Liquidity-driven approach to dynamic asset allocation: evidence from the German stock market

Abstract

Fluctuations in market-wide liquidity may offer opportunities of earning illiquidity premiums. For the US stock market, an investment strategy that profitably exploits these market-wide liquidity fluctuations is proposed by Xiong (J Portf Manag 39(3):102–111, 2013), who focus on an in-sample analysis. In this article, we firstly replicate the liquidity-driven investment strategy of Xiong (J Portf Manag 39(3):102–111, 2013) for the German stock market showing that a successful harvesting of illiquidity premiums is possible as well. Secondly, we extend the study design of Xiong (J Portf Manag 39(3):102–111, 2013) in that we conduct a strict out-of-sample analysis. Our results show that the initial superior in-sample results drastically deteriorate in an out-of-sample framework rendering the practical application of the liquidity-driven investment strategy for the German stock market impossible. Lastly, we modify the rather static investment methodology by a novel approach in which the asset allocation responds flexibly to market-wide liquidity fluctuations. This modification leads to significant performance improvements.

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Notes

  1. 1.

    This relation is confirmed for international markets by Cavenaile et al. (2014).

  2. 2.

    See Amihud (2002), Næs et al. (2011) and Xiong et al. (2013).

  3. 3.

    We follow Næs et al. (2011) and Xiong et al. (2013), who also implement the Hodrick and Prescott (1997) filter with a multiplier \(\alpha \) of 12,800.

  4. 4.

    We tried an alternative cutoff point of at least 50 % trading days which in fact does not change our findings.

  5. 5.

    Pesaran and Timmermann (1995) and Welch and Goyal (2008) highlight the importance of out-of-sample studies.

  6. 6.

    Similar observations were made by Guo (2009) and Croushore (2011) in the context of financial forecasting using revised predictor data that by construction include future information, which were not available to the forecaster in real-time.

  7. 7.

    Additionally, in analogy to Table 5, the Amihud threshold in Table 7 is determined in such a way as to equate the average holding proportions of the DAA with the SAA for the considered estimation period.

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Acknowledgments

We have benefited from many helpful comments by an anonymous referee and the editorial team of FMPM. Armin Varmaz acknowledges financial support from the HSB research funds (Fund Number 81811308).

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Correspondence to Eduard Baitinger.

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Baitinger, E., Fieberg, C., Poddig, T. et al. Liquidity-driven approach to dynamic asset allocation: evidence from the German stock market. Financ Mark Portf Manag 29, 365–379 (2015). https://doi.org/10.1007/s11408-015-0257-1

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Keywords

  • Dynamic asset allocation
  • Liquidity
  • Amihud illiquidity measure
  • Liquidity risk
  • Investment strategy
  • Out-of-sample study

JEL Classification

  • G11
  • G12
  • C32
  • C53