英文标题：
《Stock Price Prediction using Principle Components》
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作者：
Mahsa Ghorbani, Edwin K. P. Chong
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最新提交年份：
2018
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英文摘要：
  The literature provides strong evidence that stock prices can be predicted from past price data. Principal component analysis (PCA) is a widely used mathematical technique for dimensionality reduction and analysis of data by identifying a small number of principal components to explain the variation found in a data set. In this paper, we describe a general method for stock price prediction using covariance information, in terms of a dimension reduction operation based on principle component analysis. Projecting the noisy observation onto a principle subspace leads to a well-conditioned problem. We illustrate our method on daily stock price values for five companies in different industries. We investigate the results based on mean squared error and directional change statistic of prediction, as measures of performance, and volatility of prediction as a measure of risk.
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中文摘要：
文献提供了强有力的证据，证明股票价格可以从过去的价格数据中预测。主成分分析（PCA）是一种广泛使用的数学技术，通过识别少量主成分来解释数据集中发现的变化，对数据进行降维和分析。在本文中，我们描述了一种基于主成分分析的降维操作，利用协方差信息进行股价预测的通用方法。将噪声观测投影到主子空间会导致一个条件良好的问题。我们举例说明了我们对不同行业的五家公司的每日股价值的方法。我们研究了基于预测均方误差和方向变化统计的结果，作为绩效的衡量标准，以及基于预测波动性的结果，作为风险的衡量标准。
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Mathematical Finance        数学金融学
分类描述：Mathematical and analytical methods of finance, including stochastic, probabilistic and functional analysis, algebraic, geometric and other methods
金融的数学和分析方法，包括随机、概率和泛函分析、代数、几何和其他方法
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