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Stock Price Prediction using Principle Components

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  • Mahsa Ghorbani
  • Edwin K. P. Chong

Abstract

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.

Suggested Citation

  • Mahsa Ghorbani & Edwin K. P. Chong, 2018. "Stock Price Prediction using Principle Components," Papers 1803.05075, arXiv.org.
    • Handle: RePEc:arx:papers:1803.05075
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    References listed on IDEAS

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    1. Shukla, Ravi & Trzcinka, Charles, 1990. "Sequential Tests of the Arbitrage Pricing Theory: A Comparison of Principal Components and Maximum Likelihood Factors," Journal of Finance, American Finance Association, vol. 45(5), pages 1541-1564, December.
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