Report NEP-BIG-2023-12-11

This is the archive for NEP-BIG, a report on new working papers in the area of Big Data. Tom Coupé issued this report. It is usually issued weekly.

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Other reports in NEP-BIG

The following items were announced in this report:

• Khaoula Naili, 2023. "Critical AI Challenges in Legal Practice: An application to French Administrative Decisions," Working Papers 2023-06, CRESE.
• Abdel-Karim, Benjamin M. & Benlian, Alexander & Hinz, Oliver, 2023. "The Predictive Value of Data from Virtual Investment Communities," Publications of Darmstadt Technical University, Institute for Business Studies (BWL) 141359, Darmstadt Technical University, Department of Business Administration, Economics and Law, Institute for Business Studies (BWL).
• Chunyang Huang & Shaoliang Zhang, 2023. "Explainable artificial intelligence model for identifying Market Value in Professional Soccer Players," Papers 2311.04599, arXiv.org, revised Nov 2023.
• Nabeel, Rao, 2023. "A Neural Network Classifies Traumatic Brain Injury Outcomes: Glasgow Coma Triples Are Needed," OSF Preprints a472n, Center for Open Science.
• Item repec:idq:ictduk:18184 is not listed on IDEAS anymore

• Hannes Ullrich & Michael Allan Ribers, 2023. "Machine predictions and human decisions with variation in payoffs and skill: the case of antibiotic prescribing," Berlin School of Economics Discussion Papers 0027, Berlin School of Economics.
• Koti S. Jaddu & Paul A. Bilokon, 2023. "Combining Deep Learning on Order Books with Reinforcement Learning for Profitable Trading," Papers 2311.02088, arXiv.org.
• Reza Yarbakhsh & Mahdieh Soleymani Baghshah & Hamidreza Karimaghaie, 2023. "Predicting risk/reward ratio in financial markets for asset management using machine learning," Papers 2311.09148, arXiv.org.
• Chen, Ying & Grith, Maria & Lai, Hannah L. H., 2023. "Neural Tangent Kernel in Implied Volatility Forecasting: A Nonlinear Functional Autoregression Approach," MPRA Paper 119022, University Library of Munich, Germany.
• Siu Hin Tang & Mathieu Rosenbaum & Chao Zhou, 2023. "Forecasting Volatility with Machine Learning and Rough Volatility: Example from the Crypto-Winter," Papers 2311.04727, arXiv.org, revised Feb 2024.
• Christopher Mulenga & Joseph Phiri, 2023. "An Investigation into the use of artificial intelligence in property valuations in Zambia," AfRES afres2023-024, African Real Estate Society (AfRES).
• Alistair Macaulay & Wenting Song, 2022. "Narrative-Driven Fluctuations in Sentiment: Evidence Linking Traditional and Social Media," Economics Series Working Papers 973, University of Oxford, Department of Economics.
• Quan, Yutong & Wu, Xintong & Deng, Wanlin & Zhang, Luyao, 2023. "Decoding Social Sentiment in DAO: A Comparative Analysis of Blockchain Governance Communities," OSF Preprints bq6tu, Center for Open Science.
• Florian Englmaier & Andreas Roider & Lars Schlereth & Steffen Sebastian, 2023. "Round-Number Effects in Real Estate Prices: Evidence from Germany," CESifo Working Paper Series 10746, CESifo.
• Mathias Kraus & Julia Bingler & Markus Leippold & Tobias Schimanski & Chiara Colesanti Senni & Dominik Stammbach & Saeid Vaghefi & Nicolas Webersinke, 2023. "Enhancing Large Language Models with Climate Resources," Swiss Finance Institute Research Paper Series 23-99, Swiss Finance Institute.
• Fanyu Zhao, 2023. "Portfolio Construction using Black-Litterman Model and Factors," Papers 2311.04475, arXiv.org.
• George Athanasopoulos & Rob J Hyndman & Raffaele Mattera, 2023. "Improving out-of-sample Forecasts of Stock Price Indexes with Forecast Reconciliation and Clustering," Monash Econometrics and Business Statistics Working Papers 17/23, Monash University, Department of Econometrics and Business Statistics.
• Eric Fischer & Rebecca McCaughrin & Saketh Prazad & Mark Vandergon, 2023. "Fed Transparency and Policy Expectation Errors: A Text Analysis Approach," Staff Reports 1081, Federal Reserve Bank of New York.
• Yasuhiro Nakayama & Tomochika Sawaki, 2023. "Causal Inference on Investment Constraints and Non-stationarity in Dynamic Portfolio Optimization through Reinforcement Learning," Papers 2311.04946, arXiv.org.
• Breithaupt, Patrick & Hottenrott, Hanna & Rammer, Christian & Römer, Konstantin, 2023. "Mapping employee mobility and employer networks using professional network data," ZEW Discussion Papers 23-041, ZEW - Leibniz Centre for European Economic Research.