英文标题：
《Determining the number of factors in a forecast model by a random matrix
  test: cryptocurrencies》
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作者：
Andr\\\'es Garc\\\'ia Medina and Graciela Gonz\\\'alez-Far\\\'ias
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最新提交年份：
2019
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英文摘要：
  We determine the number of statistically significant factors in a forecast model using a random matrices test. The applied forecast model is of the type of Reduced Rank Regression (RRR), in particular, we chose a flavor which can be seen as the Canonical Correlation Analysis (CCA). As empirical data, we use cryptocurrencies at hour frequency, where the variable selection was made by a criterion from information theory. The results are consistent with the usual visual inspection, with the advantage that the subjective element is avoided. Furthermore, the computational cost is minimal compared to the cross-validation approach.
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中文摘要：
我们使用随机矩阵检验确定预测模型中统计显著因素的数量。应用的预测模型是降秩回归（RRR）类型，特别是，我们选择了一种可以被视为典型相关分析（CCA）的模式。作为经验数据，我们使用小时频率的加密货币，其中变量选择是根据信息论的标准进行的。结果与通常的目视检查一致，优点是避免了主观因素。此外，与交叉验证方法相比，计算成本最小。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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