Algorithmic Complexity of Real Financial Markets

A new approach to the understanding of complex behavior of financial markets index using tools from thermodynamics and statistical physics is developed. Physical complexity, a quantity rooted in the Kolmogorov–Chaitin theory is applied to binary sequences built up from real time series of financial markets indexes. The study is based on NASDAQ and Mexican IPC data. Different behaviors of this quantity are shown when applied to the intervals of series placed before crashes and to intervals when no financial turbulence is observed. The connection between our results and the efficient market hypothesis is discussed.

• Publication year

  2000

• Venue

  Physica A-statistical Mechanics and Its Applications

• Publication date

  2000-05-25

• Fields of study

  Mathematics, Physics, Computer Science, Economics

• Identifiers
  DOI 10.1016/S0378-4371(01)00434-4arXiv cond-mat/0005441
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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