Abstract
Summarized by the efficient market hypothesis, the idea that stock prices fully reflect all available information is always confronted with the behavior of real-world markets. While there is plenty of evidence indicating and quantifying the efficiency of stock markets, most studies assume this efficiency to be constant over time so that its dynamical and collective aspects remain poorly understood. Here we define the time-varying efficiency of stock markets by calculating the permutation entropy within sliding time-windows of log-returns of stock market indices. We show that major world stock markets can be hierarchically classified into several groups that display similar long-term efficiency profiles. However, we also show that efficiency ranks and clusters of markets with similar trends are only stable for a few months at a time. We thus propose a network representation of stock markets that aggregates their short-term efficiency patterns into a global and coherent picture. We find this financial network to be strongly entangled while also having a modular structure that consists of two distinct groups of stock markets. Our results suggest that stock market efficiency is a collective phenomenon that can drive its operation at a high level of informational efficiency, but also places the entire system under risk of failure.
Highlights
Summarized by the efficient market hypothesis, the idea that stock prices fully reflect all available information is always confronted with the behavior of real-world markets
Whether the lack of efficiency in stock markets is an opportunity for profit, or whether it represents a systemic risk for financial systems that must be fixed—fact is that the efficient market hypothesis is still an ubiquitous concept among economic agents and academics working with financial data and models
We have presented an investigation of dynamical efficiency patterns of 43 major world stock markets during the past 20 years
Summary
Summarized by the efficient market hypothesis, the idea that stock prices fully reflect all available information is always confronted with the behavior of real-world markets. Stock prices in real-world markets can become auto-correlated during short-term p eriods[6], corroborating the more holistic idea that making short-term predictions and arbitrage are possible Another evidence that stock markets are not ideally efficient are the non-Gaussian fluctuations (fat-tailed distributions) of the log-returns of asset prices[7,8], the difficulty of simple random walk models in predicting stock market crashes[9], and the existence of successful trading s trategies[10]. We use a physics-inspired approach for defining a time-varying efficiency from log-returns of Scientific Reports | (2020) 10:21992
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