2013/07/31 by Ladislav Kristoufek, Ladislav Krištoufek, Miloslav Vošvrda +1 · 88 citations
Economics, Econometrics and Finance · Mathematics · #Capital market #Complex Systems and Time Series Analysis #Dimension (graph theory) #Econometrics #Economics #Efficient-market hypothesis #Entropy (arrow of time) #Finance #Financial Risk and Volatility Modeling #Financial economics #Fractal #Fractal dimension #Geography #Market Dynamics and Volatility #Market efficiency #Mathematical analysis #Mathematics #Physics #Pure mathematics #Stock (firearms) #Stock market #Stock market index #q-fin.ST
paper · pdf · doi:10.1140/epjb/e2014-50113-6
published in The European Physical Journal B 87(7) (Springer Science+Business Media) · 12 pages, 1 figure, 4 tables
arxiv created 2014/05/17 · openalex publication_date 2014/07/01 · arxiv updated 2015/06/16 · openalex created_date 2016/06/24 · openalex updated_date 2026/08/05
We utilize long-term memory, fractal dimension and approximate entropy as input variables for the Efficiency Index [Kristoufek & Vosvrda (2013), Physica A 392]. This way, we are able to comment on stock market efficiency after controlling for different types of inefficiencies. Applying the methodology on 38 stock market indices across the world, we find that the most efficient markets are situated in the Eurozone (the Netherlands, France and Germany) and the least efficient ones in the Latin America (Venezuela and Chile).