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Liquidity in the German Stock Market

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Schmalenbach Business Review Aims and scope

Abstract

This paper presents the most extensive analysis of liquidity in the German equity market so far. We examine the evolution of liquidity over time, the determinants of liquidity, and commonality across liquidity measures and countries. We make use of a new publicly available dataset, the Market Microstructure Database Xetra (MMDB-Xetra). We find that liquidity has generally increased over time, and that in times of crisis liquidity is lower and the volatility of liquidity is significantly higher. Commonality in liquidity is highest during the financial crisis. We also find significant commonality between liquidity in the US and the German equity markets.

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Notes

  1. “Xetra” and “CDAX” are registered trademarks of Deutsche Börse AG.

  2. The CDAX contains all German stocks listed on the Frankfurt Stock Exchange that are admitted to trading on a regulated market.

  3. For more details see Table B1 in Johann et al. (2018).

  4. We recommend any user of the MMDB-Xetra database to apply similar filtering procedures to the raw data.

  5. Trading in Xetra is halted when the price change exceeds a pre-specified threshold. The exact threshold value at which a halt is triggered is not disclosed. After a volatility interruption trading is restarted with a call auction.

  6. We cannot verify when and how the rules have been changed because, as noted above, the exchange does not disclose these rules.

  7. Until September 2009 transactions in Xetra occurred either at the quoted ask or at the quoted bid price. Consequently, the effective spread was equal to the quoted spread in effect immediately before the transaction occurred. However, the effective spread is a contingent measure because an observation is only recorded when a trade occurs. If traders time their trades, i.e. if they tend to trade when the quoted spread is low, the average effective spread will be lower than the average quoted spread. In September 2009 hidden orders were introduced. Hidden orders are large, invisible limit orders. If such an order is placed between the visible best bid and ask prices and is executed, a transaction at a price within the visible quoted spread results. The MiFID pre-trade transparency requirement does not apply to hidden orders because the volume of these orders must be large enough for the large-in-scale waiver to apply.

  8. Results for correlations based on equally-weighted rather than value-weighted averages are shown in the Appendix.

  9. We repeat the analysis using equally weighted averages rather than value-weighted averages. The results can be found in the Appendix.

  10. Our definition of the crisis period follows Bao et al. (2018) and Bessembinder et al. (2017). We tested whether the inclusion of an additional dummy covering the period of the Euro crisis is warranted. To this end we implement the following procedure. We repeated the regression including month-fixed effects instead of the binary variables for the financial crisis and the post-MiFID period. We then plotted the month fixed effects. While there were spikes and elevated variability of the month fixed effects during the 2008–2009 financial crisis for some of the variables of interest (most notably volatility, quoted and effective spreads and price impact) there was no clear pattern during the post-crisis period. We therefore concluded that the independent variables included in the model do a good job at capturing the effect on equity market quality of the Euro crisis.

  11. We have re-estimated all regressions using the US Ted Spread instead of the IR Spread. We still find that lower funding liquidity is associated with higher market liquidity. The coefficients on the Ted Spread in the quoted and effective spread and price impact regressions are still significantly negative while the coefficient in the depth regression is significantly positive.

  12. To further elaborate on this point we re-estimated the model but excluded the VDAX (results not shown). In this alternative specification the coefficients on the financial crisis dummy in the quoted spread and price impact regressions become positive and significant. In the effective spread regression the coefficient becomes positive but insignificant. The coefficient in the depth regression is numerically smaller but remains positive and significant.

  13. See e.g. Copeland and Copeland 1998, Bonfiglioli and Favero (2005), Rua and Nunes (2009), Syllignakis and Kouretas (2010), Caporale et al. (2016).

  14. Admittedly, we only consider two markets.

  15. Trading hours in Germany changed several times during our sample period.

  16. This might be due to the lower importance of the time difference at the weekly level, and to idiosyncratic variation in liquidity at the daily level which partly cancels out at the weekly level.

  17. The dispersion in trading volume is much larger in the US.

  18. Trading in the US equity market is also fragmented. However, as documented by Angel et al. (2011), fragmentation started earlier in the US.

  19. This regression approach is identical to the one presented by Brockman et al. (2009) in Eq. (3).

  20. In line with Brockman et al. (2009) we use equally-weighted averages. We obtain qualitatively similar results when using value-weighted averages or medians.

  21. An F‑test further confirms that the sum of the three beta coefficients is significantly different from zero.

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Authors and Affiliations

  1. Finance Area, University of Mannheim, L9,1‑2, 68131, Mannheim, Germany

    Thomas Johann, Stefan Scharnowski, Erik Theissen, Erik Theissen & Lukas Zimmermann

  2. Centre for Financial Research, Cologne, Germany

    Erik Theissen

  3. University of Vienna, Vienna, Austria

    Christian Westheide

  4. Research Center SAFE, Frankfurt, Germany

    Christian Westheide

Authors
  1. Thomas Johann
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  2. Stefan Scharnowski
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  3. Erik Theissen
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  4. Christian Westheide
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Correspondence to Erik Theissen.

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Acknowledgements

We gratefully acknowledge financial support from the German Science Foundation (DGF) under grant TH 724/6‑1. We thank two anonymous referees for valuable comments.

Appendix

Appendix

Table 6 Correlations of liquidity and trading activity measures using equal weighting. This table shows time-series correlations between contemporaneous daily changes of various measures of volatility, liquidity, and trading activity. The daily market-wide measures are constructed using equal-weighting of the individual stock measures. The measures are defined as in Table 1. \(\Delta\) denotes daily percentage changes. Bold numbers indicate significance at the 5% level
Full size table
Table 7 Time-series regressions of liquidity measures using equal weighting. This table shows time-series regression results, where the dependent variables are daily market-wide measures of liquidity and trading activity. The market-wide measures are constructed using equal-weighting of the individual stock measures. The measures are defined as in Table 1. \(\Delta\)DAX+ (\(\Delta\)DAX–) is the contemporaneous return of the DAX if the return is positive (negative) and zero otherwise. \(\Delta\text{DAX}_{5}^{\text{+}}\) (\(\Delta\text{DAX}_{5}^{\text{--}}\)) is the return of the DAX of the five previous days if the return is positive (negative) and zero otherwise. \(\text{VDAX}_{t-1}\) is the closing level of the VDAX of the previous day. IR Spread is the spread between the 12month Euribor and the return of a 12month German government zero-coupon bond, similar to the Ted spread. Post MiFID is an indicator variable for the period after the introduction of MiFID. Financial Crisis is an indicator variable for the period from July 2007 through April 2009. Witching Day is an indicator variable for days on which derivatives on German stocks and indices expire. \(\text{Holiday}_{\text{Tr.}}\) is an indicator variable for holidays on which Xetra is open for trading. \(\text{Long Weekend}_{\text{Tr.}}\) and \(\text{Long Weekend}_{\text{No\leavevmode\nobreak\ Tr.}}\) are indicator variables for so called Brückentage, where on the holiday Xetra is open or closed for trading, respectively. Monday through Thursday are indicator variables for the respective weekdays. Newey and West (1987) adjusted t‑statistics are given in parentheses
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Johann, T., Scharnowski, S., Theissen, E. et al. Liquidity in the German Stock Market. Schmalenbach Bus Rev 71, 443–473 (2019). https://doi.org/10.1007/s41464-019-00079-6

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