Mathieu Mercadier

Personal Details

First Name:	Mathieu
Middle Name:
Last Name:	Mercadier
Suffix:
RePEc Short-ID:	pme946
	[This author has chosen not to make the email address public]
Terminal Degree:	(from RePEc Genealogy)

Affiliation

École Supérieure de Commerce de Clermont

Clermont-Ferrant, France
http://www.esc-clermont.fr/
RePEc:edi:escclfr (more details at EDIRC)

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. Mathieu Mercadier & Jean-Pierre Lardy, 2019. "Credit spread approximation and improvement using random forest regression," Post-Print hal-03241566, HAL.
   • Mercadier, Mathieu & Lardy, Jean-Pierre, 2019. "Credit spread approximation and improvement using random forest regression," European Journal of Operational Research, Elsevier, vol. 277(1), pages 351-365.
   • Mathieu Mercadier & Jean-Pierre Lardy, 2019. "Credit Spread Approximation and Improvement using Random Forest Regression," Post-Print hal-02057019, HAL.
   • Mathieu Mercadier & Jean-Pierre Lardy, 2021. "Credit spread approximation and improvement using random forest regression," Papers 2106.07358, arXiv.org.

   Cited by:

   1. Efstathios Polyzos & Aristeidis Samitas & Ghulame Rubbaniy, 2024. "The perfect bail‐in: Financing without banks using peer‐to‐peer lending," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(3), pages 3393-3412, July.
   2. Solomon Y. Deku & Alper Kara & Artur Semeyutin, 2021. "The predictive strength of MBS yield spreads during asset bubbles," Review of Quantitative Finance and Accounting, Springer, vol. 56(1), pages 111-142, January.
   3. Mathieu Mercadier & Frank Strobel, 2021. "A one-sided Vysochanskii-Petunin inequality with financial applications," Post-Print hal-03241628, HAL.
      • Mercadier, Mathieu & Strobel, Frank, 2021. "A one-sided Vysochanskii-Petunin inequality with financial applications," European Journal of Operational Research, Elsevier, vol. 295(1), pages 374-377.
   4. Tolga Yalçin & Pol Paradell Solà & Paschalia Stefanidou-Voziki & Jose Luis Domínguez-García & Tugce Demirdelen, 2023. "Exploiting Digitalization of Solar PV Plants Using Machine Learning: Digital Twin Concept for Operation," Energies, MDPI, vol. 16(13), pages 1-17, June.
   5. Yang, Cai & Zhang, Hongwei & Weng, Futian, 2024. "Effects of COVID-19 vaccination programs on EU carbon price forecasts: Evidence from explainable machine learning," International Review of Financial Analysis, Elsevier, vol. 91(C).
   6. Chengyuan Li & Haoran Zhu & Hanjun Luo & Suyang Zhou & Jieping Kong & Lei Qi & Congjun Rao, 2023. "Spread Prediction and Classification of Asian Giant Hornets Based on GM-Logistic and CSRF Models," Mathematics, MDPI, vol. 11(6), pages 1-26, March.
   7. Nielson, Jordan & Bhaganagar, Kiran & Meka, Rajitha & Alaeddini, Adel, 2020. "Using atmospheric inputs for Artificial Neural Networks to improve wind turbine power prediction," Energy, Elsevier, vol. 190(C).
   8. Hoang Hiep Nguyen & Jean-Laurent Viviani & Sami Ben Jabeur, 2023. "Bankruptcy prediction using machine learning and Shapley additive explanations," Post-Print hal-04223161, HAL.
   9. Santiago Carbo-Valverde & Pedro Cuadros-Solas & Francisco Rodríguez-Fernández, 2020. "A machine learning approach to the digitalization of bank customers: Evidence from random and causal forests," PLOS ONE, Public Library of Science, vol. 15(10), pages 1-39, October.
   10. Mohammad S. Uddin & Guotai Chi & Mazin A. M. Al Janabi & Tabassum Habib, 2022. "Leveraging random forest in micro‐enterprises credit risk modelling for accuracy and interpretability," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(3), pages 3713-3729, July.

Articles

1. Mercadier, Mathieu & Lardy, Jean-Pierre, 2019. "Credit spread approximation and improvement using random forest regression," European Journal of Operational Research, Elsevier, vol. 277(1), pages 351-365.
   • Mathieu Mercadier & Jean-Pierre Lardy, 2019. "Credit Spread Approximation and Improvement using Random Forest Regression," Post-Print hal-02057019, HAL.
   • Mathieu Mercadier & Jean-Pierre Lardy, 2021. "Credit spread approximation and improvement using random forest regression," Papers 2106.07358, arXiv.org.
   • Mathieu Mercadier & Jean-Pierre Lardy, 2019. "Credit spread approximation and improvement using random forest regression," Post-Print hal-03241566, HAL.
   See citations under working paper version above.Sorry, no citations of articles recorded.

More information

Research fields, statistics, top rankings, if available.

Statistics

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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 3 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-CMP: Computational Economics (2) 2021-06-21 2021-06-28. Author is listed
2. NEP-RMG: Risk Management (2) 2021-06-21 2021-06-21. Author is listed
3. NEP-BIG: Big Data (1) 2021-06-28. Author is listed

Corrections

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