that the proposed method provides markedly high hit ratios for forecasting m
ovement
directions of the constituents in the KOSPI and HSI. Since our experiments computed the
one-day-ahead predictions using rolling windows data of a long period, the results are not
the product of limited sample selection but reliable with all the available information at
that time. Our results also verifies the co-movement effect between the Korean (or
Hong Kong) stock market and the US stock market because of the usage of S&P 500 and
exchange rates.
As a future study, a theoretical study on the performance of the proposed method is of
worth. The clustering of the co-moved stocks according to the biplot needs a further
investigation. The theoretical analysis of the better performance on forecasting the
constituents is also worth studying. Moreover, other feature selection methods, for
example, deep belief networks (DBN), may be also efficient to extract the features of the
stock prices for classifiers, which is subject to another future research.
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