IDEAS home Printed from https://ideas.repec.org/f/c/pba531.html

João Afonso Bastos
(Joao Afonso Bastos)

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. João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.

    Cited by:

    1. Caiado, Jorge & Lúcio, Francisco, 2023. "Stock market forecasting accuracy of asymmetric GARCH models during the COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 68(C).
    2. Roy Cerqueti & Raffaele Mattera & Germana Scepi, 2024. "Multiway clustering with time-varying parameters," Computational Statistics, Springer, vol. 39(1), pages 51-92, February.
    3. João A. Bastos, 2025. "A deep learning test of the martingale difference hypothesis," Working Papers REM 2025/0374, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    4. Lúcio, Francisco & Caiado, Jorge, 2022. "COVID-19 and Stock Market Volatility: A Clustering Approach for S&P 500 Industry Indices," Finance Research Letters, Elsevier, vol. 49(C).
    5. Roy Cerqueti & Pierpaolo D’Urso & Livia Giovanni & Raffaele Mattera & Vincenzina Vitale, 2024. "Fuzzy clustering of time series based on weighted conditional higher moments," Computational Statistics, Springer, vol. 39(6), pages 3091-3114, September.

  2. João A. Bastos & Sara M. Matos, 2021. "Explainable models of credit losses," Working Papers REM 2021/0161, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.

    Cited by:

    1. Ahmed, Abdulaziz & Topuz, Kazim & Moqbel, Murad & Abdulrashid, Ismail, 2024. "What makes accidents severe! explainable analytics framework with parameter optimization," European Journal of Operational Research, Elsevier, vol. 317(2), pages 425-436.
    2. Kun Liu & Jin Zhao, 2024. "KACDP: A Highly Interpretable Credit Default Prediction Model," Papers 2411.17783, arXiv.org.
    3. Ghosh, Indranil & De, Arijit, 2024. "Maritime Fuel Price Prediction of European Ports using Least Square Boosting and Facebook Prophet: Additional Insights from Explainable Artificial Intelligence," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 189(C).
    4. Janssens, Bram & Schetgen, Lisa & Bogaert, Matthias & Meire, Matthijs & Van den Poel, Dirk, 2024. "360 Degrees rumor detection: When explanations got some explaining to do," European Journal of Operational Research, Elsevier, vol. 317(2), pages 366-381.
    5. 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.
    6. Distaso, Walter & Roccazzella, Francesco & Vrins, Frédéric, 2025. "Business cycle and realized losses in the consumer credit industry," European Journal of Operational Research, Elsevier, vol. 323(3), pages 1024-1039.
    7. Julia Brasse & Hanna Rebecca Broder & Maximilian Förster & Mathias Klier & Irina Sigler, 2023. "Explainable artificial intelligence in information systems: A review of the status quo and future research directions," Electronic Markets, Springer;IIM University of St. Gallen, vol. 33(1), pages 1-30, December.
    8. Nazemi, Abdolreza & Fabozzi, Frank J., 2024. "Interpretable machine learning for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 164(C).
    9. M. K. Nallakaruppan & Himakshi Chaturvedi & Veena Grover & Balamurugan Balusamy & Praveen Jaraut & Jitendra Bahadur & V. P. Meena & Ibrahim A. Hameed, 2024. "Credit Risk Assessment and Financial Decision Support Using Explainable Artificial Intelligence," Risks, MDPI, vol. 12(10), pages 1-18, October.
    10. 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).
    11. Bauer, Kevin & Gill, Andrej & Langenbucher, Katja & Franke, Lucia, 2025. "Institutionalizing explainability: On credit scoring, AI, and consumer agency," SAFE White Paper Series 116, Leibniz Institute for Financial Research SAFE.
    12. Cai Yang & Mohammad Zoynul Abedin & Hongwei Zhang & Futian Weng & Petr Hajek, 2025. "An interpretable system for predicting the impact of COVID-19 government interventions on stock market sectors," Annals of Operations Research, Springer, vol. 347(2), pages 1031-1058, April.
    13. Xiong Xiong & Fan Yang & Li Su, 2023. "Popularity, face and voice: Predicting and interpreting livestreamers' retail performance using machine learning techniques," Papers 2310.19200, arXiv.org.
    14. Petter Eilif de Lange & Borger Melsom & Christian Bakke Vennerød & Sjur Westgaard, 2022. "Explainable AI for Credit Assessment in Banks," JRFM, MDPI, vol. 15(12), pages 1-23, November.
    15. Thuy, Arthur & Benoit, Dries F., 2024. "Explainability through uncertainty: Trustworthy decision-making with neural networks," European Journal of Operational Research, Elsevier, vol. 317(2), pages 330-340.
    16. 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).
    17. Koen W. de Bock & Kristof Coussement & Arno De Caigny & Roman Slowiński & Bart Baesens & Robert N Boute & Tsan-Ming Choi & Dursun Delen & Mathias Kraus & Stefan Lessmann & Sebastián Maldonado & David , 2023. "Explainable AI for Operational Research: A Defining Framework, Methods, Applications, and a Research Agenda," Post-Print hal-04219546, HAL.
    18. 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.
    19. De Bock, Koen W. & Coussement, Kristof & Caigny, Arno De & Słowiński, Roman & Baesens, Bart & Boute, Robert N. & Choi, Tsan-Ming & Delen, Dursun & Kraus, Mathias & Lessmann, Stefan & Maldonado, Sebast, 2024. "Explainable AI for Operational Research: A defining framework, methods, applications, and a research agenda," European Journal of Operational Research, Elsevier, vol. 317(2), pages 249-272.

  3. Bastos, João A., 2019. "Forecasting the capacity of mobile networks," MPRA Paper 92727, University Library of Munich, Germany.

    Cited by:

    1. Irina Kochetkova & Anna Kushchazli & Sofia Burtseva & Andrey Gorshenin, 2023. "Short-Term Mobile Network Traffic Forecasting Using Seasonal ARIMA and Holt-Winters Models," Future Internet, MDPI, vol. 15(9), pages 1-15, August.

  4. Joao A. Bastos, 2013. "Ensemble predictions of recovery rates," CEMAPRE Working Papers 1301, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. Bellotti, Anthony & Brigo, Damiano & Gambetti, Paolo & Vrins, Frédéric, 2021. "Forecasting recovery rates on non-performing loans with machine learning," International Journal of Forecasting, Elsevier, vol. 37(1), pages 428-444.
    2. Joost Bosker & Marc Gürtler & Marvin Zöllner, 2025. "Machine learning-based variable selection for clustered credit risk modeling," Journal of Business Economics, Springer, vol. 95(4), pages 617-652, May.
    3. Nazemi, Abdolreza & Fabozzi, Frank J., 2024. "Interpretable machine learning for creditor recovery rates," Journal of Banking & Finance, Elsevier, vol. 164(C).
    4. Chen, Xiaowei & Wang, Gang & Zhang, Xiangting, 2019. "Modeling recovery rate for leveraged loans," Economic Modelling, Elsevier, vol. 81(C), pages 231-241.
    5. Gambetti, Paolo & Roccazzella, Francesco & Vrins, Frédéric, 2020. "Meta-learning approaches for recovery rate prediction," LIDAM Discussion Papers LFIN 2020007, Université catholique de Louvain, Louvain Finance (LFIN).
    6. João A. Bastos, 2022. "Predicting Credit Scores with Boosted Decision Trees," Forecasting, MDPI, vol. 4(4), pages 1-11, November.
    7. Marc Gürtler & Marvin Zöllner, 2023. "Heterogeneities among credit risk parameter distributions: the modality defines the best estimation method," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 251-287, March.
    8. Xia, Yufei & Zhao, Junhao & He, Lingyun & Li, Yinguo & Yang, Xiaoli, 2021. "Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1590-1613.
    9. Ruey-Ching Hwang & Chih-Kang Chu & Kaizhi Yu, 2021. "Predicting the Loss Given Default Distribution with the Zero-Inflated Censored Beta-Mixture Regression that Allows Probability Masses and Bimodality," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(3), pages 143-172, June.
    10. Martin Leo & Suneel Sharma & K. Maddulety, 2019. "Machine Learning in Banking Risk Management: A Literature Review," Risks, MDPI, vol. 7(1), pages 1-22, March.
    11. Olson, Luke M. & Qi, Min & Zhang, Xiaofei & Zhao, Xinlei, 2021. "Machine learning loss given default for corporate debt," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 144-159.
    12. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    13. Altman, Edward I. & Kalotay, Egon A., 2014. "Ultimate recovery mixtures," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 116-129.
    14. Cheng, Hui & Jiang, Cuiqing & Wang, Zhao & Ni, Xiaoya, 2025. "Multi-view locally weighted regression for loss given default forecasting," International Journal of Forecasting, Elsevier, vol. 41(1), pages 290-306.
    15. Nazemi, Abdolreza & Baumann, Friedrich & Fabozzi, Frank J., 2022. "Intertemporal defaulted bond recoveries prediction via machine learning," European Journal of Operational Research, Elsevier, vol. 297(3), pages 1162-1177.
    16. Chih-Kang Chu & Ruey-Ching Hwang, 2019. "Predicting Loss Distributions for Small-Size Defaulted-Debt Portfolios Using a Convolution Technique that Allows Probability Masses to Occur at Boundary Points," Journal of Financial Services Research, Springer;Western Finance Association, vol. 56(1), pages 95-117, August.
    17. Dimitris Andriosopoulos & Michalis Doumpos & Panos M. Pardalos & Constantin Zopounidis, 2019. "Computational approaches and data analytics in financial services: A literature review," Journal of the Operational Research Society, Taylor & Francis Journals, vol. 70(10), pages 1581-1599, October.
    18. Nazemi, Abdolreza & Fatemi Pour, Farnoosh & Heidenreich, Konstantin & Fabozzi, Frank J., 2017. "Fuzzy decision fusion approach for loss-given-default modeling," European Journal of Operational Research, Elsevier, vol. 262(2), pages 780-791.
    19. Miller, Patrick & Töws, Eugen, 2018. "Loss given default adjusted workout processes for leases," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 189-201.
    20. Kaposty, Florian & Kriebel, Johannes & Löderbusch, Matthias, 2020. "Predicting loss given default in leasing: A closer look at models and variable selection," International Journal of Forecasting, Elsevier, vol. 36(2), pages 248-266.
    21. Pascal François & Weiyu Jiang, 2019. "Credit Value Adjustment with Market-implied Recovery," Journal of Financial Services Research, Springer;Western Finance Association, vol. 56(2), pages 145-166, October.
    22. Hwang, Ruey-Ching & Chu, Chih-Kang & Yu, Kaizhi, 2020. "Predicting LGD distributions with mixed continuous and discrete ordinal outcomes," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1003-1022.
    23. Hurlin, Christophe & Leymarie, Jérémy & Patin, Antoine, 2018. "Loss functions for Loss Given Default model comparison," European Journal of Operational Research, Elsevier, vol. 268(1), pages 348-360.

  5. Joao A. Bastos & Joaquim J. S. Ramalho, 2010. "Nonparametric models of financial leverage decisions," CEMAPRE Working Papers 1005, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. Villani, Mattias & Kohn, Robert & Nott, David J., 2012. "Generalized smooth finite mixtures," Journal of Econometrics, Elsevier, vol. 171(2), pages 121-133.
    2. Feng Li & Mattias Villani, 2013. "Efficient Bayesian Multivariate Surface Regression," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 40(4), pages 706-723, December.

  6. Joao A. Bastos, 2010. "Predicting bank loan recovery rates with neural networks," CEMAPRE Working Papers 1003, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. Stephan Höcht & Aleksey Min & Jakub Wieczorek & Rudi Zagst, 2022. "Explaining Aggregated Recovery Rates," Risks, MDPI, vol. 10(1), pages 1-30, January.
    2. Natalia Nehrebecka, 2019. "Bank loans recovery rate in commercial banks: A case study of non-financial corporations," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 37(1), pages 139-172.
    3. Aleksey Min & Matthias Scherer & Amelie Schischke & Rudi Zagst, 2020. "Modeling Recovery Rates of Small- and Medium-Sized Entities in the US," Mathematics, MDPI, vol. 8(11), pages 1-18, October.

  7. Joao A. Bastos & Jorge Caiado, 2010. "Recurrence quantification analysis of global stock markets," CEMAPRE Working Papers 1006, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. Halari, Anwar & Helliar, Christine & Power, David M. & Tantisantiwong, Nongnuch, 2019. "Taking advantage of Ramadan and January in Muslim countries," The Quarterly Review of Economics and Finance, Elsevier, vol. 74(C), pages 85-96.
    2. Juan Benjamín Duarte Duarte & Juan Manuel Mascare?nas P�rez-I�igo, 2014. "Comprobación de la eficiencia débil en los principales mercados financieros latinoamericanos," Estudios Gerenciales, Universidad Icesi.
    3. Juan Meng & Bin Mo & He Nie, 2023. "The dynamics of crude oil future prices on China's energy markets: Quantile‐on‐quantile and casualty‐in‐quantiles approaches," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(12), pages 1853-1871, December.
    4. Xu, Mengjia & Shang, Pengjian & Lin, Aijing, 2017. "Multiscale recurrence quantification analysis of order recurrence plots," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 469(C), pages 381-389.
    5. Teresa Aparicio & Dulce Saura, 2013. "Do Exchange Rate Series Present General Dependence? Some Results using Recurrence Quantification Analysis," Journal of Economics and Behavioral Studies, AMH International, vol. 5(10), pages 678-686.
    6. Krishnadas M. & K. P. Harikrishnan & G. Ambika, 2022. "Recurrence measures and transitions in stock market dynamics," Papers 2208.03456, arXiv.org.
    7. Yao, Can-Zhong & Lin, Qing-Wen, 2017. "Recurrence plots analysis of the CNY exchange markets based on phase space reconstruction," The North American Journal of Economics and Finance, Elsevier, vol. 42(C), pages 584-596.
    8. Orlando, Giuseppe & Zimatore, Giovanna, 2018. "Recurrence quantification analysis of business cycles," Chaos, Solitons & Fractals, Elsevier, vol. 110(C), pages 82-94.
    9. Mostafa Shabani & Martin Magris & George Tzagkarakis & Juho Kanniainen & Alexandros Iosifidis, 2022. "Predicting the State of Synchronization of Financial Time Series using Cross Recurrence Plots," Papers 2210.14605, arXiv.org, revised Nov 2022.
    10. Chen, Yuan & Lin, Aijing, 2022. "Order pattern recurrence for the analysis of complex systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).
    11. Fatoorehchi, Hooman & Zarghami, Reza & Abolghasemi, Hossein & Rach, Randolph, 2015. "Chaos control in the cerium-catalyzed Belousov–Zhabotinsky reaction using recurrence quantification analysis measures," Chaos, Solitons & Fractals, Elsevier, vol. 76(C), pages 121-129.
    12. M. Shabani & M. Magris & George Tzagkarakis & J. Kanniainen & A. Iosifidis, 2023. "Predicting the state of synchronization of financial time series using cross recurrence plots," Post-Print hal-04415269, HAL.
    13. Jiang, Runze & Shang, Pengjian & Yin, Yi, 2025. "Global ordinal pattern attention entropy: A novel feature extraction method for complex signals," Chaos, Solitons & Fractals, Elsevier, vol. 191(C).
    14. Froguel, Lucas Belasque & de Lima Prado, Thiago & Corso, Gilberto & dos Santos Lima, Gustavo Zampier & Lopes, Sergio Roberto, 2022. "Efficient computation of recurrence quantification analysis via microstates," Applied Mathematics and Computation, Elsevier, vol. 428(C).
    15. Sanjay Sathish & Charu C Sharma, 2024. "Leveraging RNNs and LSTMs for Synchronization Analysis in the Indian Stock Market: A Threshold-Based Classification Approach," Papers 2409.06728, arXiv.org.
    16. B. Goswami & G. Ambika & N. Marwan & J. Kurths, 2011. "On interrelations of recurrences and connectivity trends between stock indices," Papers 1103.5189, arXiv.org.
    17. Sergii Piskun & Oleksandr Piskun & Dmitry Chabanenko, 2011. "RQA Application for the Monitoring of Financial and Commodity markets state," Papers 1112.0297, arXiv.org.
    18. Marisa Faggini, 2011. "Chaotic Time Series Analysis in Economics: Balance and Perspectives," Working papers 25, Former Department of Economics and Public Finance "G. Prato", University of Torino.
    19. Kiran Sharma & Shreyansh Shah & Anindya S. Chakrabarti & Anirban Chakraborti, 2016. "Sectoral co-movements in the Indian stock market: A mesoscopic network analysis," Papers 1607.05514, arXiv.org.
    20. Leonidas Sandoval Junior, 2013. "To lag or not to lag? How to compare indices of stock markets that operate at different times," Business and Economics Working Papers 195, Unidade de Negocios e Economia, Insper.
    21. Goswami, B. & Ambika, G. & Marwan, N. & Kurths, J., 2012. "On interrelations of recurrences and connectivity trends between stock indices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4364-4376.
    22. Ashe, Sinéad & Egan, Paul, 2023. "Examining financial and business cycle interaction using cross recurrence plot analysis," Finance Research Letters, Elsevier, vol. 51(C).
    23. M., Krishnadas & Harikrishnan, K.P. & Ambika, G., 2022. "Recurrence measures and transitions in stock market dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 608(P1).
    24. Marisa Faggini & Bruna Bruno & Anna Parziale, 2019. "Does Chaos Matter in Financial Time Series Analysis?," International Journal of Economics and Financial Issues, Econjournals, vol. 9(4), pages 18-24.
    25. Tantisantiwong, Nongnuch & Halari, Anwar & Helliar, Christine & Power, David, 2018. "East meets West: When the Islamic and Gregorian calendars coincide," The British Accounting Review, Elsevier, vol. 50(4), pages 402-424.
    26. Ioannis Andreadis & Athanasios D. Fragkou & Theodoros E. Karakasidis & Apostolos Serletis, 2023. "Nonlinear dynamics in Divisia monetary aggregates: an application of recurrence quantification analysis," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-17, December.
    27. Sandoval, Leonidas Junior, 2013. "To lag or not to lag? How to compare indices of stock markets that operate at different times," Insper Working Papers wpe_319, Insper Working Paper, Insper Instituto de Ensino e Pesquisa.

  8. Joao A. Bastos & Jorge Caiado, 2010. "The structure of international stock market returns," CEMAPRE Working Papers 1002, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. Srinivasan Palamalai & Kalaivani M. & Christopher Devakumar, 2013. "Stock Market Linkages in Emerging Asia-Pacific Markets," SAGE Open, , vol. 3(4), pages 21582440135, November.
    2. Guglielmo Maria Caporale & Luis A. Gil-Alana & C. James Orlando, 2015. "Linkages between the US and European Stock Markets: A Fractional Cointegration Approach," CESifo Working Paper Series 5523, CESifo.
    3. P., Srinivasan & M., Kalaivani, 2013. "Stock Market Linkages in Emerging Asia-Pacific Markets," MPRA Paper 45871, University Library of Munich, Germany.
    4. Andile Khula & Ntebogang Dinah Moroke, 2017. "The Performance of Maximum Likelihood Factor Analysis on South African Stock Price Performance," Journal of Economics and Behavioral Studies, AMH International, vol. 8(6), pages 40-51.

  9. Joao A. Bastos & Jorge Caiado, 2009. "Clustering financial time series with variance ratio statistics," CEMAPRE Working Papers 0904, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. B. Lafuente-Rego & P. D’Urso & J. A. Vilar, 2020. "Robust fuzzy clustering based on quantile autocovariances," Statistical Papers, Springer, vol. 61(6), pages 2393-2448, December.
    2. Caiado, Jorge & Lúcio, Francisco, 2023. "Stock market forecasting accuracy of asymmetric GARCH models during the COVID-19 pandemic," The North American Journal of Economics and Finance, Elsevier, vol. 68(C).
    3. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2015. "Clustering of time series via non-parametric tail dependence estimation," Statistical Papers, Springer, vol. 56(3), pages 701-721, August.
    4. Sipan Aslan & Ceylan Yozgatligil & Cem Iyigun, 2018. "Temporal clustering of time series via threshold autoregressive models: application to commodity prices," Annals of Operations Research, Springer, vol. 260(1), pages 51-77, January.
    5. Albino, Andreia & Caiado, Jorge & Crato, Nuno, 2024. "Time series clustering using fragmented autocorrelations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 650(C).
    6. Gautier Marti & Frank Nielsen & Miko{l}aj Bi'nkowski & Philippe Donnat, 2017. "A review of two decades of correlations, hierarchies, networks and clustering in financial markets," Papers 1703.00485, arXiv.org, revised Nov 2020.
    7. Pierpaolo D’Urso & Livia Giovanni & Riccardo Massari, 2021. "Trimmed fuzzy clustering of financial time series based on dynamic time warping," Annals of Operations Research, Springer, vol. 299(1), pages 1379-1395, April.
    8. Abdollahi, Hooman & Junttila, Juha-Pekka & Lehkonen, Heikki, 2024. "Clustering asset markets based on volatility connectedness to political news," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 93(C).
    9. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2014. "Clustering of financial time series in risky scenarios," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 8(4), pages 359-376, December.
    10. Galagedera, Don U.A., 2013. "A new perspective of equity market performance," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 333-357.
    11. Erniel B. Barrios & Paolo Victor T. Redondo, 2021. "Nonparametric Test for Volatility in Clustered Multiple Time Series," Papers 2104.14412, arXiv.org, revised May 2024.
    12. Jorge Caiado & Nuno Crato & Pilar Poncela, 2020. "A fragmented-periodogram approach for clustering big data time series," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(1), pages 117-146, March.
    13. Lúcio, Francisco & Caiado, Jorge, 2022. "COVID-19 and Stock Market Volatility: A Clustering Approach for S&P 500 Industry Indices," Finance Research Letters, Elsevier, vol. 49(C).
    14. Dias, José G. & Vermunt, Jeroen K. & Ramos, Sofia, 2015. "Clustering financial time series: New insights from an extended hidden Markov model," European Journal of Operational Research, Elsevier, vol. 243(3), pages 852-864.
    15. João A. Bastos & Jorge Caiado, 2021. "On the classification of financial data with domain agnostic features," Working Papers REM 2021/0185, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    16. Ekaterina Dorodnykh, 2013. "What Drives Stock Exchange Integration?," International Journal of Business and Economic Sciences Applied Research (IJBESAR), Democritus University of Thrace (DUTH), Kavala Campus, Greece, vol. 6(2), pages 47-79, September.
    17. Anna CZAPKIEWICZ & Pawel MAJDOSZ, 2014. "Grouping Stock Markets with Time-Varying Copula-GARCH Model," Czech Journal of Economics and Finance (Finance a uver), Charles University Prague, Faculty of Social Sciences, vol. 64(2), pages 144-159, March.

  10. Joao A. Bastos, 2009. "Forecasting bank loans loss-given-default," CEMAPRE Working Papers 0901, Centre for Applied Mathematics and Economics (CEMAPRE), School of Economics and Management (ISEG), Technical University of Lisbon.

    Cited by:

    1. Yuta Tanoue & Satoshi Yamashita & Hideaki Nagahata, 2020. "Comparison study of two-step LGD estimation model with probability machines," Risk Management, Palgrave Macmillan, vol. 22(3), pages 155-177, September.
    2. Morne Joubert & Tanja Verster & Helgard Raubenheimer & Willem D. Schutte, 2021. "Adapting the Default Weighted Survival Analysis Modelling Approach to Model IFRS 9 LGD," Risks, MDPI, vol. 9(6), pages 1-17, June.
    3. Alessandro Girardi & Marco Ventura, 2021. "Measuring credit crunch in Italy: evidence from a survey-based indicator," Annals of Operations Research, Springer, vol. 299(1), pages 567-592, April.
    4. Hussain, Inayat & Durand, Robert B. & Harris, Mark N., 2016. "Default resolution and access to fresh credit in an emerging market," Pacific-Basin Finance Journal, Elsevier, vol. 39(C), pages 256-274.
    5. Joseph L. Breeden, 2024. "An Age–Period–Cohort Framework for Profit and Profit Volatility Modeling," Mathematics, MDPI, vol. 12(10), pages 1-23, May.
    6. Aneta Ptak-Chmielewska & Paweł Kopciuszewski, 2023. "Application of the Bayesian approach in loss given default modelling," Bank i Kredyt, Narodowy Bank Polski, vol. 54(6), pages 625-650.
    7. Chen, Rongda & Zhou, Hanxian & Jin, Chenglu & Zheng, Wei, 2019. "Modeling of recovery rate for a given default by non-parametric method," Pacific-Basin Finance Journal, Elsevier, vol. 57(C).
    8. Gürtler, Marc & Hibbeln, Martin Thomas & Usselmann, Piet, 2018. "Exposure at default modeling – A theoretical and empirical assessment of estimation approaches and parameter choice," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 176-188.
    9. Seidler, Jakub & Konečný, Tomáš & Belyaeva, Aelita & Belyaev, Konstantin, 2017. "The time dimension of the links between loss given default and the macroeconomy," Working Paper Series 2037, European Central Bank.
    10. 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).
    11. Toshiro Masahiro & Tasaki Masao & Hikidera Yusuke & Hibiki Norio, 2019. "Estimating the Recovery Rates for Unsecured Loans to Small Sized Firms," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 13(2), pages 1-26, July.
    12. Nazemi, Abdolreza & Rezazadeh, Hani & Fabozzi, Frank J. & Höchstötter, Markus, 2022. "Deep learning for modeling the collection rate for third-party buyers," International Journal of Forecasting, Elsevier, vol. 38(1), pages 240-252.
    13. João Bastos, 2014. "Ensemble Predictions of Recovery Rates," Journal of Financial Services Research, Springer;Western Finance Association, vol. 46(2), pages 177-193, October.
    14. TOBBACK, Ellen & MARTENS, David & VAN GESTEL, Tony & BAESENS, Bart, 2012. "Forecasting loss given default models: Impact of account characteristics and the macroeconomic state," Working Papers 2012019, University of Antwerp, Faculty of Business and Economics.
    15. Georgescu, Oana-Maria & Ponte Marques, Aurea & Galow, Benjamin, 2024. "Loss-given-default and macroeconomic conditions," Working Paper Series 2954, European Central Bank.
    16. Cheng, Dan & Cirillo, Pasquale, 2018. "A reinforced urn process modeling of recovery rates and recovery times," Journal of Banking & Finance, Elsevier, vol. 96(C), pages 1-17.
    17. Raffaella Calabrese, 2012. "Regression Model for Proportions with Probability Masses at Zero and One," Working Papers 201209, Geary Institute, University College Dublin.
    18. Patrick Weber & K. Valerie Carl & Oliver Hinz, 2024. "Applications of Explainable Artificial Intelligence in Finance—a systematic review of Finance, Information Systems, and Computer Science literature," Management Review Quarterly, Springer, vol. 74(2), pages 867-907, June.
    19. Diana Bonfim & Daniel Dias, 2010. "Access to Bank Credit after Corporate Default," Economic Bulletin and Financial Stability Report Articles and Banco de Portugal Economic Studies, Banco de Portugal, Economics and Research Department.
    20. Guglielmo Maria Caporale & Alessandro Girardi, 2011. "Price Discovery and Trade Fragmentation in a Multi-Market Environment: Evidence from the MTS System," Discussion Papers of DIW Berlin 1139, DIW Berlin, German Institute for Economic Research.
    21. Ellis Kofi, Akwaa-Sekyi & Portia, Bosompra, 2015. "Determinants of business loan default in Ghana," MPRA Paper 71961, University Library of Munich, Germany.
    22. Tanoue, Yuta & Kawada, Akihiro & Yamashita, Satoshi, 2017. "Forecasting loss given default of bank loans with multi-stage model," International Journal of Forecasting, Elsevier, vol. 33(2), pages 513-522.
    23. Sopitpongstorn, Nithi & Silvapulle, Param & Gao, Jiti & Fenech, Jean-Pierre, 2021. "Local logit regression for loan recovery rate," Journal of Banking & Finance, Elsevier, vol. 126(C).
    24. Christophe Hurlin & Jérémy Leymarie & Antoine Patin, 2018. "Loss functions for LGD model comparison," Working Papers halshs-01516147, HAL.
    25. Krüger, Steffen & Rösch, Daniel, 2017. "Downturn LGD modeling using quantile regression," Journal of Banking & Finance, Elsevier, vol. 79(C), pages 42-56.
    26. Nithi Sopitpongstorn & Param Silvapulle & Jiti Gao, 2017. "Local logit regression for recovery rate," Monash Econometrics and Business Statistics Working Papers 19/17, Monash University, Department of Econometrics and Business Statistics.
    27. Gürtler, Marc & Hibbeln, Martin, 2013. "Improvements in loss given default forecasts for bank loans," Journal of Banking & Finance, Elsevier, vol. 37(7), pages 2354-2366.
    28. Bonfim, Diana & Dias, Daniel A. & Richmond, Christine, 2012. "What happens after corporate default? Stylized facts on access to credit," Journal of Banking & Finance, Elsevier, vol. 36(7), pages 2007-2025.
    29. Lionel Sopgoui, 2024. "Impact of Climate transition on Credit portfolio's loss with stochastic collateral," Papers 2408.13266, arXiv.org, revised May 2025.
    30. 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).
    31. Stefan Hlawatsch & Sebastian Ostrowski, 2010. "Simulation and Estimation of Loss Given Default," FEMM Working Papers 100010, Otto-von-Guericke University Magdeburg, Faculty of Economics and Management.
    32. Wojciech Starosta, 2020. "Modelling Recovery Rate for Incomplete Defaults Using Time Varying Predictors," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 12(2), pages 195-225, June.
    33. Nazemi, Abdolreza & Heidenreich, Konstantin & Fabozzi, Frank J., 2018. "Improving corporate bond recovery rate prediction using multi-factor support vector regressions," European Journal of Operational Research, Elsevier, vol. 271(2), pages 664-675.
    34. Tong, Edward N.C. & Mues, Christophe & Thomas, Lyn, 2013. "A zero-adjusted gamma model for mortgage loan loss given default," International Journal of Forecasting, Elsevier, vol. 29(4), pages 548-562.
    35. Girardi, Alessandro & Ventura, Marco & Margani, Patrizia, 2018. "An Indicator of Credit Crunch using Italian Business Surveys," MPRA Paper 88839, University Library of Munich, Germany.
    36. Salvatore D. Tomarchio & Antonio Punzo, 2019. "Modelling the loss given default distribution via a family of zero‐and‐one inflated mixture models," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 182(4), pages 1247-1266, October.
    37. Qi, Min & Zhao, Xinlei, 2011. "Comparison of modeling methods for Loss Given Default," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 2842-2855, November.
    38. Chen, Xiaowei & Wang, Gang & Zhang, Xiangting, 2019. "Modeling recovery rate for leveraged loans," Economic Modelling, Elsevier, vol. 81(C), pages 231-241.
    39. Konstantin Belyaev & Aelita Belyaeva & Tomas Konecny & Jakub Seidler & Martin Vojtek, 2012. "Macroeconomic Factors as Drivers of LGD Prediction: Empirical Evidence from the Czech Republic," Working Papers 2012/12, Czech National Bank, Research and Statistics Department.
    40. Marc Gürtler & Marvin Zöllner, 2023. "Heterogeneities among credit risk parameter distributions: the modality defines the best estimation method," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 45(1), pages 251-287, March.
    41. Diana Bonfim & Daniel Dias, 2011. "What Happens After Default? Stylized Facts on Access to Credit," Working Papers w201101, Banco de Portugal, Economics and Research Department.
    42. Betz, Jennifer & Kellner, Ralf & Rösch, Daniel, 2016. "What drives the time to resolution of defaulted bank loans?," Finance Research Letters, Elsevier, vol. 18(C), pages 7-31.
    43. Hibbeln, Martin & Gürtler, Marc, 2011. "Pitfalls in modeling loss given default of bank loans," Working Papers IF35V1, Technische Universität Braunschweig, Institute of Finance.
    44. Croux, Christophe & Jagtiani, Julapa & Korivi, Tarunsai & Vulanovic, Milos, 2020. "Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform," Journal of Economic Behavior & Organization, Elsevier, vol. 173(C), pages 270-296.
    45. Xia, Yufei & Zhao, Junhao & He, Lingyun & Li, Yinguo & Yang, Xiaoli, 2021. "Forecasting loss given default for peer-to-peer loans via heterogeneous stacking ensemble approach," International Journal of Forecasting, Elsevier, vol. 37(4), pages 1590-1613.
    46. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Is it obligor or instrument that explains recovery rate: Evidence from US corporate bond," Journal of Financial Stability, Elsevier, vol. 28(C), pages 1-15.
    47. Ruey-Ching Hwang & Huimin Chung & C. K. Chu, 2016. "A Two-Stage Probit Model for Predicting Recovery Rates," Journal of Financial Services Research, Springer;Western Finance Association, vol. 50(3), pages 311-339, December.
    48. Yurchenko, Yurii, 2019. "The impact of macroeconomic factors on collateral value within the framework of expected credit loss calculation," MPRA Paper 97135, University Library of Munich, Germany.
    49. Natalia Nehrebecka, 2019. "Bank loans recovery rate in commercial banks: A case study of non-financial corporations," Zbornik radova Ekonomskog fakulteta u Rijeci/Proceedings of Rijeka Faculty of Economics, University of Rijeka, Faculty of Economics and Business, vol. 37(1), pages 139-172.
    50. Ruey-Ching Hwang & Chih-Kang Chu & Kaizhi Yu, 2021. "Predicting the Loss Given Default Distribution with the Zero-Inflated Censored Beta-Mixture Regression that Allows Probability Masses and Bimodality," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(3), pages 143-172, June.
    51. Majid Bazarbash, 2019. "FinTech in Financial Inclusion: Machine Learning Applications in Assessing Credit Risk," IMF Working Papers 2019/109, International Monetary Fund.
    52. Olson, Luke M. & Qi, Min & Zhang, Xiaofei & Zhao, Xinlei, 2021. "Machine learning loss given default for corporate debt," Journal of Empirical Finance, Elsevier, vol. 64(C), pages 144-159.
    53. Stanhouse, Bryan & Schwarzkopf, Al & Ingram, Matt, 2011. "A computational approach to pricing a bank credit line," Journal of Banking & Finance, Elsevier, vol. 35(6), pages 1341-1351, June.
    54. Hong Wang & Catherine S. Forbes & Jean-Pierre Fenech & John Vaz, 2018. "The determinants of bank loan recovery rates in good times and bad - new evidence," Papers 1804.07022, arXiv.org.
    55. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2015. "Support vector regression for loss given default modelling," European Journal of Operational Research, Elsevier, vol. 240(2), pages 528-538.
    56. Abu, Benjamin Musah & Domanban, Paul Bata & Haruna, Issahaku, 2017. "Microcredit Loan Repayment Default among Small Scale Enterprises: A Double Hurdle Approach," MPRA Paper 101576, University Library of Munich, Germany, revised 12 Mar 2017.
    57. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    58. Altman, Edward I. & Kalotay, Egon A., 2014. "Ultimate recovery mixtures," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 116-129.
    59. Cheng, Hui & Jiang, Cuiqing & Wang, Zhao & Ni, Xiaoya, 2025. "Multi-view locally weighted regression for loss given default forecasting," International Journal of Forecasting, Elsevier, vol. 41(1), pages 290-306.
    60. Jobst, Rainer & Kellner, Ralf & Rösch, Daniel, 2020. "Bayesian loss given default estimation for European sovereign bonds," International Journal of Forecasting, Elsevier, vol. 36(3), pages 1073-1091.
    61. Yao, Xiao & Crook, Jonathan & Andreeva, Galina, 2017. "Enhancing two-stage modelling methodology for loss given default with support vector machines," European Journal of Operational Research, Elsevier, vol. 263(2), pages 679-689.
    62. Loterman, Gert & Brown, Iain & Martens, David & Mues, Christophe & Baesens, Bart, 2012. "Benchmarking regression algorithms for loss given default modeling," International Journal of Forecasting, Elsevier, vol. 28(1), pages 161-170.
    63. Nazemi, Abdolreza & Fatemi Pour, Farnoosh & Heidenreich, Konstantin & Fabozzi, Frank J., 2017. "Fuzzy decision fusion approach for loss-given-default modeling," European Journal of Operational Research, Elsevier, vol. 262(2), pages 780-791.
    64. Miller, Patrick & Töws, Eugen, 2018. "Loss given default adjusted workout processes for leases," Journal of Banking & Finance, Elsevier, vol. 91(C), pages 189-201.
    65. Raffaella Calabrese, 2012. "Estimating bank loans loss given default by generalized additive models," Working Papers 201224, Geary Institute, University College Dublin.
    66. Baker, Rose D. & McHale, Ian G., 2018. "Time-varying ratings for international football teams," European Journal of Operational Research, Elsevier, vol. 267(2), pages 659-666.
    67. Kaposty, Florian & Kriebel, Johannes & Löderbusch, Matthias, 2020. "Predicting loss given default in leasing: A closer look at models and variable selection," International Journal of Forecasting, Elsevier, vol. 36(2), pages 248-266.
    68. Betz, Jennifer & Kellner, Ralf & Rösch, Daniel, 2018. "Systematic Effects among Loss Given Defaults and their Implications on Downturn Estimation," European Journal of Operational Research, Elsevier, vol. 271(3), pages 1113-1144.
    69. Pascal François, 2019. "The Determinants of Market-Implied Recovery Rates," Risks, MDPI, vol. 7(2), pages 1-15, May.
    70. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Corporate Loan Recovery Rates under Downturn Conditions in a Developing Economy: Evidence from Zimbabwe," Risks, MDPI, vol. 10(10), pages 1-24, October.
    71. 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.
    72. Peter-Hendrik Ingermann & Frederik Hesse & Christian Bélorgey & Andreas Pfingsten, 2016. "The recovery rate for retail and commercial customers in Germany: a look at collateral and its adjusted market values," Business Research, Springer;German Academic Association for Business Research, vol. 9(2), pages 179-228, August.
    73. Florian Kaposty & Philipp Klein & Matthias Löderbusch & Andreas Pfingsten, 2022. "Loss given default in SME leasing," Review of Managerial Science, Springer, vol. 16(5), pages 1561-1597, July.
    74. Hartmann-Wendels, Thomas & Miller, Patrick & Töws, Eugen, 2014. "Loss given default for leasing: Parametric and nonparametric estimations," Journal of Banking & Finance, Elsevier, vol. 40(C), pages 364-375.
    75. Han, Chulwoo & Jang, Youngmin, 2013. "Effects of debt collection practices on loss given default," Journal of Banking & Finance, Elsevier, vol. 37(1), pages 21-31.
    76. So Sohn & Yoon Kim, 2013. "Behavioral credit scoring model for technology-based firms that considers uncertain financial ratios obtained from relationship banking," Small Business Economics, Springer, vol. 41(4), pages 931-943, December.
    77. Zaid Altaf & Farooq Shah, 2025. "Effects of credit score literacy and psychological traits on borrowing behavior: evidence from India using PLS-SEM," Future Business Journal, Springer, vol. 11(1), pages 1-24, December.
    78. Thamayanthi Chellathurai, 2017. "Probability Density Of Recovery Rate Given Default Of A Firm’S Debt And Its Constituent Tranches," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(04), pages 1-34, June.
    79. Rakshith Bhandary & Bidyut Kumar Ghosh, 2025. "Credit Card Default Prediction: An Empirical Analysis on Predictive Performance Using Statistical and Machine Learning Methods," JRFM, MDPI, vol. 18(1), pages 1-20, January.
    80. Ellen Tobback & David Martens & Tony Van Gestel & Bart Baesens, 2014. "Forecasting Loss Given Default models: impact of account characteristics and the macroeconomic state," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 65(3), pages 376-392, March.
    81. Justin A. Sirignano & Gerry Tsoukalas & Kay Giesecke, 2016. "Large-Scale Loan Portfolio Selection," Operations Research, INFORMS, vol. 64(6), pages 1239-1255, December.
    82. Pesola, Jarmo, 2011. "Joint effect of financial fragility and macroeconomic shocks on bank loan losses: Evidence from Europe," Journal of Banking & Finance, Elsevier, vol. 35(11), pages 3134-3144, November.
    83. Aneta Ptak-Chmielewska & Paweł Kopciuszewski & Anna Matuszyk, 2023. "Application of the kNN-Based Method and Survival Approach in Estimating Loss Given Default for Unresolved Cases," Risks, MDPI, vol. 11(2), pages 1-14, February.
    84. Hurlin, Christophe & Leymarie, Jérémy & Patin, Antoine, 2018. "Loss functions for Loss Given Default model comparison," European Journal of Operational Research, Elsevier, vol. 268(1), pages 348-360.
    85. Christoph Memmel & Angelika Sachs & Ingrid Stein, 2012. "Contagion in the Interbank Market with Stochastic Loss Given Default," International Journal of Central Banking, International Journal of Central Banking, vol. 8(3), pages 177-206, September.
    86. Antão, Paula & Lacerda, Ana, 2011. "Capital requirements under the credit risk-based framework," Journal of Banking & Finance, Elsevier, vol. 35(6), pages 1380-1390, June.

  11. Bastos, Joao, 2007. "Credit scoring with boosted decision trees," MPRA Paper 8034, University Library of Munich, Germany.

    Cited by:

    1. Fitzpatrick, Trevor & Mues, Christophe, 2016. "An empirical comparison of classification algorithms for mortgage default prediction: evidence from a distressed mortgage market," European Journal of Operational Research, Elsevier, vol. 249(2), pages 427-439.
    2. Kim, Soo Y. & Upneja, Arun, 2014. "Predicting restaurant financial distress using decision tree and AdaBoosted decision tree models," Economic Modelling, Elsevier, vol. 36(C), pages 354-362.
    3. Zhang, Zhiwang & Gao, Guangxia & Shi, Yong, 2014. "Credit risk evaluation using multi-criteria optimization classifier with kernel, fuzzification and penalty factors," European Journal of Operational Research, Elsevier, vol. 237(1), pages 335-348.
    4. Marco Locurcio & Francesco Tajani & Pierluigi Morano & Debora Anelli & Benedetto Manganelli, 2021. "Credit Risk Management of Property Investments through Multi-Criteria Indicators," Risks, MDPI, vol. 9(6), pages 1-23, June.

Articles

  1. Bastos, João A. & Matos, Sara M., 2022. "Explainable models of credit losses," European Journal of Operational Research, Elsevier, vol. 301(1), pages 386-394.
    See citations under working paper version above.
  2. João A. Bastos, 2022. "Predicting Credit Scores with Boosted Decision Trees," Forecasting, MDPI, vol. 4(4), pages 1-11, November.

    Cited by:

    1. Guilherme Armando de Almeida Pereira & Kiara de Deus Demura, 2025. "Can Simple Balancing Algorithms Improve School Dropout Forecasting? The Case of the State Education Network of Espírito Santo, Brazil," Forecasting, MDPI, vol. 7(4), pages 1-19, October.

  3. João A. Bastos, 2019. "Forecasting the capacity of mobile networks," Telecommunication Systems: Modelling, Analysis, Design and Management, Springer, vol. 72(2), pages 231-242, October.
    See citations under working paper version above.
  4. João A. Bastos & Joaquim J. S. Ramalho, 2016. "Nonparametric Models Of Financial Leverage Decisions," Bulletin of Economic Research, Wiley Blackwell, vol. 68(4), pages 348-366, October.
    See citations under working paper version above.
  5. João A. Bastos & Jorge Caiado, 2014. "Clustering financial time series with variance ratio statistics," Quantitative Finance, Taylor & Francis Journals, vol. 14(12), pages 2121-2133, December.
    See citations under working paper version above.
  6. João Bastos, 2014. "Ensemble Predictions of Recovery Rates," Journal of Financial Services Research, Springer;Western Finance Association, vol. 46(2), pages 177-193, October.
    See citations under working paper version above.
  7. Bastos, João A. & Caiado, Jorge, 2011. "Recurrence quantification analysis of global stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(7), pages 1315-1325.
    See citations under working paper version above.
  8. Bastos, João A., 2010. "Forecasting bank loans loss-given-default," Journal of Banking & Finance, Elsevier, vol. 34(10), pages 2510-2517, October.
    See citations under working paper version above.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.