27 research outputs found
Statistical Investigation of Connected Structures of Stock Networks in Financial Time Series
In this study, we have investigated factors of determination which can affect
the connected structure of a stock network. The representative index for
topological properties of a stock network is the number of links with other
stocks. We used the multi-factor model, extensively acknowledged in financial
literature. In the multi-factor model, common factors act as independent
variables while returns of individual stocks act as dependent variables. We
calculated the coefficient of determination, which represents the measurement
value of the degree in which dependent variables are explained by independent
variables. Therefore, we investigated the relationship between the number of
links in the stock network and the coefficient of determination in the
multi-factor model. We used individual stocks traded on the market indices of
Korea, Japan, Canada, Italy and the UK. The results are as follows. We found
that the mean coefficient of determination of stocks with a large number of
links have higher values than those with a small number of links with other
stocks. These results suggest that common factors are significantly
deterministic factors to be taken into account when making a stock network.
Furthermore, stocks with a large number of links to other stocks can be more
affected by common factors.Comment: 11 pages, 2 figure
Relationship between degree of efficiency and prediction in stock price changes
This study investigates empirically whether the degree of stock market
efficiency is related to the prediction power of future price change using the
indices of twenty seven stock markets. Efficiency refers to weak-form efficient
market hypothesis (EMH) in terms of the information of past price changes. The
prediction power corresponds to the hit-rate, which is the rate of the
consistency between the direction of actual price change and that of predicted
one, calculated by the nearest neighbor prediction method (NN method) using the
out-of-sample. In this manuscript, the Hurst exponent and the approximate
entropy (ApEn) are used as the quantitative measurements of the degree of
efficiency. The relationship between the Hurst exponent, reflecting the various
time correlation property, and the ApEn value, reflecting the randomness in the
time series, shows negative correlation. However, the average prediction power
on the direction of future price change has the strongly positive correlation
with the Hurst exponent, and the negative correlation with the ApEn. Therefore,
the market index with less market efficiency has higher prediction power for
future price change than one with higher market efficiency when we analyze the
market using the past price change pattern. Furthermore, we show that the Hurst
exponent, a measurement of the long-term memory property, provides more
significant information in terms of prediction of future price changes than the
ApEn and the NN method.Comment: 10 page
Topological Properties of the Minimal Spanning Tree in Korean and American Stock Markets
We investigate a factor that can affect the number of links of a specific
stock in a network between stocks created by the minimal spanning tree (MST)
method, by using individual stock data listed on the S&P500 and KOSPI. Among
the common factors mentioned in the arbitrage pricing model (APM), widely
acknowledged in the financial field, a representative market index is
established as a possible factor. We found that the correlation distribution,
, of 400 stocks taken from the S&P500 index shows a very similar
with that of the Korean stock market and those deviate from the correlation
distribution of time series removed a nonlinearity by the surrogate method. We
also shows that the degree distribution of the MSTs for both stock markets
follows a power-law distribution with the exponent 2.1, while the
degree distribution of the time series eliminated a nonlinearity follows an
exponential distribution with the exponent, . Furthermore the
correlation, , between the degree k of individual stock, , and
the market index, , follows a power-law distribution, , with the exponent \gamma_{\textrm{S&P500}} \approx 0.16 and
, respectively. Thus, regardless of the
markets, the indivisual stocks closely related to the common factor in the
market, the market index, are likely to be located around the center of the
network between stocks, while those weakly related to the market index are
likely to be placed in the outside
The effect of a market factor on information flow between stocks using minimal spanning tree
We empirically investigated the effects of market factors on the information
flow created from N(N-1)/2 linkage relationships among stocks. We also examined
the possibility of employing the minimal spanning tree (MST) method, which is
capable of reducing the number of links to N-1. We determined that market
factors carry important information value regarding information flow among
stocks. Moreover, the information flow among stocks evidenced time-varying
properties according to the changes in market status. In particular, we noted
that the information flow increased dramatically during periods of market
crises. Finally, we confirmed, via the MST method, that the information flow
among stocks could be assessed effectively with the reduced linkage
relationships among all links between stocks from the perspective of the
overall market
Fat tails in financial return distributions revisited: evidence from the Korean stock market
This study empirically re-examines fat tails in stock return distributions by applying statistical methods to an extensive dataset taken from the Korean stock market. The tails of the return distributions are shown to be much fatter in recent periods than in past periods and much fatter for small-capitalization stocks than for large-capitalization stocks. After controlling for the 1997 Korean foreign currency crisis and using the GARCH filter models to control for volatility clustering in the returns, the fat tails in the distribution of residuals are found to persist. We show that market crashes and volatility clustering may not sufficiently account for the existence of fat tails in return distributions. These findings are robust regardless of period or type of stock group
Effect of changing data size on eigenvalues in the Korean and Japanese stock markets
In this study, we attempted to determine how eigenvalues change, according to
random matrix theory (RMT), in stock market data as the number of stocks
comprising the correlation matrix changes. Specifically, we tested for changes
in the eigenvalue properties as a function of the number and type of stocks in
the correlation matrix. We determined that the value of the eigenvalue
increases in proportion with the number of stocks. Furthermore, we noted that
the largest eigenvalue maintains its identical properties, regardless of the
number and type, whereas other eigenvalues evidence different features
Market Efficiency in Foreign Exchange Markets
We investigate the relative market efficiency in financial market data, using
the approximate entropy(ApEn) method for a quantification of randomness in time
series. We used the global foreign exchange market indices for 17 countries
during two periods from 1984 to 1998 and from 1999 to 2004 in order to study
the efficiency of various foreign exchange markets around the market crisis. We
found that on average, the ApEn values for European and North American foreign
exchange markets are larger than those for African and Asian ones except Japan.
We also found that the ApEn for Asian markets increase significantly after the
Asian currency crisis. Our results suggest that the markets with a larger
liquidity such as European and North American foreign exchange markets have a
higher market efficiency than those with a smaller liquidity such as the
African and Asian ones except Japan