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Algorithmic complexity theory and the relative efficiency of financial markets

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  • Giglio, Ricardo
  • Matsushita, Raul
  • Figueiredo, Annibal
  • Gleria, Iram
  • Da Silva, Sergio

Abstract

Financial economists usually assess market efficiency in absolute terms. This is to be viewed as a shortcoming. One way of dealing with the relative efficiency of markets is to resort to the efficiency interpretation provided by algorithmic complexity theory. We employ such an approach in order to rank 36 stock exchanges, 37 individual company stocks, and 19 US dollar exchange rates in terms of their relative efficiency.

Suggested Citation

  • Giglio, Ricardo & Matsushita, Raul & Figueiredo, Annibal & Gleria, Iram & Da Silva, Sergio, 2008. "Algorithmic complexity theory and the relative efficiency of financial markets," MPRA Paper 8704, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:8704
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    References listed on IDEAS

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    1. Oh, Gabjin & Kim, Seunghwan & Eom, Cheoljun, 2007. "Market efficiency in foreign exchange markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 209-212.
    2. Meredith Beechey & David Gruen & James Vickery, 2000. "The Efficient Market Hypothesis: A Survey," RBA Research Discussion Papers rdp2000-01, Reserve Bank of Australia.
    3. Ching-Wei Tan, 1999. "Estimating the Complexity Function of Financial Time Series: An Estimation Based on Predictive Stochastic Complexity," Computing in Economics and Finance 1999 1143, Society for Computational Economics.
    4. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
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    Cited by:

    1. Cristescu, C.P. & Stan, C. & Scarlat, E.I., 2009. "The dynamics of exchange rate time series and the chaos game," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(23), pages 4845-4855.
    2. Alvarez-Ramirez, J. & Rodriguez, E. & Espinosa-Paredes, G., 2012. "A partisan effect in the efficiency of the US stock market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(20), pages 4923-4932.
    3. Li, Yiying & Ren, Xiaohang & Taghizadeh-Hesary, Farhad, 2023. "Vulnerability of sustainable markets to fossil energy shocks," Resources Policy, Elsevier, vol. 85(PB).
    4. Wang, Fang & Gacesa, Marko, 2023. "Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models," International Review of Financial Analysis, Elsevier, vol. 88(C).
    5. Lucio Maria Calcagnile & Fulvio Corsi & Stefano Marmi, 2016. "Entropy and efficiency of the ETF market," Papers 1609.04199, arXiv.org.
    6. Shternshis, Andrey & Mazzarisi, Piero & Marmi, Stefano, 2022. "Measuring market efficiency: The Shannon entropy of high-frequency financial time series," Chaos, Solitons & Fractals, Elsevier, vol. 162(C).
    7. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2010. "Complexity-entropy causality plane: A useful approach to quantify the stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1891-1901.
    8. Fang Wang & Marko Gacesa, 2024. "Semi-strong Efficient Market of Bitcoin and Twitter: an Analysis of Semantic Vector Spaces of Extracted Keywords and Light Gradient Boosting Machine Models," Papers 2409.15988, arXiv.org.
    9. Andrey Shternshis & Piero Mazzarisi, 2022. "Variance of entropy for testing time-varying regimes with an application to meme stocks," Papers 2211.05415, arXiv.org, revised Jun 2023.
    10. Lucio Maria Calcagnile & Fulvio Corsi & Stefano Marmi, 2020. "Entropy and Efficiency of the ETF Market," Computational Economics, Springer;Society for Computational Economics, vol. 55(1), pages 143-184, January.
    11. Brandouy, Olivier & Delahaye, Jean-Paul & Ma, Lin & Zenil, Hector, 2014. "Algorithmic complexity of financial motions," Research in International Business and Finance, Elsevier, vol. 30(C), pages 336-347.
    12. Abounoori, Esmaiel & Shahrazi, Mahdi & Rasekhi, Saeed, 2012. "An investigation of Forex market efficiency based on detrended fluctuation analysis: A case study for Iran," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3170-3179.
    13. Lahmiri, Salim & Bekiros, Stelios & Avdoulas, Christos, 2018. "Time-dependent complexity measurement of causality in international equity markets: A spatial approach," Chaos, Solitons & Fractals, Elsevier, vol. 116(C), pages 215-219.
    14. Da Silva, Sergio, 2015. "Financial Market Efficiency Should be Gauged in Relative Rather than Absolute Terms," MPRA Paper 64497, University Library of Munich, Germany.
    15. Olivier Brandouy & Jean-Paul Delahaye & Lin Ma, 2015. "Estimating the Algorithmic Complexity of Stock Markets," Papers 1504.04296, arXiv.org.
    16. Matsushita, Raul & Brandão, Helena & Nobre, Iuri & Da Silva, Sergio, 2024. "Differential entropy estimation with a Paretian kernel: Tail heaviness and smoothing," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 646(C).
    17. Giglio, Ricardo & Da Silva, Sergio, 2009. "Ranking the stocks listed on Bovespa according to their relative efficiency," MPRA Paper 22720, University Library of Munich, Germany.
    18. Brouty, Xavier & Garcin, Matthieu, 2024. "Fractal properties, information theory, and market efficiency," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).

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    More about this item

    Keywords

    financial efficiency; algorithmic complexity;

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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