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Portfolio selection in a data-rich environment

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  • Bouaddi, Mohammed
  • Taamouti, Abderrahim

Abstract

We model portfolio weights as a function of latent factors that summarize the information in a large number of economic variables. This approach (hereafter diffusion index approach) offers the opportunity to exploit a much richer information base to improve portfolio selection. We use factor analysis to estimate the space spanned by the factors. This provides consistent estimates for the optimal weights as the number of economic variables and sample size go to infinity. We consider an empirical application to illustrate the practical usefulness of our approach. The results indicate that the diffusion index approach helps to improve the portfolio performance.

Suggested Citation

  • Bouaddi, Mohammed & Taamouti, Abderrahim, 2013. "Portfolio selection in a data-rich environment," Journal of Economic Dynamics and Control, Elsevier, vol. 37(12), pages 2943-2962.
    • Handle: RePEc:eee:dyncon:v:37:y:2013:i:12:p:2943-2962
    DOI: 10.1016/j.jedc.2013.08.010
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    Cited by:

    1. Fabrizio Cipollini & Giampiero Gallo & Alessandro Palandri, 2020. "A Dynamic Conditional Approach to Portfolio Weights Forecasting," Econometrics Working Papers Archive 2020_06, Universita' degli Studi di Firenze, Dipartimento di Statistica, Informatica, Applicazioni "G. Parenti".
    2. Lin, Weidong & Taamouti, Abderrahim, 2024. "Portfolio selection under non-gaussianity and systemic risk: A machine learning based forecasting approach," International Journal of Forecasting, Elsevier, vol. 40(3), pages 1179-1188.
    3. Kakeu, Johnson & Bouaddi, Mohammed, 2017. "Empirical evidence of news about future prospects in the risk-pricing of oil assets," Energy Economics, Elsevier, vol. 64(C), pages 458-468.
    4. Gomes, Pedro & Taamouti, Abderrahim, 2016. "In search of the determinants of European asset market comovements," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 103-117.

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    More about this item

    Keywords

    Portfolio's weights modeling; Factor analysis; Principal components; Portfolio performance; Stock returns; Fama–French factors; Economic factors; VIX;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G19 - Financial Economics - - General Financial Markets - - - Other

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