A first application of independent component analysis to extracting structure from stock returns
A first application of independent component analysis to extracting structure from stock returns
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DOI：
10.1142/s0129065797000458
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发表时间：
1997-08-01
影响因子：
8
通讯作者：
Weigend, AS
中科院分区：
文献类型：
--
作者：
Back, AD;Weigend, AS
本文探讨了一种被称为独立分量分析（ICA）或盲源分离的信号处理技术在多元金融时间序列（如股票投资组合）中的应用。ICA的关键思想是将观测到的多变量时间序列线性映射到统计独立分量（ICs）的新空间。我们将ICA应用于日本28只最大股票的三年日收益，并将结果与主成分分析结果进行比较。结果表明，估计的投资组合分为两类，(i)不常见的大冲击（导致股票价格的重大变化）和（ii）频繁的较小波动（对股票的总体水平贡献不大）。我们表明，使用少量阈值加权集成电路可以很好地重建整体股票价格。相反，当使用来自主成分而不是独立成分的冲击时，重建的价格与原始价格的相似性较小。ICA被证明是一种潜在的分析和理解金融时间序列驱动机制的强大方法。Chin和Weigend（1998）描述了其在投资组合优化中的应用。
This paper explores the application 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).