Complexity analysis of the stock market (original) (raw)
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Influence of the Investor’s Behavior on the Complexity of the Stock Market
Brazilian Journal of Physics, 2011
One of the pillars of the finance theory is the efficient-market hypothesis, which is used to analyze the stock market. However, in recent years, this hypothesis has been questioned by a number of studies showing evidence of unusual behaviors in the returns of financial assets ("anomalies") caused by behavioral aspects of the economic agents. Therefore, it is time to initiate a debate about the efficient-market hypothesis and the "behavioral finances." We here introduce a cellular automaton model to study the stock market complexity, considering different behaviors of the economical agents. From the analysis of the stationary standard of investment observed in the simulations and the Hurst exponents obtained for the term series of stock index, we draw conclusions concerning the complexity of the model compared to real markets. We also investigate which conditions of the investors are able to influence the efficient market hypothesis statements.
Levels of complexity in financial markets
Physica A: Statistical Mechanics and its Applications, 2001
We consider different levels of complexity which are observed in the empirical investigation of financial time series. We discuss recent empirical and theoretical work showing that statistical properties of financial time series are rather complex under several ways. Specifically, they are complex with respect to their (i) temporal and (ii) ensemble properties. Moreover, the ensemble return properties show a behavior which is specific to the nature of the trading day reflecting if it is a normal or an extreme trading day.
Algorithmic Complexity of Real Financial Markets
Physica A: Statistical Mechanics and its Applications, 2001
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 effcient market hypothesis is discussed.
Efficiency of financial markets and algorithmic complexity
Journal of Physics: Conference Series, 2010
In this work we are interested in the concept of market efficiency and its relationship with the algorithmic complexity theory. We employ a methodology based on the Lempel-Ziv index to analyze the relative efficiency of high-frequency data coming from the Brazilian stock market.
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EPL (Europhysics Letters), 2008
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 and 20 US dollar exchange rates in terms of their relative efficiency.
Complexity-entropy causality plane: A useful approach to quantify the stock market inefficiency
Physica A-statistical Mechanics and Its Applications, 2010
The complexity-entropy causality plane has been recently introduced as a powerful tool for discriminating Gaussian from non-Gaussian process and different degrees of correlations [O.A. Rosso, H.A. Larrondo, M.T. Martín, A. Plastino, M.A. Fuentes, Distinguishing noise from chaos, Phys. Rev. Lett. 99 (2007) 154102]. We propose to use this representation space to distinguish the stage of stock market development. Our empirical results demonstrate that this statistical physics approach is useful, allowing a more refined classification of stock market dynamics. (M. Zanin), benjamin.tabak@bcb.gov.br (B.M. Tabak), dario.perez@ucv.cl (D.G. Pérez), oarosso@fibertel.com.ar (O.A. Rosso).
2010
Bozdogan [8] [6] [7] developed a new model selection criteria called information measure of complexity (ICOMP) for model selection. In contrast to Akaike’s [1] information criterion (AIC) and other AIC type criteria that are traditionally used for regression analysis, ICOMP takes into account the interdependencies of the parameter estimates. This paper is divided into two parts. In the first part we compare and contrast ICOMP with AIC and other AIC type selection criterion for model selection in regression analysis involving stock market securities. While in the second part we apply the definition of information theoretic measure of complexity to portfolio analysis. We compare the complexity of a portfolio of securities with its’ measure of diversification (PDI) and examine the similarities and differences between the two quantities as it affects portfolio management.
Levels of complexity in !nancial markets
2000
We consider di/erent levels of complexity which are observed in the empirical investigation of !nancial time series. We discuss recent empirical and theoretical work showing that statistical properties of !nancial time series are rather complex under several ways. Speci!cally, they are complex with respect to their (i) temporal and (ii) ensemble properties. Moreover, the ensemble return properties show a behavior