Name
Abstract | ||
|---|---|---|
In this article we present a new method for the analysis of dependencies in case of multivariate time series. In this approach, we assume that the set of time series representing the various financial instruments creates a multidimensional variable. Such a multidimensional variable is decomposed into independent components which enable to analyze the morphology of given financial instruments and to identify the hidden interdependencies. We propose a new multiplicative version of the Natural Gradient ICA algorithm that could be used in automated trading systems or modeling environments. The presented method is tested on real stock markets data. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-29350-4_72 | ICAISC (2) |
Keywords | Field | DocType |
time series,multivariate time series,financial instrument,multiplicative ica algorithm,automated trading system,multidimensional variable,natural gradient ica algorithm,new method,hidden interdependency,new multiplicative version,financial market,various financial instrument,interaction analysis | Interdependence,Data mining,Multiplicative function,Computer science,Financial instrument,Artificial intelligence,Financial market,Algorithmic trading,Natural gradient,Multivariate statistics,Algorithm,Independent component analysis,Machine learning | Conference |
Volume | ISSN | Citations |
7268 | 0302-9743 | 1 |
PageRank | References | Authors |
0.37 | 3 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ryszard Szupiluk | 1 | 38 | 8.97 |
| Piotr Wojewnik | 2 | 20 | 6.32 |
| Tomasz Zabkowski | 3 | 32 | 11.28 |