**5. Conclusions**

This paper empirically investigates whether popular low-frequency liquidity proxies capture liquidity effectively in emerging markets, and, if they do, which proxy measures liquidity best. We carry out a comprehensive analysis using tick-by-tick trade and quote data covering 1183 stocks from 21 emerging markets, spanning four continental regions. Our study complements those by Lesmond (2005), and Goyenko et al. (2009) in important ways. While Lesmond (2005) relies on quarterly quoted spreads, we use comprehensive market microstructure data, which allows us to compare various low-frequency liquidity proxies using various measures of transaction costs and price impact, exclusively from market microstructure data. Our study extends the analysis of Goyenko et al. (2009) for the U.S. market to emerging markets.

Our major findings are summarized as follows. We find rich dispersion in transaction costs and price impacts across emerging markets. Furthermore, we find that most of the spread proxies, including the Roll's (1984) spread, Hasbrouck's (2009) estimate, and Lesmond et al.'s (1999) *LOT* measure performs relatively well. The *LOT* measure has an obvious edge over the other two spread proxies in a majority of the markets. With respect to price impact proxies, the Amihud (2002) measure, Cooper et al.'s (1985) Amivest measure and Pástor and Stambaugh's (2003) measure are close substitutes, with the Amihud measure being more effective in some cases. Our regression analysis shows that certain firm and market characteristics significantly influence how accurately a low-frequency spread proxy captures a high-frequency spread benchmark. Turnover, stock volatility, firm size, openness to foreign investors, market volatility, legal origin, and trading mechanism all affect the measurement accuracy of a proxy significantly.

Our coverage of emerging market stocks is quite comprehensive. However, the timeseries is limited to about three to four months in the year 2004. Studies by Abdi and Ranaldo (2017) and Chung and Zhang (2014) show that the cross-sectional pattern of the effectiveness of liquidity proxies, is quite stable over time. Therefore, the findings of our paper should still hold valid and offer valuable information to researchers and practitioners.

Our sample firms represent a greater number of liquid firms than the average firms in emerging markets. One distinct characteristic of emerging markets is that a small number of large corporations often make up the majority of the total market capitalization and trading activity. Therefore, although in some of the markets, our sample includes only the largest firms, they represent their respective markets reasonably well. Further, foreign investors in emerging markets generally deal with large firms because of better liquidity, greater visibility, and easier access to firm-specific information (Kang and Stulz 1997; Chiyachantana et al. 2004). Our sample stocks are likely to become primary targets of global investments. In this regard, our findings offer useful information to global investors.

**Author Contributions:** Formal analysis, C.Y.; Original draft preparation, H.A.; revision and editing, J.C.; funding acquisition, H.A., C.Y. and J.C.

**Funding:** This research has greatly benefitted from the financial support by IREC at the Institute of Finance and Banking at Seoul National University and by City University of Hong Kong.

**Acknowledgments:** We are grateful to Ki Beom Binh, Joon Ho Hwang, Dongcheol Kim, Sheng-Yung Yang, Seung Dong You, and seminar participants at the first IREC symposium, the 2011 Korea Allied Finance Associations Joint Conference, the 2nd KFA-TFA Joint Conference in Finance, and Korea University for their helpful comments and discussions. All errors are our own.

**Conflicts of Interest:** The authors declare no conflict of interest.
