Another method for examining the relation between illiquidity and expected stock returns is to use portfolio sorting and investigate the performance of zero-investment portfolios. In this section, I use portfolio analysis in which quintiles are formed by sorting stocks based on their illiquidity metrics and one-month ahead returns are calculated for each quintile to find out whether there exists a significant difference in future returns between stocks in the highest and lowest illiquidity quintiles.
Table 2.5 presents the time-series averages of illiquidity and value-weighted returns for each of these illiquidity-sorted portfolios.12 For all illiquidity variables except Gamma, I see that the average return of the illiquidity portfolios increases from the lowest to the highest illiquidity quintile. The average monthly return difference between the extreme return quintiles is 1.6% which is significant at the 1% level. For Gamma, the average return
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difference between the extreme portfolios is 0.6%. The average raw return differences between all of these portfolios are statistically significant. The findings are also economically significant. The results indicate that stocks in the highest illiquidity quintile generate about 19.2% (7.2% in the case of Gamma) higher annual returns in comparison with stocks in the lowest illiquidity quintile.
Moreover, I investigate whether the significant return difference between extreme illiquidity portfolios can be rationalized by Carhart's (1997) market, value, size and momentum factors. I should emphasize that these factors are not borrowed from any U.S. databases and I generate them myself by sorting all stocks to portfolios as explained below. To achieve my goal, monthly return differences between high and low illiquidity quintiles are regressed on the four factors and checked whether the intercepts in result of these regressions are statistically significant using the following model:
Rtn MKTMKTt HMLHMLt SMBSMBt UMDUMDt tn (2.10)
where Rt+n is the one-, three- and six-month ahead return of the zero-investment portfolios and MKTt, HMLt, SMBt and UMDt are the market, value, size and momentum factors in month t, respectively. α is the return alpha and βMKT, βHML, βSMB and βUMD are the market, value, size and momentum betas, respectively. The market factor (MKT) is measured by the excess return on the BIST-100 index. I estimate the value (HML) and size (SMB) factors by forming quintile portfolios every month using sorts of stocks on their book-to-market ratios and market values of equity, respectively. Then, the average monthly return differences between the highest and lowest quintile portfolios are calculated. The momentum factor (UMD) is constructed as the return difference between the 30 percent of firms with the highest lagged six-month returns and the 30 percent of firms with the lowest lagged six- month returns.
Table 2.6 presents the intercepts from these regressions. In Panel A of Table 2.6, for one-month ahead returns, the 4-factor alpha for the return difference between quintile 5 and quintile 1 is 1.58% with a t-statistic of 3.46 when Illiq is used as the illiquidity variable. I also obtain statistically significant 4-factor alphas when I utilize IlliqRKW, IlliqMA, KLV and Illiqzero. For Gamma, the 4-factor alpha is 0.61% with a t-statistic of 2.09. Panels B and C
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which focus on three- and six-month ahead returns, show that the 4-factor alphas for the return differences between quintile 5 and quintile 1 are 4.24% and 7.47% with t-statistics of 3.61 and 3.67, respectively when Illiq is used as the illiquidity variable. The 4-factor alphas are also statistically significant when I utilize other illiquidity measures in both panels. These results suggest that after controlling for the market, value, size, and momentum factors, the return difference between the high and low illiquidity quintiles is still positive and significant. In other words, these four popular risk factors cannot fully account for the positive relationship between illiquidity and expected stock returns. Collectively, I conclude that there is a significantly positive relation between illiquidity and future equity returns.
I also investigate the relation between the illiquidity premium and some market-wide factors. The capital markets in Turkey have undergone major structural reforms in the past decade and these reforms may have had an impact on the relation between illiquidity and expected equity returns. Since these reforms were gradually implemented and their effects were only reflected in the markets over time, it is empirically difficult to identify specific dates for market reforms and carry out event studies around those dates. Instead, I proxy for the process of market reforms using the aggregate market capitalization (Agg Mkt Cap) in Borsa Istanbul and examine the link between this variable and the illiquidity premium. Additionally, I assess the magnitude of the illiquidity premium during periods of extreme market upswings and downswings by defining dummies for the 10% of months with the largest price drops in BIST-100 index (Low Mkt Dum) and 10% of months with the largest price increases in BIST-100 index (High Mkt Dum). In my empirical treatment, I regress the monthly return differences between high and low illiquidity quintiles based on IlliqMA on the four factors defined in equation (2.10) and various combinations of the three market variables defined above. The results are presented in Table 2.7. I find that the aggregate market capitalization has a negative albeit insignificant relation with the illiquidity premium in all specifications. Additionally, the illiquidity premium is higher (lower) during periods of extreme market downswings (upswings) as evidenced by the significantly positive (negative) coefficient of Low Mkt Dum (High Mkt Dum). In other words, when the market is doing well, the investors expect lower return premiums from illiquid stocks and vice versa. Only the market factor has a significantly positive coefficient among Carhart’s (1997) four factors and
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the intercept terms retain its positive significance in all specifications extending my results from Table 2.6.