82,546 research outputs found
Evolution and anti-evolution in a minimal stock market model
We present a novel microscopic stock market model consisting of a large
number of random agents modeling traders in a market. Each agent is
characterized by a set of parameters that serve to make iterated predictions of
two successive returns. The future price is determined according to the offer
and the demand of all agents. The system evolves by redistributing the capital
among the agents in each trading cycle. Without noise the dynamics of this
system is nearly regular and thereby fails to reproduce the stochastic return
fluctuations observed in real markets. However, when in each cycle a small
amount of noise is introduced we find the typical features of real financial
time series like fat-tails of the return distribution and large temporal
correlations in the volatility without significant correlations in the price
returns. Introducing the noise by an evolutionary process leads to different
scalings of the return distributions that depend on the definition of fitness.
Because our realistic model has only very few parameters, and the results
appear to be robust with respect to the noise level and the number of agents we
expect that our framework may serve as new paradigm for modeling self generated
return fluctuations in markets.Comment: 13 pages, 5 figure
Hierarchical Structure in Financial Markets
I find a topological arrangement of stocks traded in a financial market which
has associated a meaningful economic taxonomy. The topological space is a graph
connecting the stocks of the portfolio analyzed. The graph is obtained starting
from the matrix of correlation coefficient computed between all pairs of stocks
of the portfolio by considering the synchronous time evolution of the
difference of the logarithm of daily stock price. The hierarchical tree of the
subdominant ultrametric space associated with the graph provides information
useful to investigate the number and nature of the common economic factors
affecting the time evolution of logarithm of price of well defined groups of
stocks.Comment: 11 pages, 3 figures with 7 panel
Identification of clusters of companies in stock indices via Potts super-paramagnetic transitions
The clustering of companies within a specific stock market index is studied
by means of super-paramagnetic transitions of an appropriate q-state Potts
model where the spins correspond to companies and the interactions are
functions of the correlation coefficients determined from the time dependence
of the companies' individual stock prices. The method is a generalization of
the clustering algorithm by Domany et. al. to the case of anti-ferromagnetic
interactions corresponding to anti-correlations. For the Dow Jones Industrial
Average where no anti-correlations were observed in the investigated time
period, the previous results obtained by different tools were well reproduced.
For the Standard & Poor's 500, where anti-correlations occur, repulsion between
stocks modify the cluster structure.Comment: 4 pages; changed conten
From market games to real-world markets
This paper uses the development of multi-agent market models to present a
unified approach to the joint questions of how financial market movements may
be simulated, predicted, and hedged against. We examine the effect of different
market clearing mechanisms and show that an out-of-equilibrium clearing process
leads to dynamics that closely resemble real financial movements. We then show
that replacing the `synthetic' price history used by these simulations with
data taken from real financial time-series leads to the remarkable result that
the agents can collectively learn to identify moments in the market where
profit is attainable. We then employ the formalism of Bouchaud and Sornette in
conjunction with agent based models to show that in general risk cannot be
eliminated from trading with these models. We also show that, in the presence
of transaction costs, the risk of option writing is greatly increased. This
risk, and the costs, can however be reduced through the use of a delta-hedging
strategy with modified, time-dependent volatility structure.Comment: Presented at APFA2 (Liege) July 2000. Proceedings: Eur. Phys. J. B
Latex file + 10 .ps figs. [email protected]
Deep Learning in a Generalized HJM-type Framework Through Arbitrage-Free Regularization
We introduce a regularization approach to arbitrage-free factor-model
selection. The considered model selection problem seeks to learn the closest
arbitrage-free HJM-type model to any prespecified factor-model. An asymptotic
solution to this, a priori computationally intractable, problem is represented
as the limit of a 1-parameter family of optimizers to computationally tractable
model selection tasks. Each of these simplified model-selection tasks seeks to
learn the most similar model, to the prescribed factor-model, subject to a
penalty detecting when the reference measure is a local martingale-measure for
the entire underlying financial market. A simple expression for the penalty
terms is obtained in the bond market withing the affine-term structure setting,
and it is used to formulate a deep-learning approach to arbitrage-free affine
term-structure modelling. Numerical implementations are also performed to
evaluate the performance in the bond market.Comment: 23 Pages + Reference
A prognosis oriented microscopic stock market model
We present a new microscopic stochastic model for an ensemble of interacting
investors that buy and sell stocks in discrete time steps via limit orders
based on individual forecasts about the price of the stock. These orders
determine the supply and demand fixing after each round (time step) the new
price of the stock according to which the limited buy and sell orders are then
executed and new forecasts are made. We show via numerical simulation of this
model that the distribution of price differences obeys an exponentially
truncated Levy-distribution with a self similarity exponent mu~5.Comment: 14 pages RevTeX, 5 eps-figures include
Mesoscopic Community Structure of Financial Markets Revealed by Price and Sign Fluctuations
The mesoscopic organization of complex systems, from financial markets to the
brain, is an intermediate between the microscopic dynamics of individual units
(stocks or neurons, in the mentioned cases), and the macroscopic dynamics of
the system as a whole. The organization is determined by "communities" of units
whose dynamics, represented by time series of activity, is more strongly
correlated internally than with the rest of the system. Recent studies have
shown that the binary projections of various financial and neural time series
exhibit nontrivial dynamical features that resemble those of the original data.
This implies that a significant piece of information is encoded into the binary
projection (i.e. the sign) of such increments. Here, we explore whether the
binary signatures of multiple time series can replicate the same complex
community organization of the financial market, as the original weighted time
series. We adopt a method that has been specifically designed to detect
communities from cross-correlation matrices of time series data. Our analysis
shows that the simpler binary representation leads to a community structure
that is almost identical with that obtained using the full weighted
representation. These results confirm that binary projections of financial time
series contain significant structural information.Comment: 15 pages, 7 figure
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