Shannon entropy to quantify complexity in the financial market
Alexis Rodriguez Carranza, Jos\'e Luis Ponte Bejarano, Juan Carlos, Ponte Bejarano, Segundo Eloy Soto Abanto
TL;DR
This paper uses Shannon entropy to quantify the complexity of financial market dynamics in Peru by analyzing share price series and reconstructing the underlying information traffic.
Contribution
It introduces a method to measure market complexity through Shannon entropy applied to reconstructed financial dynamics in the Peruvian stock exchange.
Findings
Shannon entropy effectively captures market complexity.
Reconstructed dynamics reveal underlying information traffic.
Numerical simulations support the entropy-based analysis.
Abstract
In this paper we study the complexity in the information traffic that occurs in the peruvian financial market, using the Shannon entropy. Different series of prices of shares traded on the Lima stock exchange are used to reconstruct the unknown dynamics. We present numerical simulations on the reconstructed dynamics and we calculate the Shannon entropy to measure its complexity
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Taxonomy
TopicsComplex Systems and Time Series Analysis · Chaos control and synchronization · Evolutionary Algorithms and Applications