Report NEP-CMP-2021-04-05

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:

• Ariel Neufeld & Julian Sester, 2021. "A deep learning approach to data-driven model-free pricing and to martingale optimal transport," Papers 2103.11435, arXiv.org, revised Dec 2022.
• Huanming Zhang & Zhengyong Jiang & Jionglong Su, 2021. "A Deep Deterministic Policy Gradient-based Strategy for Stocks Portfolio Management," Papers 2103.11455, arXiv.org.
• Kollár, Aladár, 2021. "Betting models using AI: a review on ANN, SVM, and Markov chain," MPRA Paper 106821, University Library of Munich, Germany.
• Mohammadreza Ghanbari & Mahdi Goldani, 2021. "Support Vector Regression Parameters Optimization using Golden Sine Algorithm and its application in stock market," Papers 2103.11459, arXiv.org.
• Yiyan Huang & Cheuk Hang Leung & Qi Wu & Xing Yan, 2021. "Robust Orthogonal Machine Learning of Treatment Effects," Papers 2103.11869, arXiv.org, revised Dec 2022.
• Steve J. Bickley & Ho Fai Chan & Sascha L. Schmidt & Benno Torgler, 2021. "Quantum-Sapiens: The Quantum Bases for Human Expertise, Knowledge, and Problem-Solving (Extended Version with Applications)," CREMA Working Paper Series 2021-14, Center for Research in Economics, Management and the Arts (CREMA).
• Tomasz Antczak & Bartosz Skorupa & Mikolaj Szurlej & Rafal Weron & Jacek Zabawa, 2021. "Simulation modeling of epidemic risk in supermarkets: Investigating the impact of social distancing and checkout zone design," WORking papers in Management Science (WORMS) WORMS/21/05, Department of Operations Research and Business Intelligence, Wroclaw University of Science and Technology.
• Aleksy Klimowicz & Krzysztof Spirzewski, 2021. "Concept of peer-to-peer lending and application of machine learning in credit scoring," Working Papers 2021-04, Faculty of Economic Sciences, University of Warsaw.
• Karush Suri & Xiao Qi Shi & Konstantinos Plataniotis & Yuri Lawryshyn, 2021. "TradeR: Practical Deep Hierarchical Reinforcement Learning for Trade Execution," Papers 2104.00620, arXiv.org.
• Mukul Jaggi & Priyanka Mandal & Shreya Narang & Usman Naseem & Matloob Khushi, 2021. "Text Mining of Stocktwits Data for Predicting Stock Prices," Papers 2103.16388, arXiv.org.
• Hamid Bekamiri & Daniel S. Hain & Roman Jurowetzki, 2021. "PatentSBERTa: A Deep NLP based Hybrid Model for Patent Distance and Classification using Augmented SBERT," Papers 2103.11933, arXiv.org, revised Oct 2021.
• Hans Genberg & Özer Karagedikli, 2021. "Machine Learning and Central Banks: Ready for Prime Time?," Working Papers wp43, South East Asian Central Banks (SEACEN) Research and Training Centre.
• Jay Cao & Jacky Chen & John Hull & Zissis Poulos, 2021. "Deep Learning for Exotic Option Valuation," Papers 2103.12551, arXiv.org, revised Sep 2021.
• Olivier Durand-Lasserve, 2021. "Policies to Nationalize the Private Sector Labor Force in a Matching Model with Public Jobs and Quotas," Discussion Papers ks--2021-dp05, King Abdullah Petroleum Studies and Research Center.
• Walid Matar, 2021. "Long-run Effects of Real-time Electricity Pricing in the Saudi Power Sector," Discussion Papers ks--2021-dp03, King Abdullah Petroleum Studies and Research Center.
• Q. Wang & Y. Zhou & J. Shen, 2021. "Intraday trading strategy based on time series and machine learning for Chinese stock market," Papers 2103.13507, arXiv.org.
• Hannes Mueller & Christopher Rauh, 2021. "The Hard Problem of Prediction for Conflict Prevention," Working Papers 1244, Barcelona School of Economics.
• Seema Jayachandran & Monica Biradavolu & Jan Cooper, 2021. "Using Machine Learning and Qualitative Interviews to Design a Five-Question Women's Agency Index," NBER Working Papers 28626, National Bureau of Economic Research, Inc.
• Dave Cliff, 2021. "Parameterised-Response Zero-Intelligence Traders," Papers 2103.11341, arXiv.org, revised Apr 2023.
• Artur Sokolovsky & Luca Arnaboldi & Jaume Bacardit & Thomas Gross, 2021. "Volume-Centred Range Bars: Novel Interpretable Representation of Financial Markets Designed for Machine Learning Applications," Papers 2103.12419, arXiv.org, revised May 2022.
• Jay Cao & Jacky Chen & John Hull & Zissis Poulos, 2021. "Deep Hedging of Derivatives Using Reinforcement Learning," Papers 2103.16409, arXiv.org.
• Jon Ellingsen & Vegard H. Larsen & Leif Anders Thorsrud, 2020. "News media vs. FRED-MD for macroeconomic forecasting," Working Paper 2020/14, Norges Bank.
• Szekeres, Szabolcs, 2021. "Should CBA use descriptive or prescriptive discount rates? It should use both!," MPRA Paper 106029, University Library of Munich, Germany.
• Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can Machine Learning Help to Select Portfolios of Mutual Funds?," Working Papers 1245, Barcelona School of Economics.
• Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can machine learning help to select portfolios of mutual funds?," Economics Working Papers 1772, Department of Economics and Business, Universitat Pompeu Fabra.
• Hanjo Odendaal, 2021. "A machine learning approach to domain specific dictionary generation. An economic time series framework," Working Papers 06/2021, Stellenbosch University, Department of Economics.
• Shan Huang & Michael Allan Ribers & Hannes Ullrich, 2021. "The Value of Data for Prediction Policy Problems: Evidence from Antibiotic Prescribing," Discussion Papers of DIW Berlin 1939, DIW Berlin, German Institute for Economic Research.
• Jørgen Vitting Andersen & Andrzej Nowak, 2020. "Symmetry and financial Markets," Post-Print halshs-03048686, HAL.