advice," ECONtribute Discussion Papers Series, University of Bonn and University of Cologne, Germany, number 078, Apr.
• Denuit, Michel & Charpentier, Arthur & Trufin, Julien, 2021, "Autocalibration and Tweedie-dominance for insurance pricing with machine learning," LIDAM Discussion Papers ISBA, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), number 2021013, Mar.
• Christian M. Dahl & Emil N. S{o}rensen, 2021, "Time Series (re)sampling using Generative Adversarial Networks," Papers, arXiv.org, number 2102.00208, Jan.
• Zheng Gong & Carmine Ventre & John O'Hara, 2021, "The Efficient Hedging Frontier with Deep Neural Networks," Papers, arXiv.org, number 2104.05280, Apr.
• Cristina Cirillo & Lucia Imperioli & Marco Manzo, 2021, "The Value Added Tax Simulation Model: VATSIM-DF (II)," Working Papers, Ministry of Economy and Finance, Department of Finance, number wp2021-12, Mar.
• Bruno Scalzo & Alvaro Arroyo & Ljubisa Stankovic & Danilo P. Mandic, 2021, "Nonstationary Portfolios: Diversification in the Spectral Domain," Papers, arXiv.org, number 2102.00477, Jan.
• Benetos, Emmanouil & Ragano, Alessandro & Sgroi, Daniel & Tuckwell, Anthony, 2021, "Measuring National Life Satisfaction with Music," IZA Discussion Papers, Institute of Labor Economics (IZA), number 14258, Apr.
• Shalini Sharma & Víctor Elvira & Emilie Chouzenoux & Angshul Majumdar, 2021, "Recurrent Dictionary Learning for State-Space Models with an Application in Stock Forecasting," Post-Print, HAL, number hal-03184841.
• Hainaut, Donatien & Trufin, Julien & Denuit, Michel, 2021, "Response versus gradient boosting trees, GLMs and neural networks under Tweedie loss and log-link," LIDAM Discussion Papers ISBA, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA), number 2021012, Jan.
• Samuel N. Cohen & Derek Snow & Lukasz Szpruch, 2021, "Black-box model risk in finance," Papers, arXiv.org, number 2102.04757, Feb.
• Blanka Horvath & Josef Teichmann & Zan Zuric, 2021, "Deep Hedging under Rough Volatility," Papers, arXiv.org, number 2102.01962, Feb.
• Marco Due~nas & Federico Nutarelli & V'ictor Ortiz & Massimo Riccaboni & Francesco Serti, 2021, "Assessing the Heterogeneous Impact of Economy-Wide Shocks: A Machine Learning Approach Applied to Colombian Firms," Papers, arXiv.org, number 2104.04570, Apr, revised Nov 2024.
• Jaydip Sen & Abhishek Dutta & Sidra Mehtab, 2021, "Profitability Analysis in Stock Investment Using an LSTM-Based Deep Learning Model," Papers, arXiv.org, number 2104.06259, Apr.
• Cristina Fuentes-Albero & John M. Roberts, 2021, "Inflation Thresholds and Policy-Rule Inertia: Some Simulation Results," FEDS Notes, Board of Governors of the Federal Reserve System (U.S.), number 2021-04-12, Apr, DOI: 10.17016/2380-7172.2899.
• Jean Jacques Ohana & Eric Benhamou & David Saltiel & Beatrice Guez, 2021, "Is the Covid equity bubble rational? A machine learning answer," Working Papers, HAL, number hal-03189799, Apr.
• Daniel Straulino & Juan C. Saldarriaga & Jairo A. G'omez & Juan C. Duque & Neave O'Clery, 2021, "Uncovering commercial activity in informal cities," Papers, arXiv.org, number 2104.04545, Apr.
• Müller, Henrik & Rieger, Jonas & Hornig, Nico, 2021, ""We're rolling". Our Uncertainty Perception Indicator (UPI) in Q4 2020: introducing RollingLDA, a new method for the measurement of evolving economic narratives," DoCMA Working Papers, TU Dortmund University, Dortmund Center for Data-based Media Analysis (DoCMA), number 6, DOI: 10.17877/DE290R-21974.
• Fabrizio Lillo & Giulia Livieri & Stefano Marmi & Anton Solomko & Sandro Vaienti, 2021, "Analysis of bank leverage via dynamical systems and deep neural networks," Papers, arXiv.org, number 2104.04960, Apr.
• Augusto Cerqua & Roberta Di Stefano & Marco Letta & Sara Miccoli, 2020, "Local mortality estimates during the COVID-19 pandemic in Italy," Discussion Paper series in Regional Science & Economic Geography, Gran Sasso Science Institute, Social Sciences, number 2020-06, Oct, revised Oct 2020.

Report NEP-CMP-2021-04-19

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: