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M. Daniels and R. Kass, “Shrinkage Estimators for Covariance Matrices,” Biometrics, Vol. 57, No. 4, 2001, pp. 1173-1182. doi:10.1111/j.0006-341X.2001.01173.x

has been cited by the following article:

• TITLE: Exploiting Market Integration for Pure Alpha Investments via Probabilistic Principal Factors Analysis

  AUTHORS: George Tzagkarakis, Juliana Caicedo-Llano, Thomas Dionysopoulos

  KEYWORDS: Market Integration; Risk budgeting; Probabilistic Principal Component Analysis

  JOURNAL NAME: Journal of Mathematical Finance, Vol.3 No.1A, March 29, 2013

  ABSTRACT: In this paper, a long-short beta neutral portfolio strategy is proposed based on earnings yields forecasts, where positions are modified by accounting for time-varying risk budgeting by employing an appropriate integration measure. In contrast to previous works, which primarily rely on a standard principal component analysis (PCA), here we exploit the advantages of a probabilistic PCA (PPCA) framework to extract the factors to be used for designing an efficient integration measure, as well as relating these factors to an asset-pricing model. Our experimental evaluation with a dataset of 12 developed equity market indexes reveals certain improvements of our proposed approach, in terms of an increased representation capability of the underlying principal factors, along with an increased robustness to noisy and/or missing data in the original dataset.