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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. Stanley Miles issued this report. It is usually issued weekly.Subscribe to this report: email, RSS, or Mastodon, or Bluesky.
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, Graduate School of Economics and Business Administration, Hokkaido University, number 361, Oct.
- Zhanxue Gong & Xiyuan Li & Jiawen Liu & Yeming Gong, 2019, "Machine learning in explaining nonprofit organizations’ participation : a driving factors analysis approach," Post-Print, HAL, number hal-02880932, Dec, DOI: 10.1007/s00521-018-3858-6.
- Tesfatsion, Leigh, 2021, "Agent-Based Computational Economics: Overview and Brief History," ISU General Staff Papers, Iowa State University, Department of Economics, number 202111080800001125, Nov.
- Xiaofei Shi & Daran Xu & Zhanhao Zhang, 2021, "Deep Learning Algorithms for Hedging with Frictions," Papers, arXiv.org, number 2111.01931, Nov, 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, International Association of Agricultural Economists, number 315317, Aug, DOI: 10.22004/ag.econ.315317.
- 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, OECD Publishing, number 263, Nov, DOI: 10.1787/adb03746-en.
- Jie Chen & Lingfei Li, 2021, "Data-driven Hedging of Stock Index Options via Deep Learning," Papers, arXiv.org, number 2111.03477, Nov.
- 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, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo, number CARF-F-523, Nov.
- Yuga Iguchi & Riu Naito & Yusuke Okano & Akihiko Takahashi & Toshihiro Yamada, 2021, "Deep Asymptotic Expansion: Application to Financial Mathematics," CIRJE F-Series, CIRJE, Faculty of Economics, University of Tokyo, number CIRJE-F-1178, Nov.
- Damian Kisiel & Denise Gorse, 2021, "A Meta-Method for Portfolio Management Using Machine Learning for Adaptive Strategy Selection," Papers, arXiv.org, number 2111.05935, Nov.
- Drechsler, Martin & Grimm, Volker, 2022, "Land-use hysteresis triggered by staggered payment schemes for more permanent biodiversity conservation," MPRA Paper, University Library of Munich, Germany, number 110361, Oct.
- Alessandro Bitetto & Stefano Filomeni & Michele Modina, 2021, "Understanding corporate default using Random Forest: The role of accounting and market information," DEM Working Papers Series, University of Pavia, Department of Economics and Management, number 205, Oct.
- 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, Center for Economic Studies, U.S. Census Bureau, number 21-35, Nov.
- Gillmann, Niels & Kim, Alisa, 2021, "Quantification of Economic Uncertainty: a deep learning approach," VfS Annual Conference 2021 (Virtual Conference): Climate Economics, Verein für Socialpolitik / German Economic Association, number 242421.
- 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, CESifo, number 9365.
- 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, London School of Economics and Political Science, LSE Library, number 112493, Oct.
- 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, arXiv.org, number 2111.01874, Nov, revised Jun 2022.
- Simerjot Kaur & Ivan Brugere & Andrea Stefanucci & Armineh Nourbakhsh & Sameena Shah & Manuela Veloso, 2021, "Parameterized Explanations for Investor / Company Matching," Papers, arXiv.org, number 2111.01911, Oct.
- 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, arXiv.org, number 2111.06365, Nov.
- 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, Joint Research Centre, number JRC125613, Nov.
- 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, Joint Research Centre, number JRC126426, Oct.