We examine whether equity liquidity has a distinct impact on leveraged SOA for under- and over-leveraged firms. Therefore, strong institutional settings and equity liquidity can substitute each other in improving the firm's leveraged SOA by reducing the adjustment cost. Second, we find that the positive relationship between equity liquidity and leverage SOA is less pronounced for under-leveraged firms.
Third, we find that strong institutional environments tend to weaken the positive association between equity liquidity and leverage SOA. There is a stream of literature documenting the role of equity liquidity on firms' capital structure decisions. H2: The impact of equity liquidity on the SOA is less (more) pronounced for under-leveraged (over-leveraged) firms.
In addition, the impact of stock liquidity on leverage of SOA may be influenced by the country's institutional environments. Therefore, strong institutional framework and equity liquidity can complement each other to improve the firm's leverage SOA by reducing adjustment costs. Our hypothesis supports their arguments in the same vein that strong institutional framework moderates the effect of stock liquidity on leverage SOA.
H3: The impact of equity liquidity on the SOA is less (more) pronounced in countries with strong (weak) institutional environments.
Current literature models each firm's target leverage in a specific country or institutional framework as a function of time-varying firm characteristics, industry elements, and macroeconomic factors (Frank & Goyal we regress the observed leverage ratio (LEV) on a set of determinants of leverage, estimating the optimum for both the book leverage ratio (BLEV) and the market leverage ratio (MLEV). We follow the existing literature by using Fama and MacBeth's (1973) cross-sectional regressions to estimate target leverage ( Byoun, 2008; Devos et al.Depending on the costs and benefits of rebalancing their leverage ratio, firms assess how quickly they converge to their target leverage from their current positions.
If firm's managers have target leverage ratios and make proactive efforts to achieve them, the gap between target and actual leverage ratios should decrease over time, provided 𝜕𝑗 is greater than zero. While the lever adjustment speed 𝜕𝑗 in Eq. 7) is constant for all firms in a specific country, to test our hypotheses, we allow that equity liquidity increases the firm's speed of adjustment to its target ratio. Using the estimate of target leverage from Eq. 6), we calculate each firm's distance from target (𝐷𝑖𝑠𝑡𝑖,𝑡) and substitute this estimated distance into Eq. 10) includes a pooled OLS regression of leverage changes on the product of 𝐷𝑖𝑠𝑡𝑖,𝑡,𝑗 and the main variable effecting leverage SOA (i.e. equity liquidity) and control variables with standard errors to account for the generated regressors,19 ( 8) (Çolak et al., 2018; Faulkender et al., 2012).
Where 𝑈𝑛𝑑𝑒𝑟𝐿𝑒𝑣𝑖,𝑡,𝑗 is the dummy variable equal to one if the actual leverage is lower than the target leverage (i.e. negative 𝐷𝑖𝑠𝑡𝑖,𝑡,𝑗) and zero otherwise. We then examine the impact of institutional environments on the relationship between stock liquidity and SOA.
Empirical results
For a robustness check, columns 3-4 and 5-6 of Table 3 show the results of Eq. 10) for proprietary liquidity – SOA ratio of financial leverage for book leverage (BLEV) and market leverage (MLEV) using different approximations of liquidity: turnover or share of zero return. The results confirm that equity liquidity has a positive impact on SOA leverage, which is consistent with our prior findings and the findings of prior US empirical studies supporting Drobetz and Wanzenried (2006), we also confirm positive relationships for firm profitability and the relationship between market and book value with SOA leverage.
The positive relationship between macroeconomic conditions and leverage SOA is again confirmed in our results. To test H2, we estimate Eq. 12) and examine whether the SOA relationship between equity liquidity and leverage is different for underleveraged and overleveraged firms. Interestingly, our results show that the coefficients for the triple interaction terms between equity liquidity (Amihud measure), target distance and underleveraged firms dummy are negative and highly significant (at 5% and 1% respectively) for both book and market leverage models (columns 1 and 2).
These results indicate that for under-leveraged firms, the impact of stock liquidity on leverage SOA is less pronounced than for over-leveraged firms. In particular, highly liquid, over-leveraged companies can easily increase their equity, then lower the leverage ratio and adjust more quickly to their target leverage. The coefficients on the interaction terms between equity liquidity and the underleveraged dummy (𝑈𝑛𝑑𝑒𝑟𝐿𝑒𝑣𝑖,𝑡,𝑗∗ 𝐿𝐼𝑄𝑖,𝑡,𝑗) are negative and statistically significant at the 1% level.
On the face of it, it shows that overvalued firms have either greater benefits or lower costs to return to their targets. This result is consistent with previous studies that suggest that adjustments to targets are more prominent for overvalued firms (e.g., Byoun (2008), Faulkender et al. To examine whether the impact that capital liquidity has on The SOA of leverage differs between firms with strong and weak country-level institutional environments (H3), we use interaction terms between equity liquidity and country-level institutional indicators.
We expect the positive relationship between equity liquidity and SOA leverage to be influenced by the strength of institutional environments (𝐼𝐸𝑡,𝑗). More importantly, the coefficients in the triple interaction terms 𝐷𝑖𝑠𝑡𝑖,𝑡,𝑗∗ 𝐿𝐼𝑄𝑖,𝑡,𝑗∗ 𝐼𝐸𝑡,𝑗 capture the role of voice and responsibility (𝑉𝑜𝑖𝐴 𝑐𝑐𝑡,𝑗) in transforming the equity liquidity–SOA leverage relationship. Specifically, we find that equity liquidity has a positive effect on SOA leverage, and this relationship is weakened in countries with strong institutional environments.
Robustness checks
In addition, we examine two other proxies of institutional environments, namely the absence of violence (𝑃𝑜𝑙𝑉𝑖𝑜𝑡,𝑗) and the rule of law (𝑅𝑢𝑙𝐿𝑎𝑤𝑡,𝑗). In general, using two alternative proxies for information settings produces consistent results associated with 𝑉𝑜𝑖𝐴𝑐𝑐𝑡,𝑗.
Conclusion
The role of credit ratings on capital structure and the speed of adjustment: An international study. Book Leverage BLEV Book value of total debt divided by book value of total assets Data Stream Market Leverage MLEV Book value of total debt divided by the sum of market value of. Active leverage ALEV Book value of total debt divided by the sum of book value of total assets and total net income.
Amihud illiquidity LIQ The ratio of a stock's daily absolute return to its dollar volume averaged over the number of days of positive volume. Turnover Turn The number of shares traded in a day, divided by the total number of shares outstanding. Growth opportunity MTB The ratio of book value of assets minus book value of equity plus market value of equity to book value of assets.
Profitability PROF Earnings before interest, taxes, depreciation and amortization divided by the book value of assets. It reflects perceptions of the extent to which agents trust and respect the rules of society, and in particular the quality of contract enforcement, property rights, the police and courts, and the likelihood of crime and violence. This table reports the means of firm-level variables by country and for the entire sample.
This table reports the data structure and average leverage SOA for each country. Amihud is defined as the average ratio of the daily absolute return of the stock to its volume in dollars. Turn is the number of shares traded in a day, divided by the total number of shares outstanding.
Propzero is the proportion of trading days in the year that had zero price change (zero return) from the previous day, for firm i in year t. 𝑈𝑛𝑑𝑒𝑟𝐿𝑒𝑣𝑖,𝑡,𝑗 is the dummy variable that has the value of one if the firm's actual leverage ratio is below the target leverage and zero otherwise. This table reports the robustness tests using partial active leverage adjustment for all three hypotheses (H1-3) for following regressions.
This table reports the robustness checks using fixed effect regression robust to the heteroskedasticity for all three hypotheses (H1-3) for subsequent regressions.
