mpf6_Time Series Data_quandl_更正kernel PCA_AIC_BIC_trend_log_return_seasonal_decompose_sARIMAx_ADFull

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已于 2024-06-12 07:03:49 修改

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分类专栏： pythonForFinance2 文章标签： big data

于 2022-08-10 08:54:01 首次发布

本文链接：https://blog.youkuaiyun.com/Linli522362242/article/details/126113926

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本文探讨了金融投资组合中，主成分分析（PCA）如何用于降低资产价格复杂性的维度。PCA有助于简化对道琼斯工业平均指数及其30个成分股的分析。文章还强调了时间序列数据的平稳性对于避免虚假回归的重要性，并介绍了使用Augmented Dickey-Fuller测试检查单位根和通过差分、季节性分解等方法使非平稳数据变得平稳。最后，利用SARIMAX模型进行时间序列预测，通过AIC和BIC选择最佳模型参数，并进行预测和预报。

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In financial portfolios, the returns on their constituent(/kənˈstɪtʃuənt/组成的,构成的) assets depend on a number of factors, such as macroeconomic and microeconomical conditions, and various financial variables. As the number of factors increases, so does the complexity involved in modeling portfolio behavior. Given that computing resources are finite, coupled with time constraints, performing an extra computation for a new factor only increases the bottleneck on portfolio modeling calculations. A linear technique for dimensionality reduction is Principal Component Analysis (PCA). As its name suggests, PCA breaks