英文标题：
《Random selection of factors preserves the correlation structure in a
linear factor model to a high degree》
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作者：
Antti J. Tanskanen, Jani Lukkarinen, Kari Vatanen
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最新提交年份：
2018
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英文摘要：
In a very high-dimensional vector space, two randomly-chosen vectors are almost orthogonal with high probability. Starting from this observation, we develop a statistical factor model, the random factor model, in which factors are chosen at random based on the random projection method. Randomness of factors has the consequence that covariance matrix is well preserved in a linear factor representation. It also enables derivation of probabilistic bounds for the accuracy of the random factor representation of time-series, their cross-correlations and covariances. As an application, we analyze reproduction of time-series and their cross-correlation coefficients in the well-diversified Russell 3,000 equity index.
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中文摘要：
在高维向量空间中，两个随机选择的向量几乎是高概率正交的。从这一观察出发，我们开发了一个统计因子模型，即