Robustness Tests

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0.7 – 0.9 percentage points. The results are similar when employed industry fixed effects.

Most results are statistically significant for at least a 5% significance level. The results for upgrades are mostly not statistically significant, which is consistent with CR-CS assumptions. These results suggest that CRAs can identify a firm has having a higher default risk, than what the firm believes it has based on its past fundamentals. This point of view is consistent with Acharya, Davydenko, and Strebulaev (2012) argument that, in the long run, firms that present higher degrees of default risk hold in balance more cash.

Panel C from Table 10 presents the results on cash holdings from firms that predicted a change in their credit rating but the credit rating was maintained. The results are considerable weaker and not always statistically significant. Predicated but unrealized downgrades lead to a decrease in cash holdings, suggesting that firms interpret the decision from the CRA as signal that they don’t need to hold as much cash. Predicted but unrealized upgrades lead firms to increase cash holdings, which suggests that firms increase cash holdings on an attempt to increase the likelihood of a future upgrade.

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the impact in market leverage from unexpected changes in credit rating derives from speculative grade firms. Unpredicted downgrades reduce the firm’s market leverage between 2 – 6.3 percentage points. These results are significant at 1% significance level.

The results for unexpected downgrades within investment grade firms are significantly weaker and mostly not statistically significant. The results for unexpected upgrades are inconclusive and weaker than downgrades. Investment grade firms that predict an upgrade but maintained the credit rating, increase its market leverage between 0.9 – 2.1 percentage points. Most results are significant at 1% significance level. For speculative grade firms most results are economically and statistically insignificant.

Table 12 replicates the analysis from Table 9 while conditioning the sample by investment grade and speculative grade firms. Investment grade firms react similarly to predicted and unpredicted downgrades, with reductions on net debt issuances between 2.1 – 3.3 percentage points. Speculative grade firms react more to unpredicted downgrades, than predicted downgrades, by reducing net debt issuances between 2.2 – 2.8 percentage points. These results are economically and statistically significant.

For upgrades, only predicted upgrades produce meaningful impact on net debt issuances:

an increase between 3.7 – 4.3 percentage points for investment grade firms and an increase between 2.8 – 4.1 percentage points for speculative grade firms. Contrary to market leverage, net debt issuances actually produce stronger results within investment grade rather than speculative grade firms. These results are consistent with Alp (2013), which shows that, over time, good firms are more affected by stringent credit ratings criteria.

Table 13 presents the impact of predicted and unpredicted changes in credit ratings in cash holdings, by investment grade or speculative grade firms. The results show that only speculative grade firms react to downgrades by increasing its cash holdings between

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0.9 – 1.6 percentage points, regardless of the downgraded being or not predicted. The results are consistent with Bates, Kahle, and Stulz (2009) who show that overtime the firm’s liquidity in the U.S. has been increasing. Overtime the number of speculative grade firms has been increasing and I show that these firms tend to hold in balance more cash, following an unpredicted downgrade.

Overall, the results suggest that speculative grade firms are more affected regarding market leverage, while investment grade firms are more affected on net debt issuances, by the unpredictability of future changes in credit ratings. Alp (2013) shows that investment grade firms are more affected by the tightening in credit rating criteria. I argue that a higher degree of transparency regarding the credit rating methodology from the credit rating agencies, regardless of the firm being investment grade or speculative grade firm, would increase the firm’s stability regarding leverage decisions.

3.7.2. Tests for Different Years

In this section I access the impact of year specific events, such as different stages in business cycles, might have on the previous results. For example, while experiencing an economic recession firms are more likely to be downgraded and, as a consequence, reduce its market leverage. Also, Baghai, Servaes and Tamayo (2014) and Alp (2013) show that credit rating agencies have been tightening credit rating criteria over time. In this section I replicate the analysis from Table 8 while conditioning the sample in interval of 5 years each, from 1986 until 2015.

Panel A from Table 14 shows that, for most intervals, predicted changes in credit ratings do not lead to statistically significant adjustments in market leverage. Panel B shows that following an unpredicted downgrades firms decrease its market leverage significantly. Most results are statistically and economically significant, with all

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coefficients for unpredicted downgrades being negative. Panel C shows that firms that predicted a change in credit ratings but such did not occurred, do not significantly adjust its market leverage.

The evidence presented suggests that previous results are not driven by business cycles, specific year events or changes in methodologies from the CRAs. The increase in conservatism from the CRAs methodology can lead firms to reduce its market leverage.

I argue that the tightening in rating criteria makes predicting changes in credit ratings more difficult for financial managers. The harder it is for a firm to predict a change in its credit rating the more the firm reacts to it.

3.7.3. Tests for Different Predicted Credit Ratings

It is unclear the structure used and importance that each financial manager attributes to the firm’s past fundamentals. A change in this structure used can lead to different predicted credit ratings. To alleviate this concern I test different structures for the predicted credit rating by using equations (2), (3) and (4) from Table 7. Equation (2) drops from equation (1) the firm’s Dimson beta and idiosyncratic risk. Equations (3) and (4) have the same structure as equations (1) and (2), respectively, but use industry fixed effects instead of firm fixed effects. I then replicate the previous analysis on market leverage, net debt issuance and cash holdings for each specification for the predicted credit rating. Tables A.3 to A.11 in the Internet Appendix show that the results are consistent for all specifications. Predicted changes in credit ratings do not produce significantly changes in market leverage and net debt issuances. Unpredicted downgrades generate significant results, with firms reducing its market leverage and net debt issuance;

while predicted upgrades produce weak results. The results for unpredicted downgrades are statistically and economically significant for all specifications. Cash holdings increase

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following a downgrade, with the impact stronger for unpredicted rather than predicted downgrades.