Independent variable selection: Application of independent component analysis to forecasting a stock index

• Published in: Journal of asset management 2005-12, Vol.6 (4), p.248-258
• Main Authors: Cichocki, Andrzej, Stansell, Stanley R, Leonowicz, Zbigniew, Buck, James
• Format: Article
• Language: English
• Subjects: Algorithms; Decomposition; Deflation; Economic forecasting; Forecasting; Multivariate analysis; Neural networks; Principal components analysis; Random variables; Rates of return; Stock indexing; Studies; Time series
• ISSN: 1470-8272; 1479-179X
• DOI: 10.1057/palgrave.jam.2240179

Forecasting of financial time series requires the use of a possibly large set of input (explanatory) variables drawn from a very large set of potential inputs. Selection of a meaningful and useful subset of input variables is a formidable task. How to find a reasonable transformation for a large set of multivariate data is a very common problem in many areas of science. This paper proposes a technique called independent component analysis (ICA) to extract the independent components (ICs) from monthly time series on a wide range of economic variables. This procedure will reduce the number of explanatory variables by reducing the set of financial and economic information to a much smaller subset of significant or dominant ICs, which it is hoped will capture most of the useful information. Removal of the non-significant components representing random elements in each of the sets of economic data should make it much easier to identify relationships between the ICs and the stock indexes. Properly estimated ICs are independent of each other.