An information theory approach to stock market liquidity
A novel methodology is introduced to dynamically analyze the complex scaling behavior of financial data across various investment horizons. This approach comprises two steps: (a) the application of a distribution-based method for the estimation of time-varying self-similarity matrices. These matrice...
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| Veröffentlicht in: | Chaos (Woodbury, N.Y.) N.Y.), 2024-06, Vol.34 (6) |
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| container_title | Chaos (Woodbury, N.Y.) |
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| creator | Bianchi, S. Bruni, V. Frezza, M. Marconi, S. Pianese, A. Vantaggi, B. Vitulano, D. |
| description | A novel methodology is introduced to dynamically analyze the complex scaling behavior of financial data across various investment horizons. This approach comprises two steps: (a) the application of a distribution-based method for the estimation of time-varying self-similarity matrices. These matrices consist of entries that represent the scaling parameters relating pairs of distributions of price changes constructed for different temporal scales (or investment horizons); (b) the utilization of information theory, specifically the Normalized Compression Distance, to quantify the relative complexity and ascertain the similarities between pairs of self-similarity matrices. Through this methodology, distinct patterns can be identified and they may delineate the levels and the composition of market liquidity. An application to the U.S. stock index S&P500 shows the effectiveness of the proposed methodology. |
| doi_str_mv | 10.1063/5.0213429 |
| format | Article |
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| ispartof | Chaos (Woodbury, N.Y.), 2024-06, Vol.34 (6) |
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| language | eng |
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| source | AIP Journals Complete |
| subjects | Complexity Information theory Methodology Self-similarity |
| title | An information theory approach to stock market liquidity |
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