21,030 research outputs found
Integrating Independent Component Analysis and Principal Component Analysis with Neural Network to Predict Chinese Stock Market
We investigate the statistical behaviors of Chinese stock market fluctuations by independent component analysis. The independent component analysis (ICA) method is integrated into the neural network model. The proposed approach uses ICA method to analyze the input data of neural network and can obtain the latent independent components (ICs). After analyzing and removing the IC that represents noise, the rest of ICs are used as the input of neural network. In order to forect the fluctuations of Chinese stock market, the data of Shanghai Composite Index is selected and analyzed, and we compare the forecasting performance of the proposed model with those of common BP model integrating principal component analysis (PCA) and single BP model. Experimental results show that the proposed model outperforms the other two models no matter in relatively small or relatively large sample, and the performance of BP model integrating PCA is closer to that of the proposed model in relatively large sample. Further, the prediction results on the points where the prices fluctuate violently by the above three models relatively deviate from the corresponding real market data
Quantum Brownian motion model for the stock market
It is believed by the majority today that the efficient market hypothesis is
imperfect because of market irrationality. Using the physical concepts and
mathematical structures of quantum mechanics, we construct an econophysics
framework for the stock market, based on which we analogously map massive
numbers of single stocks into a reservoir consisting of many quantum harmonic
oscillators and their stock index into a typical quantum open system--a quantum
Brownian particle. In particular, the irrationality of stock transactions is
quantitatively considered as the Planck constant within Heisenberg's
uncertainty relationship of quantum mechanics in an analogous manner. We
analyze real stock data of Shanghai Stock Exchange of China and investigate
fat-tail phenomena and non-Markovian behaviors of the stock index with the
assistance of the quantum Brownian motion model, thereby interpreting and
studying the limitations of the classical Brownian motion model for the
efficient market hypothesis from a new perspective of quantum open system
dynamics
Multifractal Behavior of the Korean Stock-market Index KOSPI
We investigate multifractality in the Korean stock-market index KOSPI. The
generalized th order height-height correlation function shows multiscaling
properties. There are two scaling regimes with a crossover time around min. We consider the original data sets and the modified data sets
obtained by removing the daily jumps, which occur due to the difference between
the closing index and the opening index. To clarify the origin of the
multifractality, we also smooth the data through convolution with a Gaussian
function. After convolution we observe that the multifractality disappears in
the short-time scaling regime , but remains in the long-time scaling
regime , regardless of whether or not the daily jumps are removed. We
suggest that multifractality in the short-time scaling regime is caused by the
local fluctuations of the stock index. But the multifractality in the long-time
scaling regime appears to be due to the intrinsic trading properties, such as
herding behavior, information outside the market, the long memory of the
volatility, and the nonlinear dynamics of the stock market.Comment: 12 pages, 4 figures. Physica A, in pres
Agent-based model with asymmetric trading and herding for complex financial systems
Background: For complex financial systems, the negative and positive
return-volatility correlations, i.e., the so-called leverage and anti-leverage
effects, are particularly important for the understanding of the price
dynamics. However, the microscopic origination of the leverage and
anti-leverage effects is still not understood, and how to produce these effects
in agent-based modeling remains open. On the other hand, in constructing
microscopic models, it is a promising conception to determine model parameters
from empirical data rather than from statistical fitting of the results.
Methods: To study the microscopic origination of the return-volatility
correlation in financial systems, we take into account the individual and
collective behaviors of investors in real markets, and construct an agent-based
model. The agents are linked with each other and trade in groups, and
particularly, two novel microscopic mechanisms, i.e., investors' asymmetric
trading and herding in bull and bear markets, are introduced. Further, we
propose effective methods to determine the key parameters in our model from
historical market data.
Results: With the model parameters determined for six representative
stock-market indices in the world respectively, we obtain the corresponding
leverage or anti-leverage effect from the simulation, and the effect is in
agreement with the empirical one on amplitude and duration. At the same time,
our model produces other features of the real markets, such as the fat-tail
distribution of returns and the long-term correlation of volatilities.
Conclusions: We reveal that for the leverage and anti-leverage effects, both
the investors' asymmetric trading and herding are essential generation
mechanisms. These two microscopic mechanisms and the methods for the
determination of the key parameters can be applied to other complex systems
with similar asymmetries.Comment: 17 pages, 6 figure
Long-term correlations and multifractal nature in the intertrade durations of a liquid Chinese stock and its warrant
Intertrade duration of equities is an important financial measure
characterizing the trading activities, which is defined as the waiting time
between successive trades of an equity. Using the ultrahigh-frequency data of a
liquid Chinese stock and its associated warrant, we perform a comparative
investigation of the statistical properties of their intertrade duration time
series. The distributions of the two equities can be better described by the
shifted power-law form than the Weibull and their scaled distributions do not
collapse onto a single curve. Although the intertrade durations of the two
equities have very different magnitude, their intraday patterns exhibit very
similar shapes. Both detrended fluctuation analysis (DFA) and detrending moving
average analysis (DMA) show that the 1-min intertrade duration time series of
the two equities are strongly correlated. In addition, both multifractal
detrended fluctuation analysis (MFDFA) and multifractal detrending moving
average analysis (MFDMA) unveil that the 1-min intertrade durations possess
multifractal nature. However, the difference between the two singularity
spectra of the two equities obtained from the MFDMA is much smaller than that
from the MFDFA.Comment: 10 latex pages, 4 figure
- …