Report NEP-CMP-2021-11-15

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:

• Rasolomanana, Onjaniaina Mianin'Harizo, 2021. "Ensemble Neural Network Using A Small Dataset For The Prediction Of Bankruptcy : Combining Numerical And Textual Data," Discussion paper series. A 361, Graduate School of Economics and Business Administration, Hokkaido University.
• Zhanxue Gong & Xiyuan Li & Jiawen Liu & Yeming Gong, 2019. "Machine learning in explaining nonprofit organizations’ participation : a driving factors analysis approach," Post-Print hal-02880932, HAL.
• Tesfatsion, Leigh, 2021. "Agent-Based Computational Economics: Overview and Brief History," ISU General Staff Papers 202111080800001125, Iowa State University, Department of Economics.
• Xiaofei Shi & Daran Xu & Zhanhao Zhang, 2021. "Deep Learning Algorithms for Hedging with Frictions," Papers 2111.01931, arXiv.org, revised Dec 2022.
• Hasanbasri, Ardina & Koolwal, Gayatri & Kilic, Talip & Moylan, Heather, 2021. "Multidimensionality of Land Ownership Among Men and Women in Sub-Saharan Africa," 2021 Conference, August 17-31, 2021, Virtual 315317, International Association of Agricultural Economists.
• Julie Lassébie & Luca Marcolin & Marieke Vandeweyer & Benjamin Vignal, 2021. "Speaking the same language: A machine learning approach to classify skills in Burning Glass Technologies data," OECD Social, Employment and Migration Working Papers 263, OECD Publishing.
• Jie Chen & Lingfei Li, 2021. "Data-driven Hedging of Stock Index Options via Deep Learning," Papers 2111.03477, arXiv.org.
• Yuga Iguchi & Riu Naito & Yusuke Okano & Akihiko Takahashi & Toshihiro Yamada, 2021. "Deep Asymptotic Expansion: Application to Financial Mathematics（forthcoming in proceedings of IEEE CSDE 2021)," CARF F-Series CARF-F-523, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
• Yuga Iguchi & Riu Naito & Yusuke Okano & Akihiko Takahashi & Toshihiro Yamada, 2021. "Deep Asymptotic Expansion: Application to Financial Mathematics," CIRJE F-Series CIRJE-F-1178, CIRJE, Faculty of Economics, University of Tokyo.
• Damian Kisiel & Denise Gorse, 2021. "A Meta-Method for Portfolio Management Using Machine Learning for Adaptive Strategy Selection," Papers 2111.05935, arXiv.org.
• Drechsler, Martin & Grimm, Volker, 2022. "Land-use hysteresis triggered by staggered payment schemes for more permanent biodiversity conservation," MPRA Paper 110361, University Library of Munich, Germany.
• Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021. "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series 205, University of Pavia, Department of Economics and Management.
• John M. Abowd & Joelle Abramowitz & Margaret C. Levenstein & Kristin McCue & Dhiren Patki & Trivellore Raghunathan & Ann M. Rodgers & Matthew D. Shapiro & Nada Wasi & Dawn Zinsser, 2021. "Finding Needles in Haystacks: Multiple-Imputation Record Linkage Using Machine Learning," Working Papers 21-35, Center for Economic Studies, U.S. Census Bureau.
• Gillmann, Niels & Kim, Alisa, 2021. "Quantification of Economic Uncertainty: a deep learning approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics 242421, Verein für Socialpolitik / German Economic Association.
• Kajal Lahiri & Cheng Yang, 2021. "Boosting Tax Revenues with Mixed-Frequency Data in the Aftermath of Covid-19: The Case of New York," CESifo Working Paper Series 9365, CESifo.
• Skarda, Ieva & Asaria, Miqdad & Cookson, Richard, 2021. "LifeSim: a lifecourse dynamic microsimulation model of the millennium birth cohort in England," LSE Research Online Documents on Economics 112493, London School of Economics and Political Science, LSE Library.
• Christian Bayer & Chiheb Ben Hammouda & Ra'ul Tempone, 2021. "Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing," Papers 2111.01874, arXiv.org, revised Jun 2022.
• Simerjot Kaur & Ivan Brugere & Andrea Stefanucci & Armineh Nourbakhsh & Sameena Shah & Manuela Veloso, 2021. "Parameterized Explanations for Investor / Company Matching," Papers 2111.01911, arXiv.org.
• Yong Cai & Santiago Camara & Nicholas Capel, 2021. "It's not always about the money, sometimes it's about sending a message: Evidence of Informational Content in Monetary Policy Announcements," Papers 2111.06365, arXiv.org.
• RIGHI Riccardo & LOPEZ COBO Montserrat & SAMOILI Sofia & CARDONA Melisande & VAZQUEZ-PRADA BAILLET Miguel & DE PRATO Giuditta, 2021. "EU in the global Artificial Intelligence landscape," JRC Research Reports JRC125613, Joint Research Centre.
• Sofia Samoili & Montserrat Lopez Cobo & Blagoj Delipetrev & Fernando Martinez-Plumed & Emilia Gomez & Giuditta De Prato, 2021. "AI Watch. Defining Artificial Intelligence 2.0. Towards an operational definition and taxonomy of AI for the AI landscape," JRC Research Reports JRC126426, Joint Research Centre.