Abstract
In this study we aim at identifying companies influencing the performance of the stock market sector. We propose an approach for constructing the similarity between stock company profiles based on the estimates of the log return similarity of stock prices and on Fuzzy Spectral Modularity community detection method to infer the network hubs and significant communities and we applied it to the Italian stock market store. Experimental results show that companies in the same sector highly affect the price change of each other. Moreover, We notice a robust temporal stability of detected communities, and the short time correlation computed with the fuzzy rand index is strong.
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Mahmoud, H., Masulli, F., Resta, M., Rovetta, S., Abdulatif, A. (2015). Hubs and Communities Identification in Dynamical Financial Networks. In: Bassis, S., Esposito, A., Morabito, F. (eds) Advances in Neural Networks: Computational and Theoretical Issues. Smart Innovation, Systems and Technologies, vol 37. Springer, Cham. https://doi.org/10.1007/978-3-319-18164-6_10
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DOI: https://doi.org/10.1007/978-3-319-18164-6_10
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