What Are the Best Liquidity Proxies for Global Research?

Corporate Shareholdings and the Liquidity of Malaysian Stocks: Investor Heterogeneity, Trading Account Types and the Underlying Channels

“…Empirically, the reliability of the Amihud (2002) illiquidity ratio has been verified in a series of horseraces using data from the U.S. (Goyenko et al, 2009), emerging markets (Lesmond, 2005), frontier markets (Marshall et al, 2013) and global stock exchanges (Fong et al, 2014b). In these four studies, the Amihud ratio is found to exhibit one of the highest correlations among cost-pervolume proxies with intraday benchmarks.…”

“…Liquidity also influences the decision of capital structure (Lipson and Mortal, 2009), enhances market efficiency (Chordia et al, 2008), increases firm performance (Fang et al, 2009) and improves corporate governance (Edmans et al, 2013). 2 As a dependent variable, the literature explores the contributing factors of stock liquidity, which include corporate governance (Chung et al, 2010), financial transparency (Heflin et al, 2005), financial liberalization (Vagias and van Dijk, 2012), security analysts (Roulstone, 2003), local institutions (Agarwal, 2007), local blockholders (Brockman et al, 2009), local individual investors (Amihud et al, 1999) and foreign institutions (Rhee and Wang, 2009). prices (for the liquidity menu, see Goyenko et al, 2009;Fong et al, 2014b). This positive development has contributed to a gradual increase in the understudied emerging markets in recent decade.…”

Liquidity measurement in frontier markets

“…Given this, and the fact that "Zeros" is easier to implement, studies such as Bekaert et al (2007) just use Zeros rather than Zeros and LOT. Moreover, the FHT proxy we include is another simplification of the LOT model and Fong et al (2011) show that this is more accurate than LOT.…”

“…The monthly average quoted spread is, following Fong et al (2011), calculated by time weighting the intraday spreads. The five-minute price impact measure is calculated as per Eq.…”

“…We adopt a similar approach to Goyenko et al (2009) and Fong et al (2011) and run "horse races" between popular liquidity proxies in frontier markets to determine which measures have the largest correlations and lowest root mean squared errors with high frequency liquidity benchmarks.…”