Report NEP-BIG-2022-01-10

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

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

The following items were announced in this report:

• Chris Redl & Sandile Hlatshwayo, 2021, "Forecasting Social Unrest: A Machine Learning Approach," IMF Working Papers, International Monetary Fund, number 2021/263, Nov.
• Leogrande, Angelo & Costantiello, Alberto & Laureti, Lucio & Leogrande, Domenico, 2021, "The Determinants of Design Applications in Europe," MPRA Paper, University Library of Munich, Germany, number 110836, Nov.
• 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.
• 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.
• 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.
• 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.
• Adam N. Smith & Stephan Seiler & Ishant Aggarwal, 2021, "Optimal Price Targeting," CESifo Working Paper Series, CESifo, number 9439.
• 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.
• Louis Abraham, 2021, "Competition analysis on the over-the-counter credit default swap market," Working Papers, HAL, number hal-03454808, Nov.
• 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.
• Ben Hambly & Renyuan Xu & Huining Yang, 2021, "Recent Advances in Reinforcement Learning in Finance," Papers, arXiv.org, number 2112.04553, Dec, revised Feb 2023.
• 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.
• 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.
• Ake-Kob, Alin & Blazeviciene, Aurelija & Colonna, Liane & Cartolovni, Anto & Dantas, Carina & Fedosov, Anton & Florez-Revuelta, Francisco & Fosch-Villaronga, Eduard & He, Zhicheng & Klimczuk, Andrzej , 2021, "State of the art on ethical, legal, and social issues linked to audio- and video-based AAL solutions," EconStor Research Reports, ZBW - Leibniz Information Centre for Economics, number 248470, DOI: 10.13140/RG.2.2.28785.81764.
• 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.
• 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.
• Uta Pigorsch & Sebastian Schafer, 2021, "High-Dimensional Stock Portfolio Trading with Deep Reinforcement Learning," Papers, arXiv.org, number 2112.04755, Dec.
• Emmanuel Okamba, 2022, "Travailleur du savoir et risque d'intolérance sensorielle lié à l'interaction homme-machine intelligente," Working Papers, HAL, number hal-03453843, Jul.
• Timothy Holt & Mitsuru Igami & Simon Scheidegger, 2021, "Detecting Edgeworth Cycles," Cahiers de Recherches Economiques du Département d'économie, Université de Lausanne, Faculté des HEC, Département d’économie, number 21.16, Nov.
• Fabio Montobbio & Jacopo Staccioli & Maria Enrica Virgillito & Marco Vivarelli, 2021, "Labour-saving automation and occupational exposure: a text-similarity measure," DISCE - Working Papers del Dipartimento di Politica Economica, Università Cattolica del Sacro Cuore, Dipartimenti e Istituti di Scienze Economiche (DISCE), number dipe0021, Nov.
• Victor Chernozhukov & Carlos Cinelli & Whitney Newey & Amit Sharma & Vasilis Syrgkanis, 2021, "Long Story Short: Omitted Variable Bias in Causal Machine Learning," Papers, arXiv.org, number 2112.13398, Dec, revised May 2024.
• Néstor Duch-Brown & Estrella Gomez-Herrera & Frank Mueller-Langer & Songül Tolan, 2021, "Market power and artificial intelligence work on online labour markets," Bruegel Working Papers, Bruegel, number 46376, Dec.
• Lorenz, Max & Reinhard, Patrick & Spring, Thomas, 2021, "Digitalization in MedTech: Understanding the Impact on Total Knee Arthroplasty," Working Paper Series in Health Economics, Management and Policy, University of St.Gallen, School of Medicine, Chair of Health Economics, Policy and Management, number 2021-02.
• Konrad Menzel, 2021, "Structural Sieves," Papers, arXiv.org, number 2112.01377, Dec, revised Apr 2022.
• 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.
• Shwai He & Shi Gu, 2021, "Multi-modal Attention Network for Stock Movements Prediction," Papers, arXiv.org, number 2112.13593, Dec, revised Oct 2022.
• Jelnov, Artyom & Jelnov, Pavel, 2021, "Vaccination Policy and Trust," GLO Discussion Paper Series, Global Labor Organization (GLO), number 1003.
• Mehmet Canayaz & Ilja Kantorovitch & Roxana Mihet, 2021, "Privacy Laws and Value of Personal Data," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-92, Dec.

Report NEP-BIG-2022-01-10

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

Subscribe to this report: email, RSS, or Mastodon, or Bluesky.

Other reports in NEP-BIG

The following items were announced in this report: