IDEAS home Printed from https://ideas.repec.org/f/phu675.html
   My authors  Follow this author

Sullivan Hué
(Sullivan Hue)

Personal Details

First Name:Sullivan
Middle Name:
Last Name:Hue
Suffix:
RePEc Short-ID:phu675
[This author has chosen not to make the email address public]
https://sullivanhue.wixsite.com/professional

Affiliation

École d'Économie d'Aix-Marseille
Aix-Marseille Université

Aix-en-Provence/Marseille, France
http://www.amse-aixmarseille.fr/
RePEc:edi:amseafr (more details at EDIRC)

Research output

as
Jump to: Working papers Articles

Working papers

  1. Sullivan Hué & Emmanuel Flachaire & Sébastien Laurent & Ulrich Aiounou, 2024. "Treatment-effect estimation in high dimension: An inference-based approach," French Stata Users' Group Meetings 2024 18, Stata Users Group.
  2. Emmanuel Flachaire & Sullivan Hué & Sébastien Laurent & Gilles Hacheme, 2023. "Interpretable Machine Learning Using Partial Linear Models," Post-Print hal-04529011, HAL.
  3. Sullivan Hué, 2022. "GAM(L)A: An econometric model for interpretable machine learning," French Stata Users' Group Meetings 2022 19, Stata Users Group.
  4. Hué, Sullivan & Hurlin, Christophe & Pérignon, Christophe & Saurin, Sébastien, 2022. "Explainable Performance," HEC Research Papers Series 1463, HEC Paris.
  5. Elena Ivona Dumitrescu & Sullivan Hué & Christophe Hurlin & Sessi Tokpavi, 2022. "Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects," Post-Print hal-03331114, HAL.
  6. Elena Dumitrescu & Sullivan Hué & Christophe Hurlin & Sessi Tokpavi, 2021. "Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds," Working Papers hal-02507499, HAL.
  7. Sullivan HUE & Yannick LUCOTTE & Sessi TOKPAVI, 2018. "Measuring network systemic risk contributions: A leave-one-out approach," LEO Working Papers / DR LEO 2708, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.

Articles

  1. Emmanuel Flachaire & Sullivan Hué & Sébastien Laurent & Gilles Hacheme, 2024. "Interpretable Machine Learning Using Partial Linear Models," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 86(3), pages 519-540, June.
  2. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
  3. Hué, Sullivan & Lucotte, Yannick & Tokpavi, Sessi, 2019. "Measuring network systemic risk contributions: A leave-one-out approach," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 86-114.

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. Elena Ivona Dumitrescu & Sullivan Hué & Christophe Hurlin & Sessi Tokpavi, 2022. "Machine Learning for Credit Scoring: Improving Logistic Regression with Non Linear Decision Tree Effects," Post-Print hal-03331114, HAL.

    Cited by:

    1. Dangxing Chen & Weicheng Ye & Jiahui Ye, 2022. "Interpretable Selective Learning in Credit Risk," Papers 2209.10127, arXiv.org.
    2. Dangxing Chen & Luyao Zhang, 2023. "Monotonicity for AI ethics and society: An empirical study of the monotonic neural additive model in criminology, education, health care, and finance," Papers 2301.07060, arXiv.org.
    3. Ahmad El Majzoub & Fethi A. Rabhi & Walayat Hussain, 2023. "Evaluating interpretable machine learning predictions for cryptocurrencies," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 30(3), pages 137-149, July.
    4. Sun, Weixin & Zhang, Xuantao & Li, Minghao & Wang, Yong, 2023. "Interpretable high-stakes decision support system for credit default forecasting," Technological Forecasting and Social Change, Elsevier, vol. 196(C).
    5. Al-Amin Abba Dabo & Amin Hosseinian-Far, 2023. "An Integrated Methodology for Enhancing Reverse Logistics Flows and Networks in Industry 5.0," Logistics, MDPI, vol. 7(4), pages 1-26, December.
    6. Tigges, Maximilian & Mestwerdt, Sönke & Tschirner, Sebastian & Mauer, René, 2024. "Who gets the money? A qualitative analysis of fintech lending and credit scoring through the adoption of AI and alternative data," Technological Forecasting and Social Change, Elsevier, vol. 205(C).
    7. Li, Zhiyong & Li, Aimin & Bellotti, Anthony & Yao, Xiao, 2023. "The profitability of online loans: A competing risks analysis on default and prepayment," European Journal of Operational Research, Elsevier, vol. 306(2), pages 968-985.
    8. Kraus, Mathias & Tschernutter, Daniel & Weinzierl, Sven & Zschech, Patrick, 2024. "Interpretable generalized additive neural networks," European Journal of Operational Research, Elsevier, vol. 317(2), pages 303-316.
    9. Sullivan Hué, 2022. "GAM(L)A: An econometric model for interpretable machine learning," French Stata Users' Group Meetings 2022 19, Stata Users Group.
    10. Simone Narizzano & Marco Orlandi & Antonio Scalia, 2024. "The Bank of Italy’s statistical model for the credit assessment of non-financial firms," Temi di discussione (Economic working papers) 53, Bank of Italy, Economic Research and International Relations Area.
    11. Margherita Doria & Elisa Luciano & Patrizia Semeraro, 2022. "Machine learning techniques in joint default assessment," Papers 2205.01524, arXiv.org, revised Sep 2023.
    12. Sunghyon Kyeong & Daehee Kim & Jinho Shin, 2021. "Can System Log Data Enhance the Performance of Credit Scoring?—Evidence from an Internet Bank in Korea," Sustainability, MDPI, vol. 14(1), pages 1-12, December.
    13. Flavio Bazzana & Marco Bee & Ahmed Almustfa Hussin Adam Khatir, 2024. "Machine learning techniques for default prediction: an application to small Italian companies," Risk Management, Palgrave Macmillan, vol. 26(1), pages 1-23, February.
    14. Miao Zhu & Ben-Chang Shia & Meng Su & Jialin Liu, 2024. "Consumer Default Risk Portrait: An Intelligent Management Framework of Online Consumer Credit Default Risk," Mathematics, MDPI, vol. 12(10), pages 1-19, May.
    15. Yusheng Li & Mengyi Sha, 2024. "Two‐stage credit risk prediction framework based on three‐way decisions with automatic threshold learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1263-1277, August.
    16. Piccialli, Veronica & Romero Morales, Dolores & Salvatore, Cecilia, 2024. "Supervised feature compression based on counterfactual analysis," European Journal of Operational Research, Elsevier, vol. 317(2), pages 273-285.
    17. Dangxing Chen, 2022. "Two-stage Modeling for Prediction with Confidence," Papers 2209.08848, arXiv.org.
    18. Katsafados, Apostolos G. & Leledakis, George N. & Pyrgiotakis, Emmanouil G. & Androutsopoulos, Ion & Fergadiotis, Manos, 2024. "Machine learning in bank merger prediction: A text-based approach," European Journal of Operational Research, Elsevier, vol. 312(2), pages 783-797.
    19. Jomark Pablo Noriega & Luis Antonio Rivera & José Alfredo Herrera, 2023. "Machine Learning for Credit Risk Prediction: A Systematic Literature Review," Data, MDPI, vol. 8(11), pages 1-17, November.
    20. Kellner, Ralf & Nagl, Maximilian & Rösch, Daniel, 2022. "Opening the black box – Quantile neural networks for loss given default prediction," Journal of Banking & Finance, Elsevier, vol. 134(C).
    21. Dangxing Chen & Weicheng Ye, 2022. "Generalized Groves of Neural Additive Models: Pursuing transparent and accurate machine learning models in finance," Papers 2209.10082, arXiv.org, revised Jul 2024.
    22. Emmanuel Flachaire & Gilles Hacheme & Sullivan Hu'e & S'ebastien Laurent, 2022. "GAM(L)A: An econometric model for interpretable Machine Learning," Papers 2203.11691, arXiv.org.
    23. Yang, Fan & Abedin, Mohammad Zoynul & Hajek, Petr, 2024. "An explainable federated learning and blockchain-based secure credit modeling method," European Journal of Operational Research, Elsevier, vol. 317(2), pages 449-467.
    24. Kriebel, Johannes & Stitz, Lennart, 2022. "Credit default prediction from user-generated text in peer-to-peer lending using deep learning," European Journal of Operational Research, Elsevier, vol. 302(1), pages 309-323.
    25. Nadia Ayed & Khemaies Bougatef, 2024. "Performance Assessment of Logistic Regression (LR), Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and Adaptive Neuro-Fuzzy System (ANFIS) in Predicting Default Probability: The Case of," Computational Economics, Springer;Society for Computational Economics, vol. 64(3), pages 1803-1835, September.
    26. Li, Aimin & Li, Zhiyong & Bellotti, Anthony, 2023. "Predicting loss given default of unsecured consumer loans with time-varying survival scores," Pacific-Basin Finance Journal, Elsevier, vol. 78(C).
    27. Dangxing Chen & Weicheng Ye, 2022. "Monotonic Neural Additive Models: Pursuing Regulated Machine Learning Models for Credit Scoring," Papers 2209.10070, arXiv.org.
    28. Jiaming Liu & Xuemei Zhang & Haitao Xiong, 2024. "Credit risk prediction based on causal machine learning: Bayesian network learning, default inference, and interpretation," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1625-1660, August.
    29. John Martin & Sona Taheri & Mali Abdollahian, 2024. "Optimizing Ensemble Learning to Reduce Misclassification Costs in Credit Risk Scorecards," Mathematics, MDPI, vol. 12(6), pages 1-15, March.
    30. Wei Jie Yeo & Wihan van der Heever & Rui Mao & Erik Cambria & Ranjan Satapathy & Gianmarco Mengaldo, 2023. "A Comprehensive Review on Financial Explainable AI," Papers 2309.11960, arXiv.org.
    31. Yang Liu & Fei Huang & Lili Ma & Qingguo Zeng & Jiale Shi, 2024. "Credit scoring prediction leveraging interpretable ensemble learning," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(2), pages 286-308, March.
    32. Shi, Yong & Qu, Yi & Chen, Zhensong & Mi, Yunlong & Wang, Yunong, 2024. "Improved credit risk prediction based on an integrated graph representation learning approach with graph transformation," European Journal of Operational Research, Elsevier, vol. 315(2), pages 786-801.
    33. Li, Zhe & Liang, Shuguang & Pan, Xianyou & Pang, Meng, 2024. "Credit risk prediction based on loan profit: Evidence from Chinese SMEs," Research in International Business and Finance, Elsevier, vol. 67(PA).
    34. Zhou, Ying & Shen, Long & Ballester, Laura, 2023. "A two-stage credit scoring model based on random forest: Evidence from Chinese small firms," International Review of Financial Analysis, Elsevier, vol. 89(C).
    35. Chi, Guotai & Dong, Bingjie & Zhou, Ying & Jin, Peng, 2024. "Long-horizon predictions of credit default with inconsistent customers," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    36. Chen, Yujia & Calabrese, Raffaella & Martin-Barragan, Belen, 2024. "Interpretable machine learning for imbalanced credit scoring datasets," European Journal of Operational Research, Elsevier, vol. 312(1), pages 357-372.
    37. Chen, Dangxing & Ye, Jiahui & Ye, Weicheng, 2023. "Interpretable selective learning in credit risk," Research in International Business and Finance, Elsevier, vol. 65(C).

  2. Elena Dumitrescu & Sullivan Hué & Christophe Hurlin & Sessi Tokpavi, 2021. "Machine Learning or Econometrics for Credit Scoring: Let's Get the Best of Both Worlds," Working Papers hal-02507499, HAL.

    Cited by:

    1. Giuseppe Cascarino & Mirko Moscatelli & Fabio Parlapiano, 2022. "Explainable Artificial Intelligence: interpreting default forecasting models based on Machine Learning," Questioni di Economia e Finanza (Occasional Papers) 674, Bank of Italy, Economic Research and International Relations Area.
    2. Matthieu Garcin & Samuel Stéphan, 2023. "Credit scoring using neural networks and SURE posterior probability calibration," Working Papers hal-03286760, HAL.
    3. Bastien Lextrait, 2021. "Scaling up SME's credit scoring scope with LightGBM," EconomiX Working Papers 2021-25, University of Paris Nanterre, EconomiX.

  3. Sullivan HUE & Yannick LUCOTTE & Sessi TOKPAVI, 2018. "Measuring network systemic risk contributions: A leave-one-out approach," LEO Working Papers / DR LEO 2708, Orleans Economics Laboratory / Laboratoire d'Economie d'Orleans (LEO), University of Orleans.

    Cited by:

    1. Laleh Tafakori & Armin Pourkhanali & Riccardo Rastelli, 2022. "Measuring systemic risk and contagion in the European financial network," Empirical Economics, Springer, vol. 63(1), pages 345-389, July.
    2. Ouyang, Zisheng & Zhou, Xuewei & Wang, Gang-jin & Liu, Shuwen & Lu, Min, 2024. "Multilayer networks in the frequency domain: Measuring volatility connectedness among Chinese financial institutions," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 909-928.
    3. Denisa Banulescu & Christophe Hurlin & Jeremy Leymarie & O. Scaillet, 2019. "Backtesting Marginal Expected Shortfall and Related Systemic Risk Measures," Swiss Finance Institute Research Paper Series 19-48, Swiss Finance Institute.
    4. Alin Marius Andries & Steven Ongena & Nicu Sprincean & Radu Tunaru, 2020. "Risk Spillovers and Interconnectedness between Systemically Important Institutions," Swiss Finance Institute Research Paper Series 20-40, Swiss Finance Institute.
    5. Ouyang, Zisheng & Zhou, Xuewei & Lu, Min & Liu, Ke, 2024. "Imported financial risk in global stock markets: Evidence from the interconnected network," Research in International Business and Finance, Elsevier, vol. 69(C).
    6. Kumar, Sudarshan & Bansal, Avijit & Chakrabarti, Anindya S., 2019. "Ripples on financial networks," IIMA Working Papers WP 2019-10-01, Indian Institute of Management Ahmedabad, Research and Publication Department.
    7. Addi, Abdelhamid & Bouoiyour, Jamal, 2023. "Interconnectedness and extreme risk: Evidence from dual banking systems," Economic Modelling, Elsevier, vol. 120(C).
    8. Lu, Yunzhi & Li, Jie & Yang, Haisheng, 2021. "Time-varying inter-urban housing price spillovers in China: Causes and consequences," Journal of Asian Economics, Elsevier, vol. 77(C).
    9. Xuewei Zhou & Zisheng Ouyang & Rangan Gupta & Qiang Ji, 2024. "Time-Varying Multilayer Networks Analysis of Frequency Connectedness in Commodity Futures Markets," Working Papers 202422, University of Pretoria, Department of Economics.
    10. Ouyang, Zisheng & Zhou, Xuewei, 2023. "Multilayer networks in the frequency domain: Measuring extreme risk connectedness of Chinese financial institutions," Research in International Business and Finance, Elsevier, vol. 65(C).
    11. Foglia, Matteo & Addi, Abdelhamid & Wang, Gang-Jin & Angelini, Eliana, 2022. "Bearish Vs Bullish risk network: A Eurozone financial system analysis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 77(C).
    12. Jean-Baptiste Hasse, 2022. "Systemic risk: a network approach," Post-Print hal-03740283, HAL.
    13. Sudarshan Kumar & Tiziana Di Matteo & Anindya S. Chakrabarti, 2020. "Disentangling shock diffusion on complex networks: Identification through graph planarity," Papers 2001.01518, arXiv.org.
    14. Ki-Hong Choi & Ron P. McIver & Salvatore Ferraro & Lei Xu & Sang Hoon Kang, 2021. "Dynamic volatility spillover and network connectedness across ASX sector markets," Journal of Economics and Finance, Springer;Academy of Economics and Finance, vol. 45(4), pages 677-691, October.
    15. Baumöhl, Eduard & Bouri, Elie & Hoang, Thi-Hong-Van & Shahzad, Syed Jawad Hussain & Výrost, Tomáš, 2020. "Increasing systemic risk during the Covid-19 pandemic: A cross-quantilogram analysis of the banking sector," EconStor Preprints 222580, ZBW - Leibniz Information Centre for Economics.
    16. Ouyang, Zisheng & Zhou, Xuewei, 2023. "Interconnected networks: Measuring extreme risk connectedness between China’s financial sector and real estate sector," International Review of Financial Analysis, Elsevier, vol. 90(C).
    17. Lilit Popoyan & Mauro Napoletano & Andrea Roventini, 2023. "Systemically important banks - emerging risk and policy responses: An agent-based investigation," LEM Papers Series 2023/30, Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy.
    18. Badics, Milan Csaba & Huszar, Zsuzsa R. & Kotro, Balazs B., 2023. "The impact of crisis periods and monetary decisions of the Fed and the ECB on the sovereign yield curve network," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 88(C).
    19. Baumöhl, Eduard & Bouri, Elie & Hoang, Thi-Hong-Van & Shahzad, Syed Jawad Hussain & Výrost,Tomáš, 2020. "From physical to financial contagion: the COVID-19 pandemic and increasing systemic risk among banks," EconStor Preprints 218944, ZBW - Leibniz Information Centre for Economics.
    20. Deyan Radev, 2024. "Dynamic Measures of Sovereign Systemic Risk," Economic Studies journal, Bulgarian Academy of Sciences - Economic Research Institute, issue 5, pages 3-24.
    21. Narayan, Shivani & Kumar, Dilip & Bouri, Elie, 2023. "Systemically important financial institutions and drivers of systemic risk: Evidence from India," Pacific-Basin Finance Journal, Elsevier, vol. 82(C).
    22. Wu, Baohui & Zhu, Pingheng & Yin, Hua & Wen, Fenghua, 2023. "The risk spillover of high carbon enterprises in China: Evidence from the stock market," Energy Economics, Elsevier, vol. 126(C).

Articles

  1. Dumitrescu, Elena & Hué, Sullivan & Hurlin, Christophe & Tokpavi, Sessi, 2022. "Machine learning for credit scoring: Improving logistic regression with non-linear decision-tree effects," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1178-1192.
    See citations under working paper version above.
  2. Hué, Sullivan & Lucotte, Yannick & Tokpavi, Sessi, 2019. "Measuring network systemic risk contributions: A leave-one-out approach," Journal of Economic Dynamics and Control, Elsevier, vol. 100(C), pages 86-114.
    See citations under working paper version above.Sorry, no citations of articles recorded.

More information

Research fields, statistics, top rankings, if available.

Statistics

Access and download statistics for all items

Co-authorship network on CollEc

NEP Fields

NEP is an announcement service for new working papers, with a weekly report in each of many fields. This author has had 6 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-BIG: Big Data (5) 2020-04-06 2021-01-04 2022-03-21 2022-09-12 2024-09-09. Author is listed
  2. NEP-CMP: Computational Economics (5) 2020-04-06 2021-01-04 2022-03-21 2022-09-12 2024-09-09. Author is listed
  3. NEP-RMG: Risk Management (4) 2019-11-25 2020-04-06 2021-01-04 2022-03-21. Author is listed
  4. NEP-ECM: Econometrics (2) 2020-04-06 2022-03-21. Author is listed
  5. NEP-BAN: Banking (1) 2022-03-21
  6. NEP-FOR: Forecasting (1) 2022-09-12

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, Sullivan Hue
(Sullivan Hue) 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.