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

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  • Eduard Baitinger
  • Christian Fieberg
  • Thorsten Poddig
  • Armin Varmaz

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. Copyright Swiss Society for Financial Market Research 2015

Suggested Citation

  • Eduard Baitinger & Christian Fieberg & Thorsten Poddig & Armin Varmaz, 2015. "Liquidity-driven approach to dynamic asset allocation: evidence from the German stock market," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 29(4), pages 365-379, November.
    • Handle: RePEc:kap:fmktpm:v:29:y:2015:i:4:p:365-379
    DOI: 10.1007/s11408-015-0257-1
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    References listed on IDEAS

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    More about this item

    Keywords

    Dynamic asset allocation; Liquidity; Amihud illiquidity measure; Liquidity risk; Investment strategy ; Out-of-sample study; G11; G12; C32; C53;
    All these keywords.

    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods

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