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Simple Adaptive Filter as a Part of Information System for Market Data Analysis

100%

Janowicz M. , Kietlińska K. , Zembrzuski A.

nr nr 4

221-228

Application of the simple least mean squares (LMS) adaptive filter of to the Warsaw Exchange Market (GPW) has been analyzed using stocks belonging to WIG20 group as examples. LMS filter has been used as a binary classifier, that is, to forecast the sign of changes in the (normalized) stock values. Two kinds of data has been used, namely, the differenced and double-differenced normalized close values of stocks. It has been shown that while the predictive power of LMS filter is virtually zero for the differenced series, it rises significantly in the case of double-differenced series for all analyzed stocks. We attribute this to the better stationarity properties of the double-differenced time series. (original abstract)

Different Sources of Market Information and Product Innovativeness

80%

Dąbrowski D.

nr nr 3

7-23

The purpose of this study is to identify whether the gathering of market information from different sources - i.e. from customers, competitors and other entities - is related to product innovativeness. The relationships proposed so far have not been empirically investigated but they can have important theoretical and practical implications for product innovation. To achieve the purpose of the paper data concerning 287 new products were used by applying confirmatory factor analysis and structural equation modelling. The findings indicate that the obtaining of market information from customers and market entities, other than customers and competitors, has a positive impact on product innovativeness, but there was no such relationship in the case of gathering information from competitors. (original abstract)