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Jose Manuel Carbo Martinez

Personal Details

First Name:Jose Manuel
Middle Name:
Last Name:Carbo Martinez
Suffix:
RePEc Short-ID:pca1748
[This author has chosen not to make the email address public]
https://sites.google.com/site/carbomartinezjose/home
Terminal Degree: Instituto Flores de Lemus; Universidad Carlos III de Madrid (from RePEc Genealogy)

Affiliation

Banco de España

Madrid, Spain
http://www.bde.es/
RePEc:edi:bdegves (more details at EDIRC)

Research output

as
Jump to: Working papers Articles Chapters

Working papers

  1. Andrés Alonso-Robisco & José Manuel Carbó & Pedro Jesús Cuadros-Solas & Jara Quintanero, 2025. "The effects of open banking on fintech providers: evidence using microdata from Spain," Working Papers 2514, Banco de España.
  2. Andrés Alonso-Robisco & Andrés Azqueta-Gavaldón & José Manuel Carbó & José Luis González & Ana Isabel Hernáez & José Luis Herrera & Jorge Quintana & Javier Tarancón, 2025. "Empowering financial supervision: a SupTech experiment using machine learning in an early warning system," Occasional Papers 2504, Banco de España.
  3. Andres Alonso-Robisco & Jose Manuel Carbo & Emily Kormanyos & Elena Triebskorn, 2024. "Houston, we have a problem: can satellite information bridge the climate-related data gap?," Occasional Papers 2428, Banco de España.
  4. José Manuel Carbó & Hossein Jahanshahloo & José Carlos Piqueras, 2024. "Análisis de fuentes de datos para seguir la evolución de Bitcoin," Occasional Papers 2411, Banco de España.
  5. Andres Alonso-Robisco & Jose Manuel Carbo, 2023. "Analysis of CBDC Narrative OF Central Banks using Large Language Models," Working Papers 2321, Banco de España.
  6. Andrés Alonso-Robisco & José Manuel Carbó & José Manuel Marqués, 2023. "Machine Learning methods in climate finance: a systematic review," Working Papers 2310, Banco de España.
  7. Andrés Alonso-Robisco & José Manuel Carbó, 2022. "Inteligencia artificial y finanzas: una alianza estratégica," Occasional Papers 2222, Banco de España.
  8. José Manuel Carbó & Sergio Gorjón, 2022. "Application of machine learning models and interpretability techniques to identify the determinants of the price of bitcoin," Working Papers 2215, Banco de España.
  9. Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
  10. Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.
  11. José Manuel Carbó & Esther Diez García, 2021. "El interés por la innovación financiera en España. Un análisis con google trends," Occasional Papers 2112, Banco de España.
  12. Andrés Alonso & José Manuel Carbó, 2020. "Machine learning in credit risk: measuring the dilemma between prediction and supervisory cost," Working Papers 2032, Banco de España.

Articles

  1. Andres Alonso-Robisco & Javier Bas & Jose Manuel Carbo & Aranzazu de Juan & Jose Manuel Marques, 2025. "Where and how machine learning plays a role in climate finance research," Journal of Sustainable Finance & Investment, Taylor & Francis Journals, vol. 15(2), pages 456-497, April.
  2. Carbó, José Manuel & Gorjón, Sergio, 2024. "Determinants of the price of bitcoin: An analysis with machine learning and interpretability techniques," International Review of Economics & Finance, Elsevier, vol. 92(C), pages 123-140.
  3. Alonso-Robisco, Andres & Carbó, José Manuel, 2023. "Analysis of CBDC narrative by central banks using large language models," Finance Research Letters, Elsevier, vol. 58(PC).
  4. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.
  5. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).
  6. Laila AitBihiOuali & Jose M. Carbo & Daniel J. Graham, 2020. "Do changes in air transportation affect productivity? A cross‐country panel approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(3), pages 493-505, June.
  7. Jose M. Carbo & Daniel J. Graham & Anupriya & Daniel Casas & Patricia C. Melo, 2019. "Evaluating the causal economic impacts of transport investments: evidence from the Madrid–Barcelona high speed rail corridor," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(9), pages 1714-1723, July.

Chapters

  1. Andres Alonso-Robisco & Jose Carbo & Emily Kormanyos & Elena Triebskorn, 2025. "Houston, we have a problem: can satellite information bridge the climate-related data gap?," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Addressing climate change data needs: the central banks' contribution, volume 63, Bank for International Settlements.
  2. José Manuel Carbó Martinez & Sergio Gorjón Rivas, 2024. "Determinants of the price of bitcoin: An analysis with machine learning and interpretability techniques," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Granular data: new horizons and challenges, volume 61, Bank for International Settlements.

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. Andres Alonso-Robisco & Jose Manuel Carbo, 2023. "Analysis of CBDC Narrative OF Central Banks using Large Language Models," Working Papers 2321, Banco de España.

    Cited by:

    1. Wu, WenTing & Chen, XiaoQian & Zvarych, Roman & Huang, WeiLun, 2024. "The Stackelberg duel between Central Bank Digital Currencies and private payment titans in China," Technological Forecasting and Social Change, Elsevier, vol. 200(C).
    2. Mengming Michael Dong & Theophanis C. Stratopoulos & Victor Xiaoqi Wang, 2024. "A Scoping Review of ChatGPT Research in Accounting and Finance," Papers 2412.05731, arXiv.org.
    3. Chong Zhang & Xinyi Liu & Zhongmou Zhang & Mingyu Jin & Lingyao Li & Zhenting Wang & Wenyue Hua & Dong Shu & Suiyuan Zhu & Xiaobo Jin & Sujian Li & Mengnan Du & Yongfeng Zhang, 2024. "When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments," Papers 2407.18957, arXiv.org, revised Sep 2024.
    4. Can Celebi & Stefan Penczynski, 2024. "Using Large Language Models for Text Classification in Experimental Economics," Working Paper series, University of East Anglia, Centre for Behavioural and Experimental Social Science (CBESS) 24-01, School of Economics, University of East Anglia, Norwich, UK..

  2. Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.

    Cited by:

    1. Edward I. Altman & Marco Balzano & Alessandro Giannozzi & Stjepan Srhoj, 2023. "Revisiting SME default predictors: The Omega Score," Journal of Small Business Management, Taylor & Francis Journals, vol. 61(6), pages 2383-2417, November.
    2. Pedro Guerra & Mauro Castelli, 2021. "Machine Learning Applied to Banking Supervision a Literature Review," Risks, MDPI, vol. 9(7), pages 1-24, July.
    3. 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.
    4. Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
    5. Ryuichiro Hashimoto & Kakeru Miura & Yasunori Yoshizaki, 2023. "Application of Machine Learning to a Credit Rating Classification Model: Techniques for Improving the Explainability of Machine Learning," Bank of Japan Working Paper Series 23-E-6, Bank of Japan.
    6. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.

  3. Andrés Alonso & José Manuel Carbó, 2020. "Machine learning in credit risk: measuring the dilemma between prediction and supervisory cost," Working Papers 2032, Banco de España.

    Cited by:

    1. Wosnitza, Jan Henrik, 2022. "Calibration alternatives to logistic regression and their potential for transferring the dispersion of discriminatory power into uncertainties of probabilities of default," Discussion Papers 04/2022, Deutsche Bundesbank.
    2. Pedro Guerra & Mauro Castelli, 2021. "Machine Learning Applied to Banking Supervision a Literature Review," Risks, MDPI, vol. 9(7), pages 1-24, July.
    3. Faraz Ahmed & Kehkashan Nizam & Zubair Sajid & Sunain Qamar & Ahsan, 2024. "Striking a Balance: Evaluating Credit Risk with Traditional and Machine Learning Models," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(3), pages 30-35.
    4. 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.
    5. Lisa Crosato & Caterina Liberati & Marco Repetto, 2021. "Look Who's Talking: Interpretable Machine Learning for Assessing Italian SMEs Credit Default," Papers 2108.13914, arXiv.org, revised Sep 2021.
    6. Citterio, Alberto, 2024. "Bank failure prediction models: Review and outlook," Socio-Economic Planning Sciences, Elsevier, vol. 92(C).
    7. Andrés Alonso & José Manuel Carbó, 2022. "Accuracy of explanations of machine learning models for credit decisions," Working Papers 2222, Banco de España.
    8. Andrés Alonso & José Manuel Carbó, 2021. "Understanding the performance of machine learning models to predict credit default: a novel approach for supervisory evaluation," Working Papers 2105, Banco de España.
    9. Valter T. Yoshida Jr & Alan de Genaro & Rafael Schiozer & Toni R. E. dos Santos, 2023. "A Novel Credit Model Risk Measure: does more data lead to lower model risk in credit scoring models?," Working Papers Series 582, Central Bank of Brazil, Research Department.
    10. Zixue Zhao & Tianxiang Cui & Shusheng Ding & Jiawei Li & Anthony Graham Bellotti, 2024. "Resampling Techniques Study on Class Imbalance Problem in Credit Risk Prediction," Mathematics, MDPI, vol. 12(5), pages 1-27, February.
    11. Dimitrios Nikolaidis & Michalis Doumpos, 2022. "Credit Scoring with Drift Adaptation Using Local Regions of Competence," SN Operations Research Forum, Springer, vol. 3(4), pages 1-28, December.
    12. Antonietta di Salvatore & Mirko Moscatelli, 2024. "Improving survey information on household debt using granular credit databases," Questioni di Economia e Finanza (Occasional Papers) 839, Bank of Italy, Economic Research and International Relations Area.
    13. Pedro Guerra & Mauro Castelli & Nadine Côrte-Real, 2022. "Approaching European Supervisory Risk Assessment with SupTech: A Proposal of an Early Warning System," Risks, MDPI, vol. 10(4), pages 1-23, March.

Articles

  1. Alonso-Robisco, Andres & Carbó, José Manuel, 2023. "Analysis of CBDC narrative by central banks using large language models," Finance Research Letters, Elsevier, vol. 58(PC).

    Cited by:

    1. Mengming Michael Dong & Theophanis C. Stratopoulos & Victor Xiaoqi Wang, 2024. "A Scoping Review of ChatGPT Research in Accounting and Finance," Papers 2412.05731, arXiv.org.
    2. Arata ITO & Masahiro SATO & Rui OTA, 2024. "Content-based Metric on Monetary Policy Uncertainty by Using Large Language Models," Discussion papers 24080, Research Institute of Economy, Trade and Industry (RIETI).
    3. Julian Junyan Wang & Victor Xiaoqi Wang, 2025. "Assessing Consistency and Reproducibility in the Outputs of Large Language Models: Evidence Across Diverse Finance and Accounting Tasks," Papers 2503.16974, arXiv.org, revised Jun 2025.

  2. Andrés Alonso Robisco & José Manuel Carbó Martínez, 2022. "Measuring the model risk-adjusted performance of machine learning algorithms in credit default prediction," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 8(1), pages 1-35, December.

    Cited by:

    1. Faraz Ahmed & Kehkashan Nizam & Zubair Sajid & Sunain Qamar & Ahsan, 2024. "Striking a Balance: Evaluating Credit Risk with Traditional and Machine Learning Models," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(3), pages 30-35.
    2. González, Marta Ramos & Ureña, Antonio Partal & Fernández-Aguado, Pilar Gómez, 2023. "Forecasting for regulatory credit loss derived from the COVID-19 pandemic: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    3. Calabrese, G.G. & Falavigna, G. & Ippoliti, R., 2024. "Financial constraints prediction to lead socio-economic development: An application of neural networks to the Italian market," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
    4. Blanco-Oliver Antonio & Lara-Rubio Juan & Irimia-Diéguez Ana & Liébana-Cabanillas Francisco, 2024. "Examining user behavior with machine learning for effective mobile peer-to-peer payment adoption," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-30, December.
    5. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).
    6. Gang Kou & Yang Lu, 2025. "FinTech: a literature review of emerging financial technologies and applications," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-34, December.
    7. Cristiana Tudor & Robert Sova, 2025. "An automated adaptive trading system for enhanced performance of emerging market portfolios," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 11(1), pages 1-39, December.
    8. Ryuichiro Hashimoto & Kakeru Miura & Yasunori Yoshizaki, 2023. "Application of Machine Learning to a Credit Rating Classification Model: Techniques for Improving the Explainability of Machine Learning," Bank of Japan Working Paper Series 23-E-6, Bank of Japan.
    9. Mingchen Li & Kun Yang & Wencan Lin & Yunjie Wei & Shouyang Wang, 2024. "An interval constraint-based trading strategy with social sentiment for the stock market," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-31, December.

  3. Alonso-Robisco, Andrés & Carbó, José Manuel, 2022. "Can machine learning models save capital for banks? Evidence from a Spanish credit portfolio," International Review of Financial Analysis, Elsevier, vol. 84(C).

    Cited by:

    1. Cosma, Simona & Rimo, Giuseppe & Torluccio, Giuseppe, 2023. "Knowledge mapping of model risk in banking," International Review of Financial Analysis, Elsevier, vol. 89(C).
    2. Riyadh Mehdi & Ibrahim Elsiddig Ahmed & Elfadil A. Mohamed, 2025. "Rating the Impact of Risks in Banking on Performance: Utilizing the Adaptive Neural Network-Based Fuzzy Inference System (ANFIS)," Risks, MDPI, vol. 13(5), pages 1-23, April.
    3. Bolívar, Fernando & Duran, Miguel A. & Lozano-Vivas, Ana, 2023. "Business model contributions to bank profit performance: A machine learning approach," Research in International Business and Finance, Elsevier, vol. 64(C).
    4. 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).

  4. Laila AitBihiOuali & Jose M. Carbo & Daniel J. Graham, 2020. "Do changes in air transportation affect productivity? A cross‐country panel approach," Regional Science Policy & Practice, Wiley Blackwell, vol. 12(3), pages 493-505, June.

    Cited by:

    1. Mauro Caetano & Evandro José Silva & Diogo José Vieira & Cláudio Jorge Pinto Alves & Carlos Müller, 2022. "Criteria prioritization for investment policies in General Aviation aerodromes," Regional Science Policy & Practice, Wiley Blackwell, vol. 14(6), pages 211-233, December.

  5. Jose M. Carbo & Daniel J. Graham & Anupriya & Daniel Casas & Patricia C. Melo, 2019. "Evaluating the causal economic impacts of transport investments: evidence from the Madrid–Barcelona high speed rail corridor," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(9), pages 1714-1723, July.

    Cited by:

    1. Baek, Jisun & Park, WooRam, 2022. "The impact of improved passenger transport system on manufacturing plant productivity," Regional Science and Urban Economics, Elsevier, vol. 96(C).
    2. Xiaoxuan Zhang & John Gibson, 2025. "Local economic effects of connecting to China’s high-speed rail network: evidence from spatial econometric models," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 74(2), pages 1-32, June.
    3. Sun, Yunpeng & Razzaq, Asif & Kizys, Renatas & Bao, Qun, 2022. "High-speed rail and urban green productivity: The mediating role of climatic conditions in China," Technological Forecasting and Social Change, Elsevier, vol. 185(C).
    4. Badura, Ondrej & Melecky, Ales & Melecky, Martin, 2022. "Liberalizing Passenger Rail: The Effect of Competition on Local Unemployment," MPRA Paper 111651, University Library of Munich, Germany.
    5. Federica Rossi & Rico Maggi, 2019. "Business travel decisions and high-speed trains: an ordered logit approach," REGION, European Regional Science Association, vol. 6, pages 1-16.
    6. Dong, Yan & Huang, Jun & Wu, Ji, 2023. "Does high-speed rail affect the agglomeration of banks in China?," Emerging Markets Review, Elsevier, vol. 56(C).
    7. Li, Chunying & Zhang, Jinning & Lyu, Yanwei, 2022. "Does the opening of China railway express promote urban total factor productivity? New evidence based on SDID and SDDD model," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    8. Vickerman, Roger, 2024. "The transport problem: The need for consistent policies on pricing and investment," Transport Policy, Elsevier, vol. 149(C), pages 49-58.
    9. Ginés de Rus & Javier Campos & Daniel Graham & M. Pilar Socorro & Jorge Valido, 2020. "Evaluación Económica de Proyectos y Políticas de Transporte: Metodología y Aplicaciones. Parte 1: Metodología para el análisis coste-beneficio de proyectos y políticas de transporte," Working Papers 2020-11, FEDEA.
    10. Yang, Xuehui & Zhang, Huirong & Li, Yan, 2022. "High-speed railway, factor flow and enterprise innovation efficiency: An empirical analysis on micro data," Socio-Economic Planning Sciences, Elsevier, vol. 82(PB).
    11. Di Matteo, Dante & Mariotti, Ilaria & Rossi, Federica, 2023. "Transport infrastructure and economic performance: An evaluation of the Milan-Bologna high-speed rail corridor," Socio-Economic Planning Sciences, Elsevier, vol. 85(C).
    12. Sobieralski, Joseph B., 2021. "Transportation infrastructure and employment: Are all investments created equal?," Research in Transportation Economics, Elsevier, vol. 88(C).

Chapters

  1. Andres Alonso-Robisco & Jose Carbo & Emily Kormanyos & Elena Triebskorn, 2025. "Houston, we have a problem: can satellite information bridge the climate-related data gap?," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Addressing climate change data needs: the central banks' contribution, volume 63, Bank for International Settlements.

    Cited by:

    1. David Nefzi & Jolien Noels & Romana Peronaci & Christian Schmieder & Ünal Seven & Ömer K Seyhun & Bruno Tissot & Elena Triebskorn, 2025. "Addressing climate change data needs: the global debate and central banks' contribution," IFC Bulletins chapters, in: Bank for International Settlements (ed.), Addressing climate change data needs: the central banks' contribution, volume 63, Bank for International Settlements.

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 12 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 (8) 2020-11-16 2021-03-15 2021-05-17 2022-08-29 2022-09-05 2023-05-22 2023-09-25 2025-04-07. Author is listed
  2. NEP-PAY: Payment Systems and Financial Technology (8) 2020-11-16 2021-05-17 2022-08-29 2022-09-05 2023-05-22 2023-09-25 2024-05-27 2025-03-03. Author is listed
  3. NEP-CMP: Computational Economics (6) 2020-11-16 2021-03-15 2022-08-29 2022-09-05 2023-09-25 2025-04-07. Author is listed
  4. NEP-RMG: Risk Management (3) 2020-11-16 2021-03-15 2025-04-07. Author is listed
  5. NEP-BAN: Banking (2) 2020-11-16 2023-09-25
  6. NEP-CBA: Central Banking (2) 2023-09-25 2024-09-16
  7. NEP-ENE: Energy Economics (2) 2023-05-22 2024-09-16
  8. NEP-FDG: Financial Development and Growth (2) 2022-09-05 2025-04-07
  9. NEP-MON: Monetary Economics (2) 2022-09-05 2023-09-25
  10. NEP-AGR: Agricultural Economics (1) 2024-09-16
  11. NEP-AIN: Artificial Intelligence (1) 2023-09-25
  12. NEP-ENV: Environmental Economics (1) 2023-05-22
  13. NEP-FMK: Financial Markets (1) 2020-11-16
  14. NEP-NET: Network Economics (1) 2025-04-07
  15. NEP-SBM: Small Business Management (1) 2025-03-03

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