Technical analysis on the bitcoin market: trading opportunities or investors’ pitfall? M Resta, P Pagnottoni, ME De Giuli Risks 8 (2), 44, 2020 | 36 | 2020 |
Early Warning Systems: an approach via Self Organizing Maps with applications to emergent markets B Apolloni New Directions in Neural Networks: 18th Italian Workshop on Neural Networks …, 2008 | 27 | 2008 |
Unsupervised neural networks for clustering emergent patient flows M Resta, M Sonnessa, E Tànfani, A Testi Operations Research for Health Care 18, 41-51, 2018 | 22 | 2018 |
Towards an artificial technical analysis of financial markets M Resta Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural …, 2000 | 21 | 2000 |
Is VIX still the investor fear gauge? Evidence for the US and BRIC markets M Neffelli, M Resta arXiv preprint arXiv:1806.07556, 2018 | 20 | 2018 |
Hurst exponent and its applications in time-series analysis M Resta Recent Patents on Computer Science 5 (3), 211-219, 2012 | 20 | 2012 |
Seize the (intra) day: Features selection and rules extraction for tradings on high-frequency data M Resta Neurocomputing 72 (16-18), 3413-3427, 2009 | 18 | 2009 |
Neural networks in accounting: clustering firm performance using financial reporting data RP Dameri, R Garelli, M Resta Journal of Information Systems 34 (2), 149-166, 2020 | 15 | 2020 |
An agent-based simulator driven by variants of self-organizing maps M Resta Neurocomputing 147, 207-224, 2015 | 14 | 2015 |
Graph mining based SOM: a tool to analyze economic stability M Resta Applications of self-organizing maps, 1-25, 2012 | 14 | 2012 |
A computational approach for the health care market M Montefiori, M Resta Health care management science 12, 344-350, 2009 | 14 | 2009 |
Computational intelligence paradigms in economic and financial decision making M Resta Springer International Publishing, 2016 | 11 | 2016 |
Assessing the efficiency of health care providers: a SOM perspective M Resta Advances in Self-Organizing Maps: 8th International Workshop, WSOM 2011 …, 2011 | 11 | 2011 |
Yield curve estimation under extreme conditions: do RBF networks perform better? A Cafferata, PG Giribone, M Neffelli, M Resta Neural Advances in Processing Nonlinear Dynamic Signals 27, 241-251, 2019 | 10 | 2019 |
The shape of crisis lessons from self organizing maps M Resta Computational Intelligence Systems in Industrial Engineering: With Recent …, 2012 | 9 | 2012 |
Reliability and convergence on Kohonen maps: an empirical study MC Adorno, M Resta International Conference on Knowledge-Based and Intelligent Information and …, 2004 | 9 | 2004 |
Hospital emergency department: an insight by means of quantitative methods P Cremonesi, M Montefiori, M Resta The Open Pharmacoeconomics & Health Economics Journal 4 (1), 2012 | 8 | 2012 |
Modeling the yield curve of BRICS countries: Parametric vs. machine learning techniques O Castello, M Resta Risks 10 (2), 36, 2022 | 7 | 2022 |
Varsom: A tool to monitor markets' stability based on value at risk and self‐organizing maps M Resta Intelligent Systems in Accounting, Finance and Management 23 (1-2), 47-64, 2016 | 7 | 2016 |
A hybrid neural network system for trading financial markets M Resta Visual Explorations in Finance: with Self-Organizing Maps, 106-116, 1998 | 7 | 1998 |