2
votes
Dmitri Krioukov
posted by xrigher (16 April 2012)
A way to fight your traffic tickets. The paper was awarded a special prize of
$400 that the author did not have to pay to the state of California.
<br />In view of enormous, extremely surprising and completely unexpected public
interest to this work, we have added an appendix answering the two most common
questions.
3
votes
Victor M. Yakovenko
posted by Matúš Medo (3 May 2012)
This Chapter is written for the Festschrift celebrating the 70th birthday of
the distinguished economist Duncan Foley from the New School for Social
Research in New York. This Chapter reviews applications of statistical physics
methods, such as the principle of entropy maximization, to the probability
distributions of money, income, and global energy consumption per capita. The
exponential probability distribution of wages, predicted by the statistical
equilibrium theory of a labor market developed by Foley in 1996, is supported
by empirical data on income distribution in the USA for the majority (about
97%) of population. In addition, the upper tail of income distribution (about
3% of population) follows a power law and expands dramatically during financial
bubbles, which results in a significant increase of the overall income
inequality. A mathematical analysis of the empirical data clearly demonstrates
the two-class structure of a society, as pointed out Karl Marx and recently
highlighted by the Occupy Movement. Empirical data for the energy consumption
per capita around the world are close to an exponential distribution, which can
be also explained by the entropy maximization principle.
2
votes
Maxi San Miguel, Jeffrey H. Johnson, Janos Kertesz, Kimmo Kaski, Albert Díaz-Guilera, Robert S. MacK
posted by Matúš Medo (26 April 2012)
FuturICT foundations are social science, complex systems science, and ICT.
The main concerns and challenges in the science of complex systems in the
context of FuturICT are laid out in this paper with special emphasis on the
Complex Systems route to Social Sciences. This include complex systems having:
many heterogeneous interacting parts; multiple scales; complicated transition
laws; unexpected or unpredicted emergence; sensitive dependence on initial
conditions; path-dependent dynamics; networked hierarchical connectivities;
interaction of autonomous agents; self-organisation; non-equilibrium dynamics;
combinatorial explosion; adaptivity to changing environments; co-evolving
subsystems; ill-defined boundaries; and multilevel dynamics. In this context,
science is seen as the process of abstracting the dynamics of systems from
data. This presents many challenges including: data gathering by large-scale
experiment, participatory sensing and social computation, managing huge
distributed dynamic and heterogeneous databases; moving from data to dynamical
models, going beyond correlations to cause-effect relationships, understanding
the relationship between simple and comprehensive models with appropriate
choices of variables, ensemble modeling and data assimilation, modeling systems
of systems of systems with many levels between micro and macro; and formulating
new approaches to prediction, forecasting, and risk, especially in systems that
can reflect on and change their behaviour in response to predictions, and
systems whose apparently predictable behaviour is disrupted by apparently
unpredictable rare or extreme events. These challenges are part of the FuturICT
agenda.
2
votes
Andreas Gronlund, Il Gu Yi, Beom Jun Kim
posted by Matúš Medo (3 May 2012)
We investigate the structure of the profit landscape obtained from the most
basic, fluctuation based, trading strategy applied for the daily stock price
data. The strategy is parameterized
9c5
by only two variables, p and q. Stocks are
sold and bought if the log return is bigger than p and less than -q,
respectively. Repetition of this simple strategy for a long time gives the
profit defined in the underlying two-dimensional parameter space of p and q. It
is revealed that the local maxima in the profit landscape are spread in the
form of a fractal structure. The fractal structure implies that successful
strategies are not localized to any region of the profit landscape and are
neither spaced evenly throughout the profit landscape, which makes the
optimization notoriously hard and hypersensitive for partial or limited
information. The concrete implication of this property is demonstrated by
showing that optimization of one stock for future values or other stocks
renders worse profit than a strategy that ignores fluctuations, i.e., a
long-term buy-and-hold strategy.
1
vote
Gayatri Tilak, Tamas Szell, Remy Chicheportiche, Anirban Chakraborti
posted by Matúš Medo (26 April 2012)
The aim of this article is to briefly review and make new studies of
correlations and co-movements of stocks, so as to understand the
"seasonalities" and market evolution. Using the intraday data of the CAC40, we
begin by reasserting the findings of Allez and Bouchaud [New J. Phys. 13,
025010 (2011)]: the average correlation between stocks increases throughout the
day. We then use multidimensional scaling (MDS) in generating maps and
visualizing the dynamic evolution of the stock market during the day. We do not
find any marked difference in the structure of the market during a day. Another
aim is to use daily data for MDS studies, and visualize or detect specific
sectors in a market and periods of crisis. We suggest that this type of
visualization may be used in identifying potential pairs of stocks for "pairs
trade".
1
vote
Giacomo Livan, Jun-ichi Inoue, Enrico Scalas
posted by Matúš Medo (11 May 2012)
We investigate the possible drawbacks of employing the standard Pearson
estimator to measure correlation coefficients between financial stocks in the
presence of non-stationary behavior, and we provide empirical evidence against
the well-established common knowledge that using longer price time series
provides better, more accurate, correlation estimates. Then, we investigate the
possible consequences of instabilities in empirical correlation coefficient
measurements on optimal portfolio selection. We rely on previously published
works which provide a framework allowing to take into account possible risk
underestimations due to the non-optimality of the portfolio weights being used
in order to distinguish such non-optimality effects from risk underestimations
genuinely due to non-stationarities. We interpret such results in terms of
instabilities in some spectral properties of portfolio correlation matrices.
12
votes
Amos Storkey
posted by Matúš Medo (24 June 2011)
Prediction markets show considerable promise for developing flexible
mechanisms for machine learning. Here, machine learning markets for
multivariate systems are defined, and a utility-based framework is established
for their analysis. This differs from the usual approach of defining static
betting functions. It is shown that such markets can implement model
combination methods used in machine learning, such as product of expert and
mixture of expert approaches as equilibrium pricing models, by varying agent
utility functions. They can also implement models composed of local potentials,
and message passing methods. Prediction markets also allow for more flexible
combinations, by combining multiple different utility functions. Conversely,
the market mechanisms implement inference in the relevant probabilistic models.
This means that market mechanism can be utilized for implementing parallelized
model building and inference for probabilistic modelling.
1
vote
Natascia Angelini, Giacomo Bormetti, Stefano Marmi, Franco Nardini
posted by Matúš Medo (26 April 2012)
The aim of this paper is twofold: to provide a theoretical framework and to
give further empirical support to Shiller's test of the appropriateness of
prices in the stock market based on the Cycli
b14
cally Adjusted Price Earnings
(CAPE) ratio. We devote the first part of the paper to the empirical analysis
and we show that the CAPE is a powerful predictor of future long run
performances of the market not only for the U.S. but also for countries such as
Belgium, France, Germany, Japan, the Netherlands, Norway, Sweden and
Switzerland. We show four relevant empirical facts: i) the striking ability of
the logarithmic averaged earning over price ratio to predict returns of the
index, ii) how this evidence increases switching from returns to gross returns,
iii) moving over different time horizons, the regression coefficients are
constant in a statistically robust way, and iv) the poorness of the prediction
when the precursor is adjusted with long term interest rate. In the second part
we provide a theoretical justification of the empirical observations. Indeed we
propose a simple model of the price dynamics in which the return growth depends
on three components: a) a momentum component, naturally justified in terms of
agents' belief that expected returns are higher in bullish markets than in
bearish ones; b) a fundamental component proportional to the log earnings over
price ratio at time zero, from which the actual stock price may deviate as an
effect of random external disturbances, and c) a driving component ensuring the
diffusive behaviour of stock prices. Under these assumptions, we are able to
prove that, if we consider a sufficiently large number of periods, the expected
rate of return and the expected gross return are linear in the initial time
value of the log earnings over price ratio, and their variance goes to zero
with rate of convergence equal to minus one.
2
votes
Tomasz Suslo
posted by tomasz suslo (7 May 2012)
In this paper the complex-valued bes
528
t linear unbiased estimator of an unknown
constant mean of white noise was derived the ordinary least-squares estimator
of an unknown constant mean of random field (arithmetic mean) charged by an
imaginary error.
1
vote
Sayantan Ghosh, Uwe Jaekel, Francesco Petruccione
posted by Matúš Medo (11 May 2012)
Financial markets are well known examples of multi-fractal complex systems
that have garnered much interest in their characterization through complex
network theory. The recent studies have used correlation based distance metrics
for defining and analyzing financial networks. In this work the singularity
strength is employed to define a distance metric and the existence of
hierarchical structure in the Johannesburg Stock Exchange is investigated. The
multi-fractal nature of the financial market, which is otherwise hidden in the
correlation coefficient based prescriptions, is analyzed through the use of the
singularity strength based method. The presence of a super cluster is exhibited
in the network which accounts for half of the network size and is homogeneous
in the sectoral composition of the South African market.
1
vote
Mauricio Labadie, Charles-Albert Lehalle
posted by Matúš Medo (18 May 2012)
We derive explicit recursive formulas for Target Close (TC) and
Implementation Shortfall (IS) in the Almgren-Chriss framework. We explain how
to compute the optimal starting and stopping times for IS and TC, respectively,
given a minimum trading size. We also show how to add a minimum participation
rate constraint (Percentage of Volume, PVol) for both TC and IS. We also study
an alternative set of risk measures for the optimisation of algorithmic trading
curves. We assume a self-similar process (e.g. L\'evy process, fractional
Brownian motion or fractal process) and define a new risk measure, the
$p$-variation, which reduces to the variance if the process is a Brownian
motion. We deduce the explicit formula for the TC and IS algorithms under a
self-similar process. We show that there is an equivalence between self-similar
models and a family of risk measures called $p$-variations: assuming a
self-similar process and calibrating empirically the parameter $p$ for the
$p$-variation yields the same result as assuming a Brownian motion and using
the $p$-variation as risk measure instead of the variance. We also show that
$p$ can be seen as a measure of the aggressiveness: $p$ increases if and only
if the TC algorithm starts later and executes faster. From the explicit
expression of the TC algorithm one can compute the sensitivities of the curve
with respect to the parameters up to any order. As an example, we compute the
first order sensitivity with respect to both a local and a global surge of
volatility. Finally, we show how the parameter $p$ of the $p$-variation can be
implied from the optimal starting time of TC, and that under this framework $p$
can be viewed as a measure of the joint impact of market impact (i.e.
liquidity) and volatility.
1
vote
Zeyu Zheng, Kazuko Yamasaki
posted by Matúš Medo (11 May 2012)
Recently, many studies indicated that the minimum spanning tree (MST) network
whose metric distance is de?ned b
979
y using correlation coe?cients have strong
implications on extracting infor- mation from return time series. However in
many cases researchers may hope to investigate the strength of interactions but
not the directions of them. In order to study the strength of interaction and
connection of ?nancial asset returns we propose a modi?ed minimum spanning tree
network whose metric distance is de?ned from absolute cross-correlation
coe?cients. We had investigated 69 daily ?nancial time series, which
constituted by 3 types ?nance assets (29 stock market indica- tor time series,
21 currency futures price time series and 19 commodity futures price time
series). Empirical analyses show that the MST network of returns is
time-dependent in overall structure, while same type ?nancial assets usually
keep stable inter-connections. Moreover each asset in same group show similar
economic characters. In other words, each group concerned with one kind of
traditional ?nancial commodity. In addition, we ?nd the time-lag between stock
market indicator volatility time series and EUA (EU allowances), WTI (West
Texas Intermediate) volatility time series. The peak of cross-correlation
function of volatility time series between EUA (or WTI) and stock market
indicators show a signi?cant time shift (> 20days) from 0.
1
vote
Andreas Hüsler, Didier Sornette, Cars H. Hommes
posted by Matúš Medo (11 May 2012)
We analyze a controlled price formation experiment in the laboratory that
shows evidence for bubbles. We calibrate two models that demonstrate with high
statistical significance that these laboratory bubbles have a tendency to grow
faster than exponential due to positive feedback. We show that the positive
feedback operates by traders continuously upgrading their over-optimistic
expectations of future returns based on past prices rather than on realized
returns.
2
votes
Daniel Gayo-Avello
posted by Matúš Medo (3 May 2012)
Predicting X from Twitter is a popular fad within the Twitter research
subculture. It seems both appealing and relatively easy. Among such kind of
studies, electoral prediction is maybe the most attractive, and at this moment
there is a growing body of literature on such a topic. This is not only an
interesting research problem but, above all, it is extremely difficult.
However, most of the authors seem to be more interested in claiming positive
results than in providing sound and reproducible methods. It is also especially
worrisome that many recent papers seem to only acknowledge those studies
supporting the idea of Twitter predicting elections, instead of conducting a
balanced literature review showing both sides of the matter. After reading many
of such papers I have decided to write such a survey myself. Hence, in this
paper, every study relevant to the matter of electoral prediction using social
media is commented. From this review it can be concluded that the predictive
power of Twitter regarding elections has been greatly exaggerated, and that
hard research problems still lie ahead.
1
vote
Diego Rybski, Sergey V. Buldyrev, Shlomo Havlin, Fredrik Liljeros, Hernan A. Makse
posted by Matúš Medo (11 May 2012)
The timing patterns of human communication in social networks is not random.
On the contrary, communication is dominated by emergent statistical laws such
as non-trivial correlations and clustering. Recently, we found long-term
correlations in the user's activity in social communities. Here, we extend this
work to study collective behavior of the whole community. The goal is to
understand the origin of clustering and long-term persistence. At the
individual level, we find that the correlations in activity are a byproduct of
the clustering expressed in the power-law distribution of inter-event times of
single users. On the contrary, the activity of the whole community presents
long-term correlations that are a true emergent property of the system, i.e.
they are not related to the distribution of inter-event times. This result
suggests the existence of collective behavior, possible arising from nontrivial
communication patterns through the embedding social network.
1
vote
Tino Kluge, L. C. G. Rogers
posted by Matúš Medo (26 April 2012)
The potential approach is a general and simple method for modelling interest
rates, foreign exchange rates, and in principle other types of financial
assets. This paper takes data on some liquid interest rate derivatives, and
fits potential models using a small finite-state Markov chain as the base
Markov process.
2
votes
Krzy b5f sztof R. Apt, Evangelos Markakis
posted by Matúš Medo (3 May 2012)
We introduce a new threshold model of social networks, in which the nodes
influenced by their neighbours can adopt one out of several alternatives. We
characterize social networks for which adoption of a product by the whole
network is possible (respectively necessary) and the ones for which a unique
outcome is guaranteed. These characterizations directly yield polynomial time
algorithms that allow us to determine whether a given social network satisfies
one of the above properties.
<br />We also study algorithmic questions for networks without unique outcomes. We
show that the problem of determining whether a final network exists in which
all nodes adopted some product is NP-complete. In turn, the problems of
determining whether a given node adopts some (respectively, a given) product in
some (respectively, all) network(s) are either co-NP complete or can be solved
in polynomial time.
<br />Further, we show that the problem of computing the minimum possible spread of
a product is NP-hard to approximate with an approximation ratio better than
$\Omega(n)$, in contrast to the maximum spread, which is efficiently
computable. Finally, we clarify that some of the above problems can be solved
in polynomial time when there are only two products.
1
vote
Raj Rao Nadakuditi, M. E. J. Newman
posted by Matúš Medo (11 May 2012)
We study networks that display community structure -- groups of nodes within
which connections are unusually dense. Using methods from random matrix theory,
we calculate the spectra of such networks in the limit of large size, and hence
demonstrate the presence of a phase transition in matrix methods for community
detection, such as the popular modularity maximization method. The transition
separates a regime in which such methods successfully detect the community
structure from one in which the structure is present but is not detected. By
comparing these results with recent analyses of maximum-likelihood methods we
are able to show that spectral modularity maximization is an optimal detection
method in the sense that no other method will succeed in the regime where the
modularity method fails.
1
vote
V. I. Yukalov, D. Sornette
posted by Matúš Medo (11 May 2012)
A theory of exceptional extreme events, characterized by their abnormal sizes
885
compared with the rest of the distribution, is presented. Such outliers, called
"dragon-kings", have been reported in the distribution of financial drawdowns,
city-size distributions (e.g., Paris in France and London in the UK), in
material failure, epileptic seizure intensities, and other systems. Within our
theory, the large outliers are interpreted as droplets of Bose-Einstein
condensate: the appearance of outliers is a natural consequence of the
occurrence of Bose-Einstein condensation controlled by the relative degree of
attraction, or utility, of the largest entities. For large populations, Zipf's
law is recovered (except for the dragon-king outliers). The theory thus
provides a parsimonious description of the possible coexistence of a power law
distribution of event sizes (Zipf's law) and dragon-king outliers.
2
votes
Igor V. Ovchinnikov
posted by Eric Kemp-Benedict (10 May 2012)
Here, a scenario is proposed, according to which a generic self-organized
critical (SOC) system can be looked upon as a Witten-type topological field
theory (W-TFT) with spontaneously broken Becchi-Rouet-Stora-Tyutin (BRST)
symmetry. One of the conditions for the SOC is the slow driving noise, which
unambiguously suggests Stratonovich interpretation of the corresponding
stochastic differential equation (SDE). This, in turn, necessitates the use of
Parisi-Sourlas-Wu stochastic quantization procedure, which straightforwardly
leads to a model with BRST-exact action, i.e., to a W-TFT. In the parameter
space of the SDE, there must exist full-dimensional regions where the
BRST-symmetry is spontaneously broken by instantons, which in the context of
SOC are essentially avalanches. In these regions, the avalanche-type SOC
dynamics is liberated from overwise a rightful dynamics-less W-TFT, and a
Goldstone mode of Fadeev-Popov ghosts exists. Goldstinos represent modulii of
instantons (avalanches) and being gapless are responsible for the critical
avalanche distribution in the low-energy, long-wavelength limit. The above
arguments are robust against moderate variations of the SDE's parameters and
the criticality is "self-tuned". The proposition of this paper suggests that
the machinery of W-TFTs may find its applications in many different areas of
modern science studying various physical realizations of SOC. It also suggests
that there may in principle exist a connection between some of SOC's and the
concept of topological quantum computing.
1
vote
Joydeep Chandra, Ingo Scholtes, Niloy Ganguly, Frank Schweitzer
posted by Matúš Medo (26 April 2012)
In this paper, we propose a simple randomized protocol for identifying
trusted nodes based on personalized trust in large scale distributed networks.
The problem of identifying trusted nodes, based on personalized trust, in a
large network setting stems from the huge computation and message overhead
involved in exhaustively calculating and propagating the trust estimates by the
remote nodes. However, in any practical scenario, nodes generally communicate
with a small subset of nodes and thus exhaustively estimating the trust of all
the nodes can lead to huge resource consumption. In contrast, our mechanism can
be tuned to locate a desired subset of trusted nodes, based on the allowable
overhead, with respect to a particular user. The mechanism is based on a simple
exchange of random walk messages and nodes counting the number of times they
are being hit by random walkers of nodes in their neighborhood. Simulation
results to analyze the effectiveness of the algorithm show that using the
proposed algorithm, nodes identify the top trusted nodes in the network with a
very high probability by exploring only around 45% of the total nodes, and in
turn generates nearly 90% less overhead as compared to an exhaustive trust
estimation mechanism, named TrustWebRank. Finally, we provide a measure of the
global trustworthiness of a node; simulation results indicate that the measures
generated using our mechanism differ by only around 0.6% as compared to
TrustWebRank.
1
vote
Josselin Garnier, George Papanicolaou, Tzu-Wei Yang
posted by Matúš Medo (23 April 2012)
We consider a system of diffusion processes that intera
89a
ct through their
empirical mean and have a stabilizing force acting on each of them,
corresponding to a bistable potential. There are three parameters that
characterize the system: the strength of the intrinsic stabilization, the
strength of the external random perturbations, and the degree of cooperation or
interaction between them. The latter is the rate of mean reversion of each
component to the empirical mean of the system. We interpret this model in the
context of systemic risk and analyze in detail the effect of cooperation
between the components, that is, the rate of mean reversion. We show that in a
certain regime of parameters increasing cooperation tends to increase the
stability of the individual agents but it also increases the overall or
systemic risk. We use the theory of large deviations of diffusions interacting
through their mean field.
1
vote
Matteo Ortisi, Valerio Zuccolo
posted by Matúš Medo (18 May 2012)
In this paper we study the continuum time dynamics of a stock in a market
where agents behavior is modeled by a Minority Game with number of strategies
for each agent S=2 and "fake" market histories. The dynamics derived is a
generalized geometric Brownian motion; from the Black&Scholes formula the
calibration of the Mi
660
nority Game, by means of the game parameter $ \sigma^{2}$,
on the European options on DAX Index market is performed. An "$
(\alpha,\sigma^{2})$ -matrix" containing, given options' moneyness and
maturities, values of the parameters $\alpha$ and $ \sigma^{2}$ that make the
theoretical option price agree with the market price is constructed. We
conclude that the asymmetric phase of the Minority Game with $\alpha$ close to
$\alpha_c$ is coherent with options implied volatility market.
1
vote
Yoshiharu Maeno, Satoshi Morinaga, Hirokazu Matsushima, Kenichi Amagai
posted by Matúš Medo (26 April 2012)
The European sovereign debt cr
63a
isis has impaired many European banks. The
distress on the European banks may transmit worldwide, and result in a
large-scale knock-on default of financial institutions. This study presents a
computer simulation model to analyze the risk of insolvency of banks and
defaults in a bank credit network. Simulation experiments reproduce the
knock-on default, and quantify the impact which is imposed on the number of
bank defaults by heterogeneity of the bank credit network, the equity capital
ratio of banks, and the capital surcharge on big banks.
2
votes
Jung-Hun Yang, Dick Ettema, Koen Frenken
posted by Matúš Medo (3 May 2012)
Understanding how spatial configurations of economic activity emerge is
important when formulating spatial planning and economic policy. A simple model
was proposed by Simon, who assumed that firms grow at a rate proportional to
their size, and that new divisions of firms with certain probabilities relocate
to other firms or to new centres of economic activity. Simon's model produces
realistic results in the sense that the sizes of economic centres follow a Zipf
distribution, which is also observed in reality. It lacks realism in the sense
that mechanisms such as cluster formation, congestion (defined as an overly
high density of the same activities) and dependence on the spatial distribution
of external parties (clients, labour markets) are ignored.
<br />The present paper proposed an extension of the Simon model that includes both
centripetal and centrifugal forces. Centripetal forces are included in the
sense that firm divisions are more likely to settle in locations that offer a
higher accessibility to other firms. Centrifugal forces are represented by an
aversion of a too high density of activities in the potential location. The
model is implemented as an agent-based simulation model in a simplified spatial
setting. By running both the Simon model and the extended model, comparisons
are made with respect to their effects on spatial configurations. To this end a
series of metrics are used, including the rank-size distribution and indices of
the degree of clustering and concentration.
1
vote
Fabien Guilbaud, Huyên Pham
posted by Matúš Medo (18 May 2012)
ac7
We propose a framework to study optimal trading policies in a one-tick
pro-rata limit order book, as typically arises in short-term interest rate
futures contracts. The high-frequency trader has the choice to trade via market
orders or limit orders, which are represented respectively by impulse controls
and regular controls. We model and discuss the consequences of the two main
features of this particular microstructure: first, the limit orders sent by the
high frequency trader are only partially executed, and therefore she has no
control on the executed quantity. For this purpose, cumulative executed volumes
are modelled by compound Poisson processes. Second, the high frequency trader
faces the overtrading risk, which is the risk of brutal variations in her
inventory. The consequences of this risk are investigated in the context of
optimal liquidation. The optimal trading problem is studied by stochastic
control and dynamic programming methods, which lead to a characterization of
the value function in terms of an integro quasi-variational inequality. We then
provide the associated numerical resolution procedure, and convergence of this
computational scheme is proved. Next, we examine several situations where we
can on one hand simplify the numerical procedure by reducing the number of
state variables, and on the other hand focus on specific cases of practical
interest. We examine both a market making problem and a best execution problem
in the case where the mid-price process is a martingale. We also detail a high
frequency trading strategy in the case where a (predictive) directional
information on the mid-price is available. Each of the resulting strategies are
illustrated by numerical tests.
1
vote
Florian Klöck, Alexander Schied, Yuemeng Sun
posted by Matúš Medo (18 May 2012)
For a market impact model, price manipulation and related notions play a role
that is similar to the role of arbitrage in a derivatives pricing model. Here,
we give a systematic
6eb
investigation into such regularity issues when orders can
be executed both at a traditional exchange and in a dark pool. To this end, we
focus on a class of dark-pool models whose market impact at the exchange is
described by an Almgren--Chriss model. Conditions for the absence of price
manipulation for all Almgren--Chriss models include the absence of temporary
cross-venue impact, the presence of full permanent cross-venue impact, and the
additional penalization of orders executed in the dark pool. When a particular
Almgren--Chriss model has been fixed, we show by a number of examples that the
regularity of the dark-pool model hinges in a subtle way on the interplay of
all model parameters and on the liquidation time constraint.
5
votes
Stefano Cardanobile, Volker Pernice, Moritz Deger, Stefan Rotter
posted by Matúš Medo (16 December 2011)
Different network models have been suggested for the topology underlying
complex interactions in natural systems. These models are aimed at replicating
specific statistical features encountered in real-world networks. However, it
is rarely considered to which degree the results obtained for one particular
network class can be extrapolated to real-world networks. We address this issue
by comparing different classical and more recently developed network models
with respect to their generalisation power, which we identify with large
structural variability and absence of constraints imposed by the construction
scheme. After having identified the most variable networks, we address the
issue of which constraints are common to all network classes and are thus
suitable candidates for being generic statistical laws of complex networks. In
fact, we find that generic, not model-related dependencies between different
network characteristics do exist. This allows, for instance, to infer global
features from local ones using regression models trained on networks with high
generalisation power. Our results confirm and extend previous findings
regarding the synchronisation properties of neural networks. Our method seems
especially relevant for large networks, which are difficult to map completely,
like the neural networks in the brain. The structure of such large networks
cannot be fully sampled with the present technology. Our approach provides a
method to estimate global properties of under-sampled networks with good
approximation. Finally, we demonstrate on three different data sets (C.
elegans' neuronal network, R. prowazekii's metabolic network, and a network of
synonyms extracted from Roget's Thesaurus) that real-world networks have
statistical relations compatible with those obtained using regression models.
2
votes
Weimao Ke
posted by Matúš Medo (3 May 2012)
We propose a model to analyze citation growth and influences of fitness
(competitiveness) factors in an evolving citation network. Applying the
proposed method to modeling citations to papers and scholars in the InfoVis
2004 data, a benchmark collection about a 31-year history of informatio
7bf
n
visualization, leads to findings consistent with citation distributions in
general and observations of the domain in particular. Fitness variables based
on prior impacts and the time factor have significant influences on citation
outcomes. We find considerably large effect sizes from the fitness modeling,
which suggest inevitable bias in citation analysis due to these factors. While
raw citation scores offer little insight into the growth of InfoVis,
normalization of the scores by influences of time and prior fitness offers a
reasonable depiction of the field's development. The analysis demonstrates the
proposed model's ability to produce results consistent with observed data and
to support meaningful comparison of citation scores over time.
2
votes
Vedant Misra, Marco Lagi, Yaneer Bar-Yam
posted by Matúš Medo (16 December 2011)
We provide direct evidence of market manipulation at the beginning of the
financial crisis in November 2007. The type of manipulation, a "bear raid,"
would have been prevented by a regulation that was repealed by the Securities
and Exchange Commission in July 2007. The regulation, the uptick rule, was
designed to prevent manipulation and promote stability and was in force from
1938 as a key part of the government response to the 1929 market crash and its
aftermath. On November 1, 2007, Citigroup experienced an unusual increase in
trading volume and decrease in price. Our analysis of financial industry data
shows that this decline coincided with an anomalous increase in borrowed
shares, the selling of which would be a large fraction of the total trading
volume. The selling of borrowed shares cannot be explained by news events as
there is no corresponding increase in selling by share owners. A similar number
of shares were returned on a single day six days later. The magnitude and
coincidence of borrowing and returning of shares is evidence of a concerted
effort to drive down Citigroup's stock price and achieve a profit, i.e., a bear
raid. Interpretations and analyses of financial markets should consider the
possibility that the intentional actions of individual actors or coordinated
groups can impact market behavior. Markets are not sufficiently transparent to
reveal even major market manipulation events. Our results point to the need for
regulations that prevent intentional actions that cause markets to deviate from
equilibrium and contribute to crashes. Enforcement actions cannot reverse
severe damage to the economic system. The current "alternative" uptick rule
which is only in effect for stocks dropping by over 10% in a single day is
insufficient. Prevention may be achieved through improved availability of
market data and the original uptick rule or other transaction limitations.
2
votes
Antonios Garas, Frank Schweitzer, Shlomo Havlin
posted by Matúš Medo (18 May 2012)
We present a generalized method for calculating the k-shell structure of
weighted networks. The method takes into ac
80b
count both the weight and the degree
of a network, in such a way that in the absence of weights we resume the shell
structure obtained by the classic k-shell decomposition. In the presence of
weights we show that the method is able to partition the network in a more
refined way, without the need of any arbitrary threshold on the weight values.
Furthermore, by simulating spreading processes using the
Susceptible-Infectious-Recovered model in four different real weighted
networks, we show that the weighted k-shell decomposition method ranks the
nodes more accurately, by placing nodes with higher spreading potential into
shells closer to the core. In addition we demonstrate our new method on a real
economic network and show that the core calculated using the weighted k-shell
method is more meaningful from an economics perspective when compared to the
unweighted method.
2
votes
Daniel R. Parisi, Didier Sornette, Dirk Helbing
posted by Matúš Medo (18 May 2012)
We propose and document the evidence for an analogy between the dynamics of
granular counter-flows in the presence of bottlenecks or restrictions and
financial price formation processes. Using extensive simulations, we find that
the counter-flows of simulated pedestrians through a door display many stylized
facts observed in financial markets when the density around the door is
compared with the logarithm of the price. The stylized properties are present
already when the agents in the pedestrian model are assumed to display a
zero-intelligent behavior. If agents are given decision-making capacity and
adapt to partially follow the majority, periods of herding behavior may
additionally occur. This generates the very slow decay of the autocorrelation
of absolute return due to an intermittent dynamics. Our finding suggest that
the stylized facts in the fluctuations of the financial prices result from a
competition of two groups with opposite interests in the presence of a
constraint funneling the flow of transactions to a narrow band of prices.
2
votes
Eric Kemp-Benedict
posted by Eric Kemp-Benedict (17 April 2012)
In two previous papers the author developed a second-order price adjustment
(t\^atonnement) process. This paper extends the approach to include both
quantity and price adjustments. We demonstrate three results: a analogue to
physical energy, called "activity" arises naturally in the model, and is not
conserved in general; price and quantity trajectories must either end at a
local minimum of a scalar potential or circulate endlessly; and disturbances
into a subspace of substitutable commodities decay over time. From this we
argue,
64f
although we do not prove, that the model features global stability,
combined with local instability, a characteristic of many real markets.
Following these observations and a brief survey of empirical results for
price-setting and consumption behavior in markets for "real" goods (as opposed
to financial markets), we conjecture that Stigler and Becker's well-known
theory of consumer preference opens the possibility of substantial degeneracy
in commodity space, and therefore that price and quantity trajectories could
lie on a relatively low-dimensional subspace within the full commodity space.
2
votes
Xuqing Huang, Sergey V. Buldyrev, Huijuan Wang, Shlomo Havlin, H. Eugene Stanley
posted by Matúš Medo (18 May 2012)
A system of interdependent networks was recently found to be very vulnerable
since cascading failures that may lead to abrupt breakdown of the system. We
develop an analytical method, based on the percolation method developed for
single networks [M.E.J. Newman, Phys. Rev. Lett. {\bf 103}, 058701 (2009)], to
study the effect of clustering within the networks on the robustness of the
interdependent networks. We find that, in contrast to single networks where the
percolation threshold, $p_c$, does not change with clustering for site
percolation and {\it decreases} with clustering for bond percolation, $p_c$ for
interdependent networks {\it increases} when networks are more clustered.
2
votes
D. R. J. O'Neale, S. C. Hendy
posted by Matúš Medo (3 May 2012)
The total number of patents produced by a country (or the number of patents
produced per capita) is often used as an indicator for innovation. Here we
present evidence tha
7b4
t the distribution of patents amongst applicants within
many OECD countries is well-described by power laws with exponents that vary
between 1.66 (Japan) and 2.37 (Poland). Using simulations based on simple
preferential attachment-type rules that generate power laws, we find we can
explain some of the variation in exponents between countries, with countries
that have larger numbers of patents per applicant generally exhibiting smaller
exponents in both the simulated and actual data. Similarly we find that the
exponents for most countries are inversely correlated with other indicators of
innovation, such as R&D intensity or the ubiquity of export baskets. This
suggests that in more advanced economies, which tend to have smaller values of
the exponent, a greater proportion of the total number of patents are filed by
large companies than in less advanced countries.
2
votes
Kristina Lerman, Suradej Intagorn, Jeon-Hyung Kang, Rumi Ghosh
posted by Matúš Medo (16 December 2011)
The structure of a social network contains information useful for predicting
its evolution. Nodes that are "close" in some sense are more likely to become
linked in the future than more distant nodes. We show that structural
information can also help predict node activity. We use proximity to capture
the degree to which two nodes are "close" to each other in the network. In
addition to standard proximity metrics used in the link prediction task, such
as neighborhood overlap, we introduce new metrics that model different types of
interactions that can occur between network nodes. We argue that the "closer"
nodes are in a social network, the more similar will be their activity. We
study this claim using data about URL recommendation on social media sites Digg
and Twitter. We show that structural proximity of two users in the follower
graph is related to similarity of their activity, i.e., how many URLs they both
recommend. We also show that given friends' activity, knowing their proximity
to the user can help better predict which URLs the user will recommend. We
compare the performance of different proximity metrics on the activity
prediction task and find that some metrics lead to substantial performance
improvements.
1
vote
James McNerney, Brian D. Fath, Gerald Silverberg
posted by Matúš Medo (23 April 2012)
We study the structure of inter-industry relationships using networks of
money flows between industries in 20 national economies. We find these networks
var
638
y around a typical structure characterized by a Weibull link weight
distribution, exponential industry size distribution, and a common community
structure. The community structure is hierarchical, with the top level of the
hierarchy comprising five industry communities: food industries, chemical
industries, manufacturing industries, service industries, and extraction
industries.
3
votes
Vladimir Filimonov, Didier Sornette
posted by Matúš Medo (23 January 2012)
We introduce a new measure of activity of financial markets that provides a
direct access to their level of endogeneity. This measure quantifies how much
of price changes are due to endogenous feedback processes, as opposed to
exogenous news. For this, we calibrate the self-excited conditional Poisson
Hawkes model, which combines in a natural and parsimonious way exogenous
influences with self-excited dynamics, to the E-mini S&P 500 futures contracts
traded in the Chicago Mercantile Exchange from 1998 to 2010. We find that the
level of endogeneity has increased significantly from 1998 to 2010, with only
70% in 1998 to less than 30% since 2007 of the price changes resulting from
some revealed exogenous information. Analogous to nuclear plant safety
concerned with avoiding "criticality", our measure provides a direct
quantification of the distance of the financial market to a critical state
defined precisely as the limit of diverging trading activity in absence of any
external driving.
2
votes
Christopher Zapart
posted by Christopher Zapart (9 November 2010)
Talk slides presented at the Econophysics Colloquium 2010, November 4-6, Taipei, Taiwan.
Three models rooted in econophysics, behavioural finance and machine learning are introduced: NeuroEntropy, NeuroIsing and NeuroWavelet. Based on these models plus intuition, the author shows results from profitable short-term trading using his own money. Hence the talk provides a positive example of an application of econophysics to real trading the NIKKEI 225 futures and options. In contrast with theoretical economics, which tells us how markets should behave, empirical econophysics describes how markets really work.
1
vote
Daniel B. Larremore, Marshall Y. Carpenter, Edward Ott, Juan G. Restrepo
posted by Matúš Medo (23 April 2012)
We characterize the distributions of size and duration of avalanches
propagating in complex networks. By an avalanche we mean the sequence of events
initiated by the externally stimulated `excitation' of a network node, which
may, with some probability, then stimulate subsequent firings of the nodes to
which it is connected, resulting in a cascade of firings. This type of process
is relevant to a wide variety of situations, including neuroscience, cascading
failures on electrical power grids, and epidemology. We find that the
statistics of avalanches can be characterized in terms of the largest
eigenvalue and corresponding eigenvector of an appropriate adjacency matrix
which encodes the structure of the network. By using mean-field analyses,
previous studies of avalanches in networks have not considered the effect of
network structure on the distribution of size and duration of avalanches. Our
results apply to individual networks (rather than network ensembles) and
provide expressions for the distributions of size and duration of avalanches
starting at particular nodes in the network. These findings might find
application in the analysis of branching processes in networks, such as
cascading power grid failures and critical brain dynamics. In particular, our
results show that some experimental signatures of critical brain dynamics
(i.e., power-law distributions of size and duration of neuronal avalanches),
are robust to complex underlying network topologies.
2
votes
Peter Klimek, Ricardo Hausmann, Stefan Thurner
posted by Matúš Medo (16 December 2011)
We show that world trade network datasets contain empirical evidence that the
dynamics of innovation in the world economy follows indeed the concept of
creative destruction, as proposed by J.A. Schumpeter more than half a century
ago. National economies can be viewed as complex, evolving systems, driven by a
stream of appearance and disappearance of goods and services. Products appear
in bursts of creative cascades. We find that products systematically tend to
co-appear, and that product appearances lead to massive disappearance events of
existing products in the following years. The opposite - disappearances
followed by periods of appearances - is not observed. This is an empirical
validation of the dominance of cascading competitive replacement events on the
scale of national economies, i.e. creative destruction. We find a tendency that
more complex products drive out less complex ones, i.e. progress has a
direction. Finally we show that the growth trajectory of a country's product
output diversity can be understood by a recently proposed evolutionary model of
Schumpeterian economic dynamics.
1
vote
Tomasz Suslo
posted by tomasz suslo (19 May 2012)
The aim of the paper is to derive for the neg
599
ative correlation function with
a time parameter an asymptotic disjunction of the numerical generalized
least-squares estimator of an unknown constant mean of random field in fact the
correct classic generalized least-squares estimator of an unknown constant mean
of the field.
1
vote
Emanuele Cozzo, Alex Arenas, Yamir Moreno
posted by Matúš Medo (18 May 2012)
We
7e4
extend the formalism of Random Boolean Networks with canalizing rules to
multilevel complex networks. The formalism allows to model genetic networks in
which each gene might take part in more than one signaling pathway. We use a
semi-annealed approach to study the stability of this class of models when
coupled in a multiplex network and show that the analytical results are in good
agreement with numerical simulations. Our main finding is that the multiplex
structure provides a mechanism for the stabilization of the system and of
chaotic regimes of individual layers. Our results help understanding why some
genetic networks that are theoretically expected to operate in the chaotic
regime can actually display dynamical stability.
1
vote
Julio Pombo-Romero, Luis M. Varela & Carlos J. Ricoy
posted by Julio Pombo (11 May 2012)
The existence of imitative behavior among consumers is a well-known phenomenon in the field of Economics. This behavior is especially common in markets determined by a high degree of innovation, asymmetric information and/or price-inelastic demand, features that exist in the pharmaceutical market. This paper presents evidence of the existence of imitative behavior among primary care physicians in Galicia (Spain) when choosing treatments for their patients. From this and other evidence, we propose a dynamic model for determining the entry of new drugs into the market. To do this, we introduce the structure of the organization of primary health care centers and the presence of groups of doctors who are specially interrelated, as well as the existence of commercial pressure on doctors. For modeling purposes, physicians are treated as spins connected in an exponentially distributed complex network of the Watts-Strogatz type. The proposed model provides an explanation for the differences observed in the patterns of the introduction of technological innovations in different regions. The main cause of these differences is the different structure of relationships among consumers, where the existence of small groups that show a higher degree of coordination over the average is particularly influential. The evidence presented, together with the proposed model, might be useful for the design of optimal strategies for the introduction of new drugs, as well as for planning policies to manage pharmaceutical expenditure.
1
vote
G. Charles-Cadogan
posted by gocadog (1 May 2012)
This paper extends the solution space for decision theory by introducing a behavioural operator that (1) transforms probability domains, and (2) generates sample paths for confidence from catalytic fuzzy or ambiguous sources. First, we prove that average sample paths for confidence/sentiment, generated from within and across source sets, differ. So conjugate priors should be used to mitigate the difference. Second, we identify loss aversion as the source of Langevin type friction that explains the popularity of Ornstein-Uhlenbeck processes for modeling mean reversion of sample paths for behaviour. However, in large markets, ergodic confidence levels, imbued by Lichtenstein and Slovic (1973) and Yaari (1987) type preference reversal operations, predict bubbles and crashes almost surely. Third, simulation of the model confirms that the distribution of priors, on Gilboa and Schmeilder (1989) source sets, controls confidence momentum and term structure of fields of confidence. For example, it explains the asset pricing ''anomaly" of sensitivity of momentum trading strategies to starting dates in Moskowitz, Ooi, Pedersen (2012}). Fourth, we provide several applications including but not limited to a sentiment based computer trading algorithm. For instance, our computer generated field of confidence mimics trends in CBOE VIX daily sentiment index, and survey driven Gallup Economic Confidence Index (GEDCI) sounding in Tversky and Wakker (1995) type impact events. We show how GEDCI splits VIX into source sets that depict term structures of confidence for relative hope and fear. A simple statistical test for relative confidence beta upholds our theory that the average sample path for confidence/sentiment differs within and across source sets. And we identify a VIX source set confidence beta arbitrage strategy.
1
vote
Xiaohan Zhao, Alessandra Sala, Christo Wilson, Xi 9cf ao Wang, Sabrina Gaito, Haitao Zheng, Ben Y.
posted by Matúš Medo (18 May 2012)
Data confidentiality policies at major social network providers have severely
limited researchers' access to large-scale datasets. The biggest impact has
been on the study of network dynamics, where researchers have studied citation
graphs and content-sharing networks, but few have analyzed detailed dynamics in
the massive social networks that dominate the web today. In this paper, we
present results of analyzing detailed dynamics in the Renren social network,
covering a period of 2 years when the network grew from 1 user to 19 million
users and 199 million edges. Rather than validate a single model of network
dynamics, we analyze dynamics at different granularities (user-, community- and
network- wide) to determine how much, if any, users are influenced by dynamics
processes at different scales. We observe in- dependent predictable processes
at each level, and find that while the growth of communities has moderate and
sustained impact on users, significant events such as network merge events have
a strong but short-lived impact that is quickly dominated by the continuous
arrival of new users.
2
votes
Huy Nguyen, Rong Zheng
posted by Matúš Medo (23 April 2012)
Influence maximization is the problem of finding a small set of seed nodes in
a social network that maximizes the spread of influence under a certain
diffusion model. The Greedy algorithm for influence maximization first proposed
by Kempe, later improved by Leskovec suffers from two sources of computational
deficiency: 1) the need to evaluate many candidate nodes before selecting a new
seed in each round, and 2) the calculation of the influence spread of any seed
set relies on Monte-Carlo simulations. In this work, we tackle both problems by
devising efficient algorithms to compute influence spread and determine the
best candidate for seed selection. The fundamental insight behind the proposed
algorithms is the linkage between influence spread determination and belief
propagation on a directed acyclic graph (DAG). Experiments using real-world
social network graphs with scales ranging from thousands to millions of edges
demonstrate the superior performance of the proposed algorithms with moderate
computation costs.
1
vote
Nathan Oken Hodas, Kristina Lerman
posted by Matúš Medo (18 May 2012)
How far and how fast does information spre
95e
ad in social media? Researchers
have recently examined a number of factors that affect information diffusion in
online social networks, including: the novelty of information, users' activity
levels, who they pay attention to, and how they respond to friends'
recommendations. Using URLs as markers of information, we carry out a detailed
study of retweeting, the primary mechanism by which information spreads on the
Twitter follower graph. Our empirical study examines how users respond to an
incoming stimulus, i.e., a tweet (message) from a friend, and reveals that
%retweeting behavior is constrained by a few simple principles. the "principle
of least effort" combined with limited attention plays a dominant role in
retweeting behavior. Specifically, we observe that users retweet information
when it is most visible, such as when it near the top of their Twitter stream.
Moreover, our measurements quantify how a user's limited attention is divided
among incoming tweets, providing novel evidence that highly connected
individuals are less likely to propagate an arbitrary tweet. Our study
indicates that the finite ability to process incoming information constrains
social contagion, and we conclude that rapid decay of visibility is the primary
barrier to information propagation online.
2
votes
Ban Zheng, Eric Moulines, Frédéric Abergel
posted by Matúš Medo (11 April 2012)
A limit order book provides information on available limit order prices and
their volumes. Based on these quantities, we give an empirical result on the
relationship between the bid-ask liquidity balance and trade sign and we show
that liquidity balance on best bid/best ask is quite informative for predicting
the future market order's direction. Moreover, we define price jump as a
83b
sell
(buy) market order arrival which is executed at a price which is smaller
(larger) than the best bid (best ask) price at the moment just after the
precedent market order arrival. Features are then extracted related to limit
order volumes, limit order price gaps, market order information and limit order
event information. Logistic regression is applied to predict the price jump
from the limit order book's feature. LASSO logistic regression is introduced to
help us make variable selection from which we are capable to highlight the
importance of different features in predicting the future price jump. In order
to get rid of the intraday data seasonality, the analysis is based on two
separated datasets: morning dataset and afternoon dataset. Based on an analysis
on forty largest French stocks of CAC40, we find that trade sign and market
order size as well as the liquidity on the best bid (best ask) are consistently
informative for predicting the incoming price jump.
1
vote
Koen Frenken and Stefan Mendritzki
posted by Eric Kemp-Benedict (1 May 2012)
Modularity is an important concept in evolutionary theorizing but lack of a consistent definition renders study difficult. Using the generalized NK-model of fitness landscapes, we differentiate modularity from decomposability. Modular and decomposable systems are both composed of subsystems, but in the former, these subsystems are connected via interface standards, while in the latter, subsystems are completely isolated. We derive the optimal level of modularity, which minimizes the time required to globally optimize a system, both for the case of two-layered systems and for the general case of multi-layered hierarchical systems containing modules within modules. This derivation supports the hypothesis of modularity as a mechanism to increase the speed of evolution. Our formal definition clarifies the concept of modularity and provides a framework and an analytical baseline for further research.
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