Back A D, Weigend A S
The Institute of Physical and Chemical Research (RIKEN), Saitama, Japan.
Int J Neural Syst. 1997 Aug;8(4):473-84. doi: 10.1142/s0129065797000458.
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).
本文探讨了一种称为独立成分分析（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元时间序列线性映射到统计独立成分（IC）的新空间中。我们将ICA应用于28只最大的日本股票的三年日收益数据，并将结果与使用主成分分析得到的结果进行比较。结果表明，估计出的IC可分为两类：（i）不频繁的大幅冲击（导致股价的主要变化），以及（ii）频繁的较小波动（对股票的总体水平贡献不大）。我们表明，通过使用少量经过阈值处理的加权IC，可以非常好地重建总体股价。相比之下，当使用主成分而非独立成分产生的冲击时，重建价格与原始价格的相似度较低。ICA被证明是一种分析和理解金融时间序列驱动机制的潜在强大方法。Chin和Weigend（1998）中描述了其在投资组合优化中的应用。
