The Analysis of Financial Time Series Data by Independent Component Analysis

2004 ◽  
pp. 174-180
Author(s):  
E. R. Pike ◽  
E G Klepfish
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Time Series Data ◽  
Financial Time Series ◽  
Component Analysis ◽  
Independent Component ◽  
Series Data ◽  
Financial Time

scholarly journals A First Application of Independent Component Analysis to Extracting Structure from Stock Returns

1997 ◽  
Vol 08 (04) ◽  
pp. 473-484 ◽  
Author(s):  
Andrew D. Back ◽  
Andreas S. Weigend
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Stock Returns ◽  
Stock Prices ◽  
Financial Time Series ◽  
Component Analysis ◽  
Independent Component ◽  
Signal Processing Technique ◽  
Independent Components ◽  
Financial Time

This paper explores the appliation of a signal processing technique known as independent component analysis (ICA) or blind source separation to multivariate financial time series such as a portfolio of stocks. The key idea of ICA is to linearly map the observed multivariate time series into a new space of statistically independent components (ICs). We apply ICA to three years of daily returns of the 28 largest Japanese stocks and compare the results with those obtained using principal component analysis. The results indicate that the estimated ICs fall into two categories, (i) infrequent large shocks (responsible for the major changes in the stock prices), and (ii) frequent smaller fluctuations (contributing little to the overall level of the stocks). We show that the overall stock price can be reconstructed surprisingly well by using a small number of thresholded weighted ICs. In contrast, when using shocks derived from principal components instead of independent components, the reconstructed price is less similar to the original one. ICA is shown to be a potentially powerful method of analyzing and understanding driving mechanisms in financial time series. The application to portfolio optimization is described in Chin and Weigend (1998).

scholarly journals Dynamically Measuring Statistical Dependencies in Multivariate Financial Time Series Using Independent Component Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-14 ◽  
Author(s):  
Nauman Shah ◽  
Stephen J. Roberts
Keyword(s):  
Time Series ◽  
Independent Component Analysis ◽  
Financial Time Series ◽  
Synthetic Data ◽  
Component Analysis ◽  
Independent Component ◽  
Financial Time ◽  
Proxy Measure ◽  
Non Gaussian ◽  
Multivariate Financial Time Series

We present a computationally tractable approach to dynamically measure statistical dependencies in multivariate non-Gaussian signals. The approach makes use of extensions of independent component analysis to calculate information coupling, as a proxy measure for mutual information, between multiple signals and can be used to estimate uncertainty associated with the information coupling measure in a straightforward way. We empirically validate relative accuracy of the information coupling measure using a set of synthetic data examples and showcase practical utility of using the measure when analysing multivariate financial time series.

scholarly journals An Application of the Associate Hopfield Network for Pattern Matching in Chart Analysis

2021 ◽  
Vol 11 (9) ◽  
pp. 3876
Author(s):  
Weiming Mai ◽  
Raymond S. T. Lee
Keyword(s):  
Time Series ◽  
Pattern Matching ◽  
Time Series Data ◽  
Financial Time Series ◽  
Noisy Data ◽  
Hopfield Neural Network ◽  
Hopfield Network ◽  
Series Data ◽  
Financial Time ◽  
Chart Patterns

Chart patterns are significant for financial market behavior analysis. Lots of approaches have been proposed to detect specific patterns in financial time series data, most of them can be categorized as distance-based or training-based. In this paper, we applied a trainable continuous Hopfield Neural Network for financial time series pattern matching. The Perceptually Important Points (PIP) segmentation method is used as the data preprocessing procedure to reduce the fluctuation. We conducted a synthetic data experiment on both high-level noisy data and low-level noisy data. The result shows that our proposed method outperforms the Template Based (TB) and Euclidean Distance (ED) and has an advantage over Dynamic Time Warping (DTW) in terms of the processing time. That indicates the Hopfield network has a potential advantage over other distance-based matching methods.

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