IDEAS home Printed from https://ideas.repec.org/f/pku776.html

Marcell Tamás Kurbucz
(Marcell Tamas Kurbucz)

Personal Details

First Name:Marcell
Middle Name:
Last Name:Kurbucz
Suffix:
RePEc Short-ID:pku776
[This author has chosen not to make the email address public]

Affiliation

Institute for Global Prosperity
Bartlett Faculty of the Built Environment
University College London (UCL)

London, United Kingdom
https://www.ucl.ac.uk/bartlett/global-prosperity
RePEc:edi:igucluk (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Marcell T. Kurbucz, 2026. "Identification of Latent Group Effects under Conditional Calibration," Papers 2604.08798, arXiv.org.
  2. Marcell T. Kurbucz & Betsab'e P'erez Garrido & Antal Jakov'ac, 2025. "Eigenvalue-Based Randomness Test for Residual Diagnostics in Panel Data Models," Papers 2504.05297, arXiv.org.
  3. Marcell T. Kurbucz & Nikolaos Tzivanakis & Nilufer Sari Aslam & Adam M. Sykulski, 2025. "SplitWise Regression: Stepwise Modeling with Adaptive Dummy Encoding," Papers 2505.15423, arXiv.org.
  4. Kurbucz, Marcell Tamas & Andree, Bo Pieter Johannes, 2025. "Building and Managing Local Databases from Google Earth Engine with the geeLite R Package," Policy Research Working Paper Series 11115, The World Bank.
  5. Andr'as Telcs & Marcell T. Kurbucz & Antal Jakov'ac, 2024. "Unified Causality Analysis Based on the Degrees of Freedom," Papers 2410.19469, arXiv.org.
  6. Marcell T. Kurbucz & P'eter P'osfay & Antal Jakov'ac, 2023. "Predicting the Price Movement of Cryptocurrencies Using Linear Law-based Transformation," Papers 2305.04884, arXiv.org.
  7. Marcell T. Kurbucz & P'eter P'osfay & Antal Jakov'ac, 2022. "Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices," Papers 2201.09790, arXiv.org.

Articles

  1. Kosztyán, Zsolt Tibor & Király, Ferenc & Kurbucz, Marcell Tamás, 2024. "Európai cégek tulajdonosi szerkezetének dinamikus hálózatelemzése [Investigating the ownership structure of European companies using dynamic network analysis methods]," Közgazdasági Szemle (Economic Review - monthly of the Hungarian Academy of Sciences), Közgazdasági Szemle Alapítvány (Economic Review Foundation), vol. 0(1), pages 57-85.
  2. Zsolt T. Kosztyán & Eszter Bogdány & István Szalkai & Marcell T. Kurbucz, 2022. "Impacts of synergies on software project scheduling," Annals of Operations Research, Springer, vol. 312(2), pages 883-908, May.
  3. Marcell Tamás Kurbucz & Attila Imre Katona & Zoltán Lantos & Zsolt Tibor Kosztyán, 2021. "The Role of Societal Aspects in the Formation of Official COVID-19 Reports: A Data-Driven Analysis," IJERPH, MDPI, vol. 18(4), pages 1-15, February.
  4. Kurbucz, Marcell Tamás, 2019. "Predicting the price of Bitcoin by the most frequent edges of its transaction network," Economics Letters, Elsevier, vol. 184(C).

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Working papers

  1. Marcell T. Kurbucz & P'eter P'osfay & Antal Jakov'ac, 2022. "Linear Laws of Markov Chains with an Application for Anomaly Detection in Bitcoin Prices," Papers 2201.09790, arXiv.org.

    Cited by:

    1. Fantazzini, Dean, 2025. "Detecting Stablecoin Failure with Simple Thresholds and Panel Binary Models: The Pivotal Role of Lagged Market Capitalization and Volatility," MPRA Paper 126906, University Library of Munich, Germany.

Articles

  1. Zsolt T. Kosztyán & Eszter Bogdány & István Szalkai & Marcell T. Kurbucz, 2022. "Impacts of synergies on software project scheduling," Annals of Operations Research, Springer, vol. 312(2), pages 883-908, May.

    Cited by:

    1. Hongbo Li & Hanyu Zhu & Linwen Zheng & Fang Xie, 2024. "Software project scheduling under activity duration uncertainty," Annals of Operations Research, Springer, vol. 338(1), pages 477-512, July.

  2. Marcell Tamás Kurbucz & Attila Imre Katona & Zoltán Lantos & Zsolt Tibor Kosztyán, 2021. "The Role of Societal Aspects in the Formation of Official COVID-19 Reports: A Data-Driven Analysis," IJERPH, MDPI, vol. 18(4), pages 1-15, February.

    Cited by:

    1. Edit Kővári & Katalin Formádi & Zsuzsanna Banász, 2023. "The Green Attitude of Four European Capitals of Culture’s Youth," Sustainability, MDPI, vol. 15(10), pages 1-19, May.
    2. Wu, Yan & Yang, Yong & Mickiewicz, Tomasz, 2023. "Corruption, the digital sectors, and the profitability of foreign subsidiaries in emerging markets," Journal of Business Research, Elsevier, vol. 161(C).

  3. Kurbucz, Marcell Tamás, 2019. "Predicting the price of Bitcoin by the most frequent edges of its transaction network," Economics Letters, Elsevier, vol. 184(C).

    Cited by:

    1. Yang Zhou & Chi Xie & Gang-Jin Wang & Jue Gong & You Zhu, 2025. "Forecasting cryptocurrency volatility: a novel framework based on the evolving multiscale graph neural network," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-52, December.
    2. Yuanyuan (Catherine) Chen, 2021. "Empirical analysis of bitcoin price," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(4), pages 692-715, October.
    3. Ren, Yi-Shuai & Ma, Chao-Qun & Kong, Xiao-Lin & Baltas, Konstantinos & Zureigat, Qasim, 2022. "Past, present, and future of the application of machine learning in cryptocurrency research," Research in International Business and Finance, Elsevier, vol. 63(C).
    4. Marthinsen, John E. & Gordon, Steven R., 2022. "The price and cost of bitcoin," The Quarterly Review of Economics and Finance, Elsevier, vol. 85(C), pages 280-288.
    5. Navid Parvini & Davood Ahmadian & Luca Vincenzo Ballestra, 2025. "Forecasting Cryptocurrency Prices Using Support Vector Regression Enhanced by Particle Swarm Optimization," Computational Economics, Springer;Society for Computational Economics, vol. 66(4), pages 3167-3196, October.
    6. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & David Martinez-Rego & Fan Wu & Lingbo Li, 2022. "Cryptocurrency trading: a comprehensive survey," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-59, December.
    7. John E. Marthinsen & Steven R. Gordon, 2022. "The Price and Cost of Bitcoin," Papers 2204.13102, arXiv.org.
    8. Fan Fang & Carmine Ventre & Michail Basios & Leslie Kanthan & Lingbo Li & David Martinez-Regoband & Fan Wu, 2020. "Cryptocurrency Trading: A Comprehensive Survey," Papers 2003.11352, arXiv.org, revised Jan 2022.
    9. Ling Cheng & Qian Shao & Fengzhu Zeng & Feida Zhu, 2024. "A Full-History Network Dataset for BTC Asset Decentralization Profiling," Papers 2411.13603, arXiv.org.
    10. Ji, Qiang & Bouri, Elie & Kristoufek, Ladislav & Lucey, Brian, 2021. "Realised volatility connectedness among Bitcoin exchange markets," Finance Research Letters, Elsevier, vol. 38(C).
    11. Dag, Ali & Dag, Asli Z. & Asilkalkan, Abdullah & Simsek, Serhat & Delen, Dursun, 2023. "A Tree Augmented Naïve Bayes-based methodology for classifying cryptocurrency trends," Journal of Business Research, Elsevier, vol. 156(C).
    12. Rico-Peña, Juan Jesús & Arguedas-Sanz, Raquel & López-Martin, Carmen, 2023. "Models used to characterise blockchain features. A systematic literature review and bibliometric analysis," Technovation, Elsevier, vol. 123(C).
    13. Sudersan Behera & Sarat Chandra Nayak & A. V. S. Pavan Kumar, 2024. "Evaluating the Performance of Metaheuristic Based Artificial Neural Networks for Cryptocurrency Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1219-1258, August.
    14. Yufang Wang & Haiyan Wang, 2020. "Using Networks and Partial Differential Equations to Predict Bitcoin Price," Papers 2001.03099, arXiv.org.
    15. Sasan Barak & Navid Parvini, 2023. "Transfer‐entropy‐based dynamic feature selection for evaluating Bitcoin price drivers," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1695-1726, December.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 7 papers announced in NEP. These are the fields, ordered by number of announcements, along with their dates. If the author is listed in the directory of specialists for this field, a link is also provided.
  1. NEP-ECM: Econometrics (4) 2024-12-09 2025-05-12 2025-08-11 2026-04-20. Author is listed
  2. NEP-PAY: Payment Systems and Financial Technology (2) 2022-03-07 2023-06-12. Author is listed
  3. NEP-BIG: Big Data (1) 2023-06-12
  4. NEP-CMP: Computational Economics (1) 2023-06-12
  5. NEP-CWA: Central and Western Asia (1) 2022-03-07
  6. NEP-DCM: Discrete Choice Models (1) 2024-12-09
  7. NEP-GEO: Economic Geography (1) 2025-05-19
  8. NEP-INV: Investment (1) 2025-08-11
  9. NEP-MAC: Macroeconomics (1) 2025-08-11

Corrections

All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. For general information on how to correct material on RePEc, see these instructions.

To update listings or check citations waiting for approval, Marcell Kurbucz
(Marcell Tamas Kurbucz) should log into the RePEc Author Service.

To make corrections to the bibliographic information of a particular item, find the technical contact on the abstract page of that item. There, details are also given on how to add or correct references and citations.

To link different versions of the same work, where versions have a different title, use this form. Note that if the versions have a very similar title and are in the author's profile, the links will usually be created automatically.

Please note that most corrections can take a couple of weeks to filter through the various RePEc services.

IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.