Domenico Santoro

Metodologie statistiche per l'analisi e la gestione del rischio

LM31 - Ingegneria gestionale

Ricevimento previo appuntamento da concordare via mail

2025

• Santoro, D., Grilli, L., Sgarro, G.A., Colasanto, F., Villani, G. (2025). MCMC Approach for Stock Price Forecasting Using an Italian-BERT Model. In: Pollice, A., Mariani, P. (eds) Methodological and Applied Statistics and Demography II. SIS 2024. Italian Statistical Society Series on Advances in Statistics. Springer, Cham. doi: 10.1007/978-3-031-64350-7_96;

2024

• Di Bari, A., Grilli, L., Santoro, D. & Villani, G.  (2024). A new methodology to support wind investment decision: a combination of natural language processing and Monte Carlo option pricing technique. Decisions in Economics and Finance, doi: 10.1007/s10203-024-00486-6 (FASCIA A);
• Santoro, D., Ciano, T. & Ferrara, M. (2024). A comparison between machine and deep learning models on high stationarity data. Scientific Reports, 14, 19409, doi: 10.1038/s41598-024-70341-6 (FASCIA A);
• Di Bari, A., Grilli, L., Santoro, D. & Villani, G. (2024). A Combination of NLP and Monte Carlo Technique to Improve Wind Investment Decisions. In: Corazza, M., Gannon, F., Legros, F., Pizzi, C., Touzé, V. (eds) Mathematical and Statistical Methods for Actuarial Sciences and Finance. MAF 2024. Springer, Cham. doi: 10.1007/978-3-031-64273-9_20;
• Sgarro, G.A., Santoro, D. & Grilli, L.  (2024). Ant Colony Optimization for solving Directed Chinese Postman Problem. Neural Computing & Applications. 36, 17615–17630. doi: 10.1007/s00521-024-10052-1 (FASCIA A);
• Guarino, A., Santoro, D., Grilli, L., Zaccagnino, R. & Balbi, M. (2024). EvoFolio: a portfolio optimization method based on multi-objective evolutionary algorithms. Neural Computing & Applications. 36, 7221–7243. doi: 10.1007/s00521-024-09456-w (FASCIA A);
• Sgarro, G.A., Grilli, L. & Santoro, D. (2024). Optimal multivariate mixture: a genetic algorithm approach. Annals of Operations Research, doi: 10.1007/s10479-024-06045-x (FASCIA A);

2023

• Cappelletti, M. G., Caputo, R., Cariglia, M., Grilli, L., Russo, C., Santoro, D. & Sgarro, G. A. (2023). Harnessing the power of blockchain in the agri-food sector: a meta-analysis of current research and best practices. Applied Mathematical Sciences, 17(10), 477-501, doi: 10.12988/ams.2023.917473;
• Guarino, A., Grilli, L., Santoro, D., Messina, F. & Zaccagnino, R. (2024). On the efficacy of "herd behavior" in the commodities market: A neuro-fuzzy agent "herding" on deep learning traders. Applied Stochastic Models in Business and Industry. 40(2): 348-372. doi: 10.1002/asmb.2793 (FASCIA A);
• Cappelletti G.M., Grilli L., Russo C.,  & Santoro, D., (2023). Benchmarking Sustainable Mobility in Higher Education. Sustainability 2023, 15(6), 5190. doi: 10.3390/su15065190;
• Di Bari A., Santoro D., Tarrazon-Rodon M. A., & Villani G., (2024). The impact of polarity score on real option valuation for multistage projects. Quality & Quantity. 58, 57–76. doi: 10.1007/s11135-023-01635-6 (FASCIA A);

2022

• Cappelletti, G. M., Grilli, L., Santoro, D., & Russo, C. (2022). Machine learning and sustainable mobility: The case of the university of Foggia (Italy). Applied Sciences, 12(17):8774. doi: 10.3390/app12178774;
• Grilli, L., & Santoro, D. (2022). Forecasting Financial Time Series with Boltzmann Entropy through Neural Networks. 19, 665–681. Computational Management Sciences, doi: 10.1007/s10287-022-00430-2 (FASCIA A);
• Colasanto, F., Grilli, L., & Santoro, D. (2022). Directional derivatives in non-Hausdorff TVS: topological filter techniques without metric structures. Applied Mathematical Sciences, 16(5), 251-260. doi: 10.12988/ams.2022.916795;
• Colasanto, F., Grilli, L., Santoro, D., & Villani, G. (2022). A neural network contribute to reverse cryptographic processes in bitcoin systems: attention on SHA256. Applied Mathematical Sciences, 16(4), 215-232. doi: 10.12988/ams.2022.916778;
• Guarino, A., Grilli, L., Santoro, D., Messina, F. & Zaccagnino, R. (2022). To learn or not to learn? evaluating autonomous, adaptive, automated traders in cryptocurrencies financial bubbles. 34, 20715–20756. Neural Computing & Applications, doi: 10.1007/s00521-022-07543-4 (FASCIA A);
• Colasanto, F., Grilli, L., Santoro, D., & Villani, G. (2022). Bert’s sentiment score for portfolio optimization: A fine-tuned view in black and litterman model. 34, 17507–17521. Neural Computing & Applications, doi: 10.1007/s00521-022-07403-1 (FASCIA A);
• Santoro, D. & Grilli, L. (2022). Generative adversarial network to evaluate quantity of information in financial markets. 34, 17473–17490. Neural Computing & Applications, doi: 10.1007/s00521-022-07401-3 (FASCIA A e finalista Best Paper AMASES 2023);
• Colasanto, F., Grilli, L., Santoro, D., & Villani, G. (2022). AlBERTino for stock price prediction: A Gibbs sampling approach. Information Sciences, 597, 341–357. doi: 10.1016/j.ins.2022.03.051;
• Santoro, D., & Villani, G. (2022). Real R&D options under sentimental information analysis. In M. Corazza (Ed.), MAF 2022, Mathematical and Statistical Methods for Actuarial Sciences and Finance (Chap. 67, pp. 1–6). 10.1007/978-3-030-99638- 3_67. IT: Springer Nature Switzerland AG;

2021

• Cappelletti, G. M., Grilli, L., Santoro, D., & Russo, C. (2021). Sustainable mobility in universities: The case of the university of Foggia (italy). Environments, 8, 57. doi: 10.3390/environments8060057;
• Grilli, L., & Santoro, D. (2021). A statistical ensemble based approach for entropy in cryptocurrencies markets. Chaotic Modeling and Simulation - CMSIM, ISSN: 2241-0503, 2, 91–103;
• Grilli, L., & Santoro, D. (2021). Cryptocurrencies markets and entropy: A statistical ensemble based approach. Applied Mathematical Sciences, 15(7), 297–320. doi: 10.12988/ams.2021.914488;
• Casalino, G., Grilli, L., Limone, P., Santoro, D., & Schicchi, D. (2021). Deep learning for knowledge tracing in learning analytics: An overview. In P. Limone (Ed.), Proceedings of the First Workshop on Technology Enhanced Learning Environments for Blended Education - The Italian e-learning Conference 2021 (teleXbe 2021) (Vol. 2817), IT: CEUR Workshop Proceedings - ISSN:1613-0073;
• Grilli, L., & Santoro, D. (2021). A statistical ensemble based approach for entropy in cryptocurrencies markets. In Y. D. Christos H. Skiadas (Ed.), Proceedings of the 13th International Chaotic Modeling and Simulation International Conference (p. 1091). ISSN: 2213-8684. GR: Springer International Publishing;
• Grilli, L., & Santoro, D. (2021). Machine-deep learning and finance: A review of recent results. In E. Set (Ed.), Proceedings book of the 4th International Conference on Mathematical and Related Sciences: Current Trends and Developments (ICMRS 2021) (p. 4). ISBN: 978-605-70978-1-1, TR.

https://www.unimercatorum.iris.cineca.it/cris/rp/rp01643

• Socio Associazione per la Matematica Applicata alle Scienze Economiche e Sociali (AMASES)
• Socio Società Italiana di Statistica (SIS)
• Socio Associazione De Componendis Cifris (De Cifris)

Membro dell'Editorial Board della rivista Applied Mathematical Sciences (ISSN 1314-7552, online)

• Natural Language Processing (NLP)
• Machine Learning (ML) - Deep Learning (DL)
• Analisi di serie temporali
• Algoritmi di ottimizzazione
• Meta-euristiche
• Finanza matematica