54 research outputs found
Quantifying the behavior of stock correlations under market stress
Understanding correlations in complex systems is crucial in the face of turbulence, such as the ongoing financial crisis. However, in complex systems, such as financial systems, correlations are not constant but instead vary in time. Here we address the question of quantifying state-dependent correlations in stock markets. Reliable estimates of correlations are absolutely necessary to protect a portfolio. We analyze 72 years of daily closing prices of the 30 stocks forming the Dow Jones Industrial Average (DJIA). We find the striking result that the average correlation among these stocks scales linearly with market stress reflected by normalized DJIA index returns on various time scales. Consequently, the diversification effect which should protect a portfolio melts away in times of market losses, just when it would most urgently be needed. Our empirical analysis is consistent with the interesting possibility that one could anticipate diversification breakdowns, guiding the design of protected portfolios
Evolvement of Uniformity and Volatility in the Stressed Global Financial Village
Background: In the current era of strong worldwide market couplings the global financial village became highly prone to
systemic collapses, events that can rapidly sweep throughout the entire village.
Methodology/Principal Findings: We present a new methodology to assess and quantify inter-market relations. The
approach is based on the correlations between the market index, the index volatility, the market Index Cohesive Force and
the meta-correlations (correlations between the intra-correlations.) We investigated the relations between six important
world markets—U.S., U.K., Germany, Japan, China and India—from January 2000 until December 2010. We found that while
the developed ‘‘western’’ markets (U.S., U.K., Germany) are highly correlated, the interdependencies between these markets
and the developing ‘‘eastern’’ markets (India and China) are volatile and with noticeable maxima at times of global world
events. The Japanese market switches ‘‘identity’’—it switches between periods of high meta-correlations with the ‘‘western’’
markets and periods when it behaves more similarly to the ‘‘eastern’’ markets.
Conclusions/Significance: The methodological framework presented here provides a way to quantify the evolvement of
interdependencies in the global market, evaluate a world financial network and quantify changes in the world inter market
relations. Such changes can be used as precursors to the agitation of the global financial village. Hence, the new approach
can help to develop a sensitive ‘‘financial seismograph’’ to detect early signs of global financial crises so they can be treated
before they develop into worldwide event
Index Cohesive Force Analysis Reveals That the US Market Became Prone to Systemic Collapses Since 2002
BACKGROUND: The 2007-2009 financial crisis, and its fallout, has strongly emphasized the need to define new ways and measures to study and assess the stock market dynamics. METHODOLOGY/PRINCIPAL FINDINGS: The S&P500 dynamics during 4/1999-4/2010 is investigated in terms of the index cohesive force (ICF--the balance between the stock correlations and the partial correlations after subtraction of the index contribution), and the Eigenvalue entropy of the stock correlation matrices. We found a rapid market transition at the end of 2001 from a flexible state of low ICF into a stiff (nonflexible) state of high ICF that is prone to market systemic collapses. The stiff state is also marked by strong effect of the market index on the stock-stock correlations as well as bursts of high stock correlations reminiscence of epileptic brain activity. CONCLUSIONS/SIGNIFICANCE: The market dynamical states, stability and transition between economic states was studies using new quantitative measures. Doing so shed new light on the origin and nature of the current crisis. The new approach is likely to be applicable to other classes of complex systems from gene networks to the human brain
Statistically validated networks in bipartite complex systems
Many complex systems present an intrinsic bipartite nature and are often
described and modeled in terms of networks [1-5]. Examples include movies and
actors [1, 2, 4], authors and scientific papers [6-9], email accounts and
emails [10], plants and animals that pollinate them [11, 12]. Bipartite
networks are often very heterogeneous in the number of relationships that the
elements of one set establish with the elements of the other set. When one
constructs a projected network with nodes from only one set, the system
heterogeneity makes it very difficult to identify preferential links between
the elements. Here we introduce an unsupervised method to statistically
validate each link of the projected network against a null hypothesis taking
into account the heterogeneity of the system. We apply our method to three
different systems, namely the set of clusters of orthologous genes (COG) in
completely sequenced genomes [13, 14], a set of daily returns of 500 US
financial stocks, and the set of world movies of the IMDb database [15]. In all
these systems, both different in size and level of heterogeneity, we find that
our method is able to detect network structures which are informative about the
system and are not simply expression of its heterogeneity. Specifically, our
method (i) identifies the preferential relationships between the elements, (ii)
naturally highlights the clustered structure of investigated systems, and (iii)
allows to classify links according to the type of statistically validated
relationships between the connected nodes.Comment: Main text: 13 pages, 3 figures, and 1 Table. Supplementary
information: 15 pages, 3 figures, and 2 Table
Local variation of hashtag spike trains and popularity in Twitter
We draw a parallel between hashtag time series and neuron spike trains. In
each case, the process presents complex dynamic patterns including temporal
correlations, burstiness, and all other types of nonstationarity. We propose
the adoption of the so-called local variation in order to uncover salient
dynamics, while properly detrending for the time-dependent features of a
signal. The methodology is tested on both real and randomized hashtag spike
trains, and identifies that popular hashtags present regular and so less bursty
behavior, suggesting its potential use for predicting online popularity in
social media.Comment: 7 pages, 7 figure
Financial time series prediction using spiking neural networks
In this paper a novel application of a particular type of spiking neural network, a Polychronous Spiking Network, was used for financial time series prediction. It is argued that the inherent temporal capabilities of this type of network are suited to non-stationary data such as this. The performance of the spiking neural network was benchmarked against three systems: two "traditional", rate-encoded, neural networks; a Multi-Layer Perceptron neural network and a Dynamic Ridge Polynomial neural network, and a standard Linear Predictor Coefficients model. For this comparison three non-stationary and noisy time series were used: IBM stock data; US/Euro exchange rate data, and the price of Brent crude oil. The experiments demonstrated favourable prediction results for the Spiking Neural Network in terms of Annualised Return and prediction error for 5-Step ahead predictions. These results were also supported by other relevant metrics such as Maximum Drawdown and Signal-To-Noise ratio. This work demonstrated the applicability of the Polychronous Spiking Network to financial data forecasting and this in turn indicates the potential of using such networks over traditional systems in difficult to manage non-stationary environments. © 2014 Reid et al
Dominating Clasp of the Financial Sector Revealed by Partial Correlation Analysis of the Stock Market
What are the dominant stocks which drive the correlations present among stocks traded in a stock market? Can a correlation analysis provide an answer to this question? In the past, correlation based networks have been proposed as a tool to uncover the underlying backbone of the market. Correlation based networks represent the stocks and their relationships, which are then investigated using different network theory methodologies. Here we introduce a new concept to tackle the above question—the partial correlation network. Partial correlation is a measure of how the correlation between two variables, e.g., stock returns, is affected by a third variable. By using it we define a proxy of stock influence, which is then used to construct partial correlation networks. The empirical part of this study is performed on a specific financial system, namely the set of 300 highly capitalized stocks traded at the New York Stock Exchange, in the time period 2001–2003. By constructing the partial correlation network, unlike the case of standard correlation based networks, we find that stocks belonging to the financial sector and, in particular, to the investment services sub-sector, are the most influential stocks affecting the correlation profile of the system. Using a moving window analysis, we find that the strong influence of the financial stocks is conserved across time for the investigated trading period. Our findings shed a new light on the underlying mechanisms and driving forces controlling the correlation profile observed in a financial market
Inference of financial networks using the normalised mutual information rate
In this paper we study data from financial markets using an information theory tool that we call the normalised Mutual Information Rate and show how to use it to infer the underlying network structure of interrelations in foreign currency exchange rates and stock indices of 14 countries world-wide and the European Union. We first present the mathematical method and discuss about its computational aspects, and then apply it to artificial data from chaotic dynamics and to correlated random variates. Next, we apply the method to infer the network structure of the financial data. Particularly, we study and reveal the interrelations among the various foreign currency exchange rates and stock indices in two separate networks for which we also perform an analysis to identify their structural properties. Our results show that both are small-world networks sharing similar properties but also having distinct differences in terms of assortativity. Finally, the consistent relationships depicted among the 15 economies are further supported by a discussion from the economics view point
Statistical Inference for Valued-Edge Networks: Generalized Exponential Random Graph Models
Across the sciences, the statistical analysis of networks is central to the
production of knowledge on relational phenomena. Because of their ability to
model the structural generation of networks, exponential random graph models
are a ubiquitous means of analysis. However, they are limited by an inability
to model networks with valued edges. We solve this problem by introducing a
class of generalized exponential random graph models capable of modeling
networks whose edges are valued, thus greatly expanding the scope of networks
applied researchers can subject to statistical analysis
Global and Local Features of Semantic Networks: Evidence from the Hebrew Mental Lexicon
BACKGROUND: Semantic memory has generated much research. As such, the majority of investigations have focused on the English language, and much less on other languages, such as Hebrew. Furthermore, little research has been done on search processes within the semantic network, even though they are abundant within cognitive semantic phenomena. METHODOLOGY/PRINCIPAL FINDINGS: We examine a unique dataset of free association norms to a set of target words and make use of correlation and network theory methodologies to investigate the global and local features of the Hebrew lexicon. The global features of the lexicon are investigated through the use of association correlations--correlations between target words, based on their association responses similarity; the local features of the lexicon are investigated through the use of association dependencies--the influence words have in the network on other words. CONCLUSIONS/SIGNIFICANCE: Our investigation uncovered Small-World Network features of the Hebrew lexicon, specifically a high clustering coefficient and a scale-free distribution, and provides means to examine how words group together into semantically related 'free categories'. Our novel approach enables us to identify how words facilitate or inhibit the spread of activation within the network, and how these words influence each other. We discuss how these properties relate to classical research on spreading activation and suggest that these properties influence cognitive semantic search processes. A semantic search task, the Remote Association Test is discussed in light of our findings
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