2,805 research outputs found
Quantifying instabilities in Financial Markets
Financial global crisis has devastating impacts to economies since early XX century and continues to impose increasing collateral damages for governments, enterprises, and society in general. Up to now, all efforts to obtain efficient methods to predict these events have been disappointing. However, the quest for a robust estimator of the degree of the market efficiency, or even, a crisis predictor, is still one of the most studied subjects in the field. We present here an original contribution that combines Information Theory with graph concepts, to study the return rate series of 32 global trade markets. Specifically, we propose a very simple quantifier that shows to be highly correlated with global financial instability periods, being also a good estimator of the market crisis risk and market resilience. We show that this estimator displays striking results when applied to countries that played central roles during the last major global market crisis. The simplicity and effectiveness of our quantifier allow us to anticipate its use in a wide range of disciplines.Fil: Gonçalves, Bruna Amin. Centro Federal de Educação Tecnológica de Minas Gerais. Programa de Pós Graduação em Modelagem Matemática e Computacional; BrasilFil: Carpi, Laura. Universidad Politécnica de Catalunya; EspañaFil: Rosso, Osvaldo AnÃbal. Universidade Federal de Alagoas; Brasil. Hospital Italiano. Departamento de Informática En Salud.; Argentina. Universidad de Los Andes.; ChileFil: Ravetti, MartÃn G.. Universidade Federal de Minas Gerais; BrasilFil: Atman, A. P. F.. Centro Federal de Educação Tecnológica de Minas Gerais. Programa de Pós Graduação em Modelagem Matemática e Computacional; Brasi
Why Do Markets Crash? Bitcoin Data Offers Unprecedented Insights
Crashes have fascinated and baffled many canny observers of financial
markets. In the strict orthodoxy of the efficient market theory, crashes must
be due to sudden changes of the fundamental valuation of assets. However,
detailed empirical studies suggest that large price jumps cannot be explained
by news and are the result of endogenous feedback loops. Although plausible, a
clear-cut empirical evidence for such a scenario is still lacking. Here we show
how crashes are conditioned by the market liquidity, for which we propose a new
measure inspired by recent theories of market impact and based on readily
available, public information. Our results open the possibility of a dynamical
evaluation of liquidity risk and early warning signs of market instabilities,
and could lead to a quantitative description of the mechanisms leading to
market crashes
Markets, herding and response to external information
We focus on the influence of external sources of information upon financial
markets. In particular, we develop a stochastic agent-based market model
characterized by a certain herding behavior as well as allowing traders to be
influenced by an external dynamic signal of information. This signal can be
interpreted as a time-varying advertising, public perception or rumor, in favor
or against one of two possible trading behaviors, thus breaking the symmetry of
the system and acting as a continuously varying exogenous shock. As an
illustration, we use a well-known German Indicator of Economic Sentiment as
information input and compare our results with Germany's leading stock market
index, the DAX, in order to calibrate some of the model parameters. We study
the conditions for the ensemble of agents to more accurately follow the
information input signal. The response of the system to the external
information is maximal for an intermediate range of values of a market
parameter, suggesting the existence of three different market regimes:
amplification, precise assimilation and undervaluation of incoming information.Comment: 30 pages, 8 figures. Thoroughly revised and updated version of
arXiv:1302.647
Strategies used as spectroscopy of financial markets reveal new stylized facts
We propose a new set of stylized facts quantifying the structure of financial
markets. The key idea is to study the combined structure of both investment
strategies and prices in order to open a qualitatively new level of
understanding of financial and economic markets. We study the detailed order
flow on the Shenzhen Stock Exchange of China for the whole year of 2003. This
enormous dataset allows us to compare (i) a closed national market (A-shares)
with an international market (B-shares), (ii) individuals and institutions and
(iii) real investors to random strategies with respect to timing that share
otherwise all other characteristics. We find that more trading results in
smaller net return due to trading frictions. We unveiled quantitative power
laws with non-trivial exponents, that quantify the deterioration of performance
with frequency and with holding period of the strategies used by investors.
Random strategies are found to perform much better than real ones, both for
winners and losers. Surprising large arbitrage opportunities exist, especially
when using zero-intelligence strategies. This is a diagnostic of possible
inefficiencies of these financial markets.Comment: 13 pages including 5 figures and 1 tabl
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