Report NEP-BIG-2021-05-24

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:

• Ghysels, Eric & Babii, Andrii & Chen, Xi & Kumar, Rohit, 2020, "Binary Choice with Asymmetric Loss in a Data-Rich Environment: Theory and an Application to Racial Justice," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15418, Oct.
• Gambacorta, Leonardo & Huang, Yiping & Li, Zhenhua & Qiu, Han & Chen, Shu, 2020, "Data vs collateral," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15262, Sep.
• Gobillon, Laurent & Combes, Pierre-Philippe & Zylberberg, Yanos, 2020, "Urban economics in a historical perspective: Recovering data with machine learning," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15308, Sep.
• Philippe Baumard, 2019, "Quand l’intelligence artificielle théorisera les organisations," Post-Print, HAL, number hal-03218196, Nov, DOI: 10.3166/rfg.2020.00409.
• Taylor, Mark & Filippou, Ilias & Rapach, David & Zhou, Guofu, 2020, "Exchange Rate Prediction with Machine Learning and a Smart Carry Trade Portfolio," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15305, Sep.
• Petra Posedel v{S}imovi'c & Davor Horvatic & Edward W. Sun, 2021, "Classifying variety of customer's online engagement for churn prediction with mixed-penalty logistic regression," Papers, arXiv.org, number 2105.07671, May, revised Jul 2021.
• Rhodes, Andrew & Johnson, Justin & Wildenbeest, Matthijs, 2020, "Platform Design When Sellers Use Pricing Algorithms," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15504, Nov.
• Dave Cliff, 2021, "BBE: Simulating the Microstructural Dynamics of an In-Play Betting Exchange via Agent-Based Modelling," Papers, arXiv.org, number 2105.08310, May.
• M. Elshendy & A. Fronzetti Colladon & E. Battistoni & P. A. Gloor, 2021, "Using four different online media sources to forecast the crude oil price," Papers, arXiv.org, number 2105.09154, May.
• Hansen, Stephen & Davis, Steven & Seminario-Amez, Cristhian, 2020, "Firm-level Risk Exposures and Stock Returns in the Wake of COVID-19," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15314, Sep.
• Mohsen Asgari & Hossein Khasteh, 2021, "Profitable Strategy Design for Trades on Cryptocurrency Markets with Machine Learning Techniques," Papers, arXiv.org, number 2105.06827, May, revised Jun 2022.
• Goutham Gopalakrishna, 2021, "ALIENs and Continuous Time Economies," Swiss Finance Institute Research Paper Series, Swiss Finance Institute, number 21-34, May.
• Haoran Wang & Shi Yu, 2021, "Robo-Advising: Enhancing Investment with Inverse Optimization and Deep Reinforcement Learning," Papers, arXiv.org, number 2105.09264, May.
• Assad, Stephanie & Calvano, Emilio & Calzolari, Giacomo & Clark, Robert & Ershov, Daniel & Johnson, Justin & Pastorello, Sergio & Rhodes, Andrew & XU, Lei & Wildenbeest, Matthijs & Denicolò, Vincenzo, 2021, "Autonomous algorithmic collusion: Economic research and policy implications," TSE Working Papers, Toulouse School of Economics (TSE), number 21-1210, Mar.
• Lechtenberg, Sandra & Hellingrath, Bernd, 2021, "Applications of artificial intelligence in supply chain management: Identification of main research fields and greatest industry interests," ERCIS Working Papers, University of Münster, European Research Center for Information Systems (ERCIS), number 37.
• Maliar, Serguei, 2020, "Deep Learning Classification: Modeling Discrete Labor Choice," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15346, Oct.
• Sean Cao & Wei Jiang & Junbo L. Wang & Baozhong Yang, 2021, "From Man vs. Machine to Man + Machine: The Art and AI of Stock Analyses," NBER Working Papers, National Bureau of Economic Research, Inc, number 28800, May.
• Hodler, Roland & Lechner, Michael & Raschky, Paul, 2020, "Reassessing the Resource Curse using Causal Machine Learning," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15272, Sep.
• Desmet, Klaus & Obradovich, Nick & Martín, Ignacio & Ortuño-Ortin, Ignacio & Awad, Edmond & Cebrían, Manuel & Cuevas Rumin, Ruben & Rahwan, Iyad & Cuevas Rumin, Angel, 2020, "Expanding the Measurement of Culture with a Sample of Two Billion Humans," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15315, Sep.
• A Fronzetti Colladon & B Guardabascio & R Innarella, 2021, "Using social network and semantic analysis to analyze online travel forums and forecast tourism demand," Papers, arXiv.org, number 2105.07727, May.
• Gries, Thomas & Naudé, Wim, 2021, "The Race of Man and Machine: Implications of Technology When Abilities and Demand Constraints Matter," IZA Discussion Papers, IZA Network @ LISER, number 14341, Apr.
• Köbis, Nils & Bonnefon, Jean-François & Rahwan, Iyad, 2021, "Bad machines corrupt good morals," TSE Working Papers, Toulouse School of Economics (TSE), number 21-1212, May.
• Adams-Prassl, Abigail & Balgova, Maria & Qian, Matthias, 2020, "Flexible Work Arrangements in Low Wage Jobs: Evidence from Job Vacancy Data," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15263, Sep.
• Valencia Caicedo, Felipe & Riano, Juan Felipe, 2020, "Collateral Damage: The Legacy of the Secret War in Laos," CEPR Discussion Papers, C.E.P.R. Discussion Papers, number 15349, Oct.
• Kang, J. & Reiner, D., 2021, "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Cambridge Working Papers in Economics, Faculty of Economics, University of Cambridge, number 2143, May.

Report NEP-BIG-2021-05-24

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: