Francesco Pattarin

Personal Details

First Name:	Francesco
Middle Name:
Last Name:	Pattarin
Suffix:
RePEc Short-ID:	ppa329
	http://personale.unimore.it/Rubrica/dettaglio/pattarin

Affiliation

Centro Studi di Banca e Finanza (CEFIN)
Dipartimento di Economia "Marco Biagi"
Università degli Studi di Modena e Reggio Emilia

Modena, Italy
http://www.cefin.unimore.it/
RePEc:edi:cbmodit (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.

Articles

1. Francesco Pattarin & Stefano Cosma, 2012. "Psychological determinants of consumer credit: the role of attitudes," Review of Behavioral Finance, Emerald Group Publishing Limited, vol. 4(2), pages 113-129, November.

   Cited by:

   1. A. Smurygina & M. Gagarina & А. Смурыгина & М. Гагарина, 2016. "Ограниченная Рациональность: Психологический Анализ Поведения Должников // Bounded Rationality: Psychological Analysis Of Debt Behaviour," Review of Business and Economics Studies // Review of Business and Economics Studies, Финансовый Университет // Financial University, vol. 4(1), pages 75-84.
   2. Boto Ferreira, Mário & Costa Pinto, Diego & Maurer Herter, Márcia & Soro, Jerônimo & Vanneschi, Leonardo & Castelli, Mauro & Peres, Fernando, 2021. "Using artificial intelligence to overcome over-indebtedness and fight poverty," Journal of Business Research, Elsevier, vol. 131(C), pages 411-425.

2. Pattarin, Francesco & Paterlini, Sandra & Minerva, Tommaso, 2004. "Clustering financial time series: an application to mutual funds style analysis," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 353-372, September.

   Cited by:

   1. Winker, Peter & Gilli, Manfred, 2004. "Applications of optimization heuristics to estimation and modelling problems," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 211-223, September.
   2. Alonso, A.M. & Berrendero, J.R. & Hernandez, A. & Justel, A., 2006. "Time series clustering based on forecast densities," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 762-776, November.
   3. Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can machine learning help to select portfolios of mutual funds?," Economics Working Papers 1772, Department of Economics and Business, Universitat Pompeu Fabra.
      • Victor DeMiguel & Javier Gil-Bazo & Francisco J. Nogales & André A. P. Santos, 2021. "Can Machine Learning Help to Select Portfolios of Mutual Funds?," Working Papers 1245, Barcelona School of Economics.
   4. 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.
   5. Takaya Fukui & Seisho Sato & Akihiko Takahashi, 2017. "Style analysis with particle filtering and generalized simulated annealing," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 4(02n03), pages 1-29, June.
   6. Liu, Shen & Maharaj, Elizabeth Ann & Inder, Brett, 2014. "Polarization of forecast densities: A new approach to time series classification," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 345-361.
   7. James Ming Chen & Mira Zovko & Nika Šimurina & Vatroslav Zovko, 2021. "Fear in a Handful of Dust: The Epidemiological, Environmental, and Economic Drivers of Death by PM 2.5 Pollution," IJERPH, MDPI, vol. 18(16), pages 1-59, August.
   8. 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.
   9. Firat, Aykut & Chatterjee, Sangit & Yilmaz, Mustafa, 2007. "Genetic clustering of social networks using random walks," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 6285-6294, August.
   10. Dhagash Mehta & Dhruv Desai & Jithin Pradeep, 2020. "Machine Learning Fund Categorizations," Papers 2006.00123, arXiv.org.
   11. Edoardo Otranto & Romana Gargano, 2015. "Financial clustering in presence of dominant markets," 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. 9(3), pages 315-339, September.
       • R. Gargano & E. Otranto, 2013. "Financial Clustering in Presence of Dominant Markets," Working Paper CRENoS 201318, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
   12. Pinar OKAN GOKTEN & Furkan BASER & Soner GOKTEN, 2017. "Using fuzzy c-means clustering algorithm in financial health scoring," The Audit Financiar journal, Chamber of Financial Auditors of Romania, vol. 15(147), pages 385-385.
   13. James Ming Chen & Mobeen Ur Rehman, 2021. "A Pattern New in Every Moment: The Temporal Clustering of Markets for Crude Oil, Refined Fuels, and Other Commodities," Energies, MDPI, vol. 14(19), pages 1-58, September.
   14. Chen, James Ming & Rehman, Mobeen Ur & Vo, Xuan Vinh, 2021. "Clustering commodity markets in space and time: Clarifying returns, volatility, and trading regimes through unsupervised machine learning," Resources Policy, Elsevier, vol. 73(C).
   15. Bracewell Paul J & Farhadieh Farinaz & Jowett Clint A & Forbes Don G. R. & Meyer Denny H, 2009. "Was Bradman Denied His Prime?," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 5(4), pages 1-26, October.
   16. Juan C. Duque & Xinyue Ye & David C. Folch, 2015. "spMorph: An exploratory space-time analysis tool for describing processes of spatial redistribution," Papers in Regional Science, Wiley Blackwell, vol. 94(3), pages 629-651, August.
   17. Marianna Lyra, 2010. "Heuristic Strategies in Finance – An Overview," Working Papers 045, COMISEF.
   18. Maharaj, Elizabeth A. & Alonso, Andres M., 2007. "Discrimination of locally stationary time series using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 879-895, October.
   19. Jin Zhang & Dietmar Maringer, 2010. "Asset Allocation under Hierarchical Clustering," Working Papers 036, COMISEF.
   20. Liu, Shen & Maharaj, Elizabeth Ann, 2013. "A hypothesis test using bias-adjusted AR estimators for classifying time series in small samples," Computational Statistics & Data Analysis, Elsevier, vol. 60(C), pages 32-49.
   21. E. Otranto, 2008. "Clustering Heteroskedastic Time Series by Model-Based Procedures," Working Paper CRENoS 200801, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
       • Otranto, Edoardo, 2008. "Clustering heteroskedastic time series by model-based procedures," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4685-4698, June.
   22. Luca De Angelis, 2013. "Latent class models for financial data analysis: some statistical developments," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(2), pages 227-242, June.
   23. Giovanni De Luca & Paola Zuccolotto, 2011. "A tail dependence-based dissimilarity measure for financial time series clustering," 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. 5(4), pages 323-340, December.
   24. De Luca Giovanni & Zuccolotto Paola, 2017. "A double clustering algorithm for financial time series based on extreme events," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 1-12, June.
   25. Stephanos Papadamou & Nikolaos A. Kyriazis & Lydia Mermigka, 2017. "Japanese Mutual Funds before and after the Crisis Outburst: A Style- and Performance-Analysis," IJFS, MDPI, vol. 5(1), pages 1-20, March.
   26. 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.
   27. E. Otranto, 2011. "Classification of Volatility in Presence of Changes in Model Parameters," Working Paper CRENoS 201113, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
   28. F. Lisi & E. Otranto, 2008. "Clustering Mutual Funds by Return and Risk Levels," Working Paper CRENoS 200813, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
   29. Mahmoudi, Mohammad Reza, 2021. "A computational technique to classify several fractional Brownian motion processes," Chaos, Solitons & Fractals, Elsevier, vol. 150(C).
   30. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.

3. Michele Lalla & Francesco Pattarin, 2001. "Unemployment Duration: An Analysis of Incomplete, Completed, and Multiple Spells in Emilia-Romagna," Quality & Quantity: International Journal of Methodology, Springer, vol. 35(2), pages 203-230, May.

   Cited by:

   1. Michele Lalla & Tommaso Minerva, 2001. "Duration models and neural networks to analyse unemployment spells," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3-4), pages 199-216.
   2. Chiara Gigliarano & Francesco Chelli, 2016. "Measuring inter-temporal intragenerational mobility: an application to the Italian labour market," Quality & Quantity: International Journal of Methodology, Springer, vol. 50(1), pages 89-102, January.

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