ratings and firm value”

A topic of relevance to financial managers is the relation between a credit rating and firm value ( V L ). The general aim of this paper is to elucidate this relation with a spe- cific objective of helping C corp managers choose an optimal target rating ( OTR ). To achieve these goals, we use the Capital Structure Model (CSM) to compute a series of firm value ( V L ) outcomes matched to credit ratings. The maximum V L ( max V L ), among all V L outcomes, identifies OTR . This identification begins with the matching of credit spreads and ratings by Damodaran (2019) for three firm categories: small , large , and financial service ( FS) . Given these spreads, we can compute costs of borrow- ing with these costs needed to compute V L and other numerical outcomes. Besides costs of borrowing, our numerical outcomes are based on other key inputs including US $1,000,000 in before-tax cash flows, C corp tax rates, and a sustainable growth rate. Major findings that guide managers include the following. First , Moody’s A3 is the most common OTR . Second , growth firms generally require higher ranked OTRs . Third , compared to small and large firms, FS firms attain greater max V L values, higher optimal debt-to-firm value ratios ( ODVs ), and generally lower ranked OTRs . Fourth , relative to small firms, large firms gain less from growth even though they attain greater max V L outcomes. Fifth , only for FS firms can we find outcomes where operational cash flows are better spent on interest payments than retained internally for growth. cash a a that are to credit


INTRODUCTION
A credit rating is a measured assessment of the creditworthiness of a borrower in general terms or a debt obligation in specific terms. A vital task for managers is to discern the relation between firm value (V L ) and credit ratings. To discover this relation, we apply the Capital Structure Model (CSM) using inputs consisting of before-tax cash flow, costs of borrowing based on credit spreads, C corp tax rates, and a sustainable growth rate. These inputs enable us to calculate V L for a sequence of debt-for-unlevered equity choices that are matched to credit ratings.
Using the CSM, we are able to show how V L changes based on a target credit rating. Given the concave downward shape of the relation between V L and debt-to-firm value ratios (DVs) where each DV is associated with a credit rating, we can identify the maximum firm value (max V L ) and thus the optimal target rating (OTR). Because max V L is tied to OTR, it is incumbent on a firm's managers to know and achieve its OTR. Whereas a firm can target a credit rating, only a rating agency can assign a credit rating to a firm and thus insure that the managerial choice of an OTR is realized.
Firm valuation depends on operating cash flows and how these cash flows are discounted as well as how they growth. For our tests, discount rates (or costs of borrowing) are based on fifteen credit spreads

LITERATURE REVIEW
This section reviews and identifies effective (average) federal tax rates in the US and overviews capital structure models including the model used in this paper.

US Effective Federal Tax Rates under Tax Cuts and Job Acts (TCJA)
For this study, we use US data given by Damodaran (2019) who relies on a sample of 7,247 publicly traded US firms with 16.7% being financial service firms. Based on surveying Damodaran's sample, we find that his sample of companies mainly contain the C corps ownership type (as opposed to the pass-through ownership type that does not pay corporate taxes). Thus, our CSM tests use federal tax rates that apply to C corp owners.
In identifying the unlevered and levered tax rates in this section, we follow Hull (2014b) by allowing tax rates to change in their predicted direction for each increasing P choice where a P choice refers to the proportion of unlevered equity (E U ) retired with a new debt issue. Hull argues that corporate and personal equity tax rates decrease with each increasing P choice while personal debt tax rates increase. For our tests, we use a 3% change in a tax rate for each of the fifteen increasing P choices with each tax rate fixed at four decimal points. The use of 3% in conjunction with the setting of unlevered tax rates enable us to achieve the desired effective levered tax rates that should occur near the optimal target rating (OTR).
US tax rates became lower with the enactment of the Tax Cuts and Jobs Act (TCJA) during December 2017. The US corporate tax rate (T C ) was lowered from a maximum statutory rate of 35% to a flat rate of 21%. Considering tax credits and deductions, an effective T C will be below 21%. For our tests, we want an effective T C around 18%, which is consistent with what the Penn Wharton Budget Model (2017) projections for T C under TCJA. Since T C is expected to fall with leverage, we set T C at its flat rate of 21% when the firm is unlevered so that the effective levered T C is near 18% when OTR is reached. For our fifteen tests the mean (median) effective T C is 18.23% (18.26%) with a standard deviation of 62%. An effective tax rate, be it a corporate rate or a personal tax rate (as discussed below), shows little variation among the three firm categories.
C corps equity investors buy and sell shares and receive non-qualified dividend with a US maximum personal tax rate on equity (T E ) of 37% un-der TCJA. However, for qualified dividends as well as capital gains, the typical maximum T E is 20%. Whereas the extremely wealthy pay 23.8% instead of 20%, lower income investors pay at rates of 0% and 15% with investors having the ability to defer capital gains so that, through charitable contributions and inheritance, T E can be zero. Given the above information, we advocate a levered effective T E on equity income of about 14%. To achieve 14%, we set the unlevered T E at 16%. For our fifteen tests the mean (median) effective T E is 13.89% (13.91%) with a standard deviation of 47%.
Interest distributions for C corp debt owners are taxed at the personal debt tax rate (T D ) that has a US maximum of 37% under TCJA. If debt is held longer than three years, any capital gains is taxed at a lower capital gains rate with a typical T D of 20%. Wealthier investors who invest in debt can avoid the maximum of 37% by investing in municipal bonds so that T D is zero. Given the above factoids, we expect most corporate debt to have an effective T D well below the maximum T D of 37%. While a wide range of possible T D outcomes can exist among investors (like all tax rates), we use a levered effective T D near 21%. To achieve 21%, we set the unlevered T D at 18%. For our fifteen tests the mean (median) effective T D is 20.68% (20.63%) with a standard deviation of 68%.

Capital Structure Models
With the fall in T C under TCJA, the interest tax shield (ITS) for a C corp is less of a benefit, albeit this is somewhat offset by lower taxes paid on interest income. Agency models provide a framework where an optimal debt-to-firm value ratio (ODV ) can exist even without the presence of an ITS. Jensen and Meckling (1976) demonstrate how maximum valuation occurs at ODV simply from principal-agent valuation effects. As agents of debt owners, equity owners can undertake risky projects to enhance their residual ownership positions at the expense of debt owners. Besides agency effects involving project selection, there are owner-manager agency effects. For C corps with many shareholders, there can be almost perfect separation between owners and managers leading to owner-manager conflicts. For smaller C corps, these problems can be eliminated since owners and managers are more apt to be the same. In general, agency models point out the need to monitor those in charge. For example, consider a firm with a glut of cash flows that leads to unwarranted compensational schemes by those who are in charge. Jensen (1986) points out that such a firm can add value by issuing additional debt because greater interest payments serve to lessen the squandering of cash flows. In conclusion, even in a world without taxes, agency costs and benefits can dictate that an ODV exists.
In addition to agency models, pecking order models of financing (Donaldson, 1961 While these findings occurred before TCJA, this range is consistent with our small firm mean of 5.18% and our large firm mean of 9.08% given later in Table 2.

METHODOLOGY
In this paper, we use a trade-off model to determine outcomes from potential target credit rating choices. This trade-off model is the Capital Structure Model (CSM). Hull (2018) updates the nongrowth and growth CSM research as applied to C corps. This update includes the modification of the levered equity growth rate (g L ) equation and the introduction of nongrowth and retained earnings (RE) constraints when applying the CSM gain to leverage (G L ) equations. These constraints prevent a firm from issuing more debt than its cash flows can support. For our tests, constraints always occur after OTR is reached.
The CSM is unique as it is captures the interdependent of growth and debt through a variable, the levered equity growth rate (g L ), that directly ties together the plowback-payout and debt-equity choices.
Since retained earnings (RE) equals the plowback ratio (PBR) times the before-tax cash flows (CF BT ) and the g L is defined in terms of RE, the CSM allows us to change PBR until our chosen g L is achieved for the leverage choice that corresponds to a target rating. For our tests, we use the CSM's two component equations that allow for changes in tax rates. This equation does not include a third component that captures wealth transfers (Hull, 2014a).

RESULTS
This section provides results using the CSM equations for C corps. First, we compute costs of debt (r D ) and costs of equity (r L ). We show how each cost is matched to a proportion of unlevered equity retired with debt (P choice) and a debt-to-firm value ratio (DV ). Second, we graphical display the concave downward relation between the gain to leverage (G L ) and the debt-to-firm value ratio (DV ). Third, we present eleven outcomes in table format for our fifteen tests. Fourth, we graphical display the concave downward shape when plotting firm values versus credit ratings. Table 1 reports outcomes for eight variables in eight columns. The first column has Moody's credit ratings. The other seven columns are ordered as follows: P choice; DV; credit spread (that relies on http://www.bondsonline.com); cost of debt (r D ); cost of equity (r L ); levered equity growth rate (g L ); and, growth-adjusted cost of equity (r Lg ). The data for the credit rating and credit spreads in Table 1 are from Damodaran (2019) who supplies data for three firm categories: small, large, and financial service (FS).

Computing Costs of Borrowing
Outcomes in Table 1 are for one of our fifteen tests. This test is for a large growth firm that targets a Moody's rating of A3. As will be seen in Table  2, this A3 target is also the OTR. Results for A3 are in the bold print row of Table 1. According to Morningstar (2019), nearly 30% of all new debt obligations in recent years have a credit rating of A3 even though there are up to twenty-one other possible ratings to choose from. As presented later in this section, an A3 rating is the optimal target rating (OTR) for most of our tests.
We begin the process to compute costs of borrowing by retrieving fifteen credit spreads from Damodaran (2019) that are matched to fifteen credit ratings and fifteen interest coverage ratios (ICRs) for each of Damodaran's three firm categories. The use of fifteen when referring to Damodaran's data should not be confused with our use of fifteen when referring to our fifteen tests. Damodaran's small firm category includes non-FS firms with a market capitalization under five billion US dollars while his large firm category consists of non-FS firms that are over five billion US dollars.
Damodaran reports ICRs for each firm category in terms of ranges where the upper bound for the first ICR is 100,000 implying a virtual unlevered outcome for the highest credit rating of Aaa. Instead of taking the midpoint of this range (as that would still be a virtual unlevered outcome), we reduce the first ICR midpoint for each firm category so it yields a first levered P choice for a rating of Aaa that is reasonably close to being in the middle between zero (the unlevered P choice) and the second levered P choice for a rating of Aa2. We use the midpoint for the next thirteen ICR ranges to determine the next thirteen ICRs. The last range for the small firm category has the same problem as the first range with a lower bound range of −100,000. For small firms, we use an approach similar to that used for its first range when assigning values. However, for the large and FS firm categories, we simply use the upper bound of the range since it is the least unfeasible selection. For our fifteen tests, the smallest and largest ICRs are 0.05 and 24, respectively.
While ICR is traditionally defined as EBIT/I, Damodaran (2019) refers to ICR as after-tax operating income divided by interest (I). Thus, for Damodaran, ICR = (1−T)EBIT/I where T is the effective tax rate on business income, which for C corps is the effective corporate tax rate. Rearranging the ICR equation and noting that T is the same as the levered corporate tax rate (T C2 ) used by Hull (2018) and EBIT is the accounting term for the CSM's cash flows before taxes (CF BT ), we have I = (1 −T C2 )CF BT /ICR where we assign $1,000,000 to CF BT for all tests. Since there are fifteen ICR outcomes per firm category, we can compute fifteen I outcomes for each small, large and FS test. I outcomes are before personal taxes are paid on interest income and thus are the same as that found in a financial statement.
From these fifteen I outcomes for each firm category, we use corresponding outcomes for the effective tax rate on debt (T D ) and the cost of debt (r D ) to calculate fifteen debt (D) outcomes where D = (1 −T D )I/r D . After computing fifteen D outcomes, we compute fif-teen P choice outcomes where P choice = D/E U with E U standing for unlevered equity. Table 1 reports P choices in the second column. The first row contains the unlevered P choice of zero while the next fifteen rows have levered P choices. It can be noted that outcomes for P choices differ for each test because ICRs and/or E U outcomes change with each test.
The "DV " column reports DV outcomes where DV = D/V L with V L = G L + E U where V L is firm value and G L is the gain to leverage. This column reveals a sequence of increasing DV outcomes that are matched to credit ratings that fall in quality. This sequence is needed to identify the DV and the credit rating that correspond to max V L and, respectively, become ODV and OTR.
To get fifteen costs of debt (r D ) that correspond to fifteen P choices, we add each credit spread to the risk-free rate (r F ) of 3% so that r D = r F + credit spread. An r F of 3% is consistent with the 30-year government bonds as given by FRED (2019a) the past fifteen years. For the credit rating of A3 in Table 1, the credit spread given by Damodaran (2019) is 1.5625%. Thus, for a rating of A3, we have: r D = r F + credit spread = 3% + 1.5625% = 4.5625%. This outcome is reported in the bold print row of the "Cost of debt (r D )" column.  Table 2. A P choice is the proportion of unlevered equity (E U ) retired by debt and DV is the debt-to-firm value ratio. The bold print row contains the optimal outcomes. Thus, the target A3 rating is also the OTR. The gray shaded rows indicate that the RE (growth) constraint given by Hull (2018) is violated and so outcomes in these rows are not feasible. For the first row, the costs of borrowing are actually unlevered outcomes. For example, r D is the risk-free rate; r L is r U ; g L is g U ; and r Lg is r Ug where the "U" refers to unlevered outcome. All other rows supply levered outcomes. To get the costs of equity (r L ), we add an equity risk premium over a corporate bond portfolio (EPB) to each cost of debt. Damodaran (2019) suggests that EPB is near 3.1% while FRED (2019b) indicates 3.5% to 4.1% with an average of 3.8%. Since the midpoint of 3.1% and 3.8% is 3.45%, we use EPB = 3.45%. By adding 3.45% to our fifteen increasing r D outcomes, we get fifteen increasing r L outcomes given by r L = r D + EPB. Continuing our illustration for an A3 rating, we have r L = r D + EPB = 4.5625% + 3.45% = 8.0125%, which is reported in the bold print row of the "Cost of equity (r L )" column. The cost of equity in the first row of 7% is actually the unlevered cost of equity (r U ) as this outcome occurs for the unlevered P choice of zero. This outcome of 7% is computed using the CAPM with a market return (r M ) of 8% and the unlevered equity beta (β U ) of 0.8 where r M and β U are consistent with data given by Damodaran.

Moody's
To illustrate, we have: The outcomes for the growth test in Table 1 (like all growth tests) are based on choosing a target credit rating and setting the plowback ratio (PBR) so that the historical growth rate of 3.12% is achieved for that target. In Table 1, this is achieved in the bold print row where we find 3.1192% in the "Levered equity growth rate (g L )" column where 3.1192% is the closest approximation to 3.12% when limiting PBR to four decimal places. The rate of 3.12% is an historical sustainable rate based on a seventy-year period as described earlier. The first row of this column is actually the unlevered growth rate (g U ). This rate, as derived by Hull (2010), is a function of PBR. Each g U can be different causing a different E U outcome for each growth test (as will be seen in Panels B, C, and D in Table 2).
The last column provides outcomes for the growth-adjusted cost of equity (r Lg ) where the first row is actually the growth-adjusted cost of unlevered equity (r Ug ). Outcomes for r Lg are computed as r Lg = r L -g L (for the first row, it is r Ug = r U -g U ) While not shown in Table 1, we can compute outcomes for debt betas (β D ) and equity betas (β E ) using the CAPM. For our large growth test in Table 1, β D = 0.3125 and β E = 1.0015 for a rating of A3. The latter is near the market beta (β M ) of 1.00. For all of our tests, the typical OTR occurs with a β E that is close to β M .
Beginning with a rating of Ba2, the rows in Table 1 are shaded gray to indicate that ratings below Ba1 are not feasible as the RE (growth) constraint given by Hull (2018) is violated. Thus, all numbers in these rows are not feasible. These unfeasible choices come after the OTR of A3 and the ODV of 0.3575 are attained. Of interest, the rating of Ba1 is the first non-investment grade that is characterized as speculative. Thus, besides being unfeasible, ratings below Ba1 would not be desired.
The growth tests in this section cover the four medium investment grade (IG) ratings of A1, A2, A3, and Baa2 that Damodaran (2019) uses. We limit the reporting to these four ratings, not only for brevity's sake, but because medium IG ratings are the most common attainable target ratings. For example, from June 12, 2018 through April 29, 2019, Morningstar (2019) reports that 61 of the 75 newly rated debt issues had a medium IG credit ratings. Thus, less than one-fifth of the ratings are either higher IG ratings or non-IG ratings. Morningstar's article is consistent with the fact that the highest IG rating of Aaa is becoming extinct. To illustrate, the Financial Times (2019) reports that there were 98 US companies that held the rating of Aaa in 1992 but currently there are only two. For our tests, targets below Baa2 are never OTRs, which is consistent with their rarity among recent debt issues.

Graphical Display of the Gain to Leverage and Its Two Components
Using the growth G L equation for C corps as given by the CSM, Figure 1 graphically displays G L and its two components when plotted against feasible DV outcomes. This figure uses the large growth test that was featured in Table 1. Figure 1 excludes outcomes that are not feasible due to violation of the CSM's RE (growth) constraint.  The second component (dashed line) is concave upward bottoming out early at −$0.022M where DV is only 0.1052. Together these two components that represent G L (solid line) are concave downward peaking at the ODV of 0.3575 at which point the max G L of $1.067M is achieved. At this point, Moody's rating is A3 and so this rating is the OTR. Table 2 reports eleven outcomes for our fifteen C corp tests that are based on before-tax cash flows (CF BT ) of $1,000,000. The outcomes given in the eleven columns are as follows. Credit rating in Panel A refers to the OTR for the three nongrowth tests. For Panels B, C, and D, credit rating refers to the medium IG credit ratings for which outcomes are reported with the OTR identified in each panel by the bold print row. P choice is the proportion of unlevered firm value retired by debt. DV is the debt-to-firm ratio. PBR is the plowback ratio, g U is the unlevered equity growth rate, and g L is the levered equity growth rate. E U is unlevered firm value, G L is the gain to leverage, and V L is firm value.

Eleven Outcomes
The dollar values for E U , G L , and V L are in millions.
%∆E U is the percentage change in E U (or G L as a percent of E U ). Net benefit from leverage (NB) is G L as a percent of D and reveals the percentage gain per dollar of debt.
Each test in Table 2 involves either a nongrowth or growth situation for one of the three firm categories of small, large, or FS. For the nongrowth results in Panel A, each firm category requires only one test to identify optimal outcomes. This is because each medium IG rating does not have to attain the same growth rate for all four medium IG ratings by engineering PBR since it is zero for all nongrowth tests. Whereas a nongrowth firm could target a credit rating that differs from its OTR, it would not want to do this unless its OTR could not be achieved. Of importance, our three nongrowth tests identify an OTR that is a commonly attainable medium IG rating, namely, A3 and Baa2.
Unlike the nongrowth tests in Panel A where each credit rating has the same PBR of zero, the growth tests in Panels B, C, and D require that we engineer PBR to obtain the same g L of 3.12% for each Mean V L outcomes (in millions of dollars) are plotted against credit ratings. The solid, dashed, and dotted lines represent small, large, and financial service (FS) firms, respectively. NOTE: "n.a." stands for not applicable as this is the unlevered situation. of the four medium IG ratings. This requires four separate tests for each firm category of small, large, and FS. From each set of four tests we can discover max V L , which identifies OTR. For Panels B, C, and D, the optimal row is in bold print as this row gives the highest V L (or max V L ) from among the four V L outcomes reported in each panel.

Plotting V L Outcomes against
Credit Ratings Table 2 only reports outcomes for medium IG ratings. In Figure 2 we expand on this by plotting mean V L outcomes (in millions) against credit ratings from Aaa to Ba2. The mean V L outcomes are the averages for three sets of five tests, namely, five small tests, five large tests, and five FS tests. We stop at Ba2 because credit ratings with lower quality are not only suboptimal but often unfeasible. Every mean V L plot point does not consist of outcomes from all five tests (one nongrowth test and four growth tests) because growth tests experience violation of the RE constraint. While the violations lower the number of observations used in computing a V L mean, these violations occur after the OTR is reached.
The top trajectory (dotted line) in Figure 2 displays higher mean V L outcomes for FS firms, while the bottom trajectory (solid line) displays the lower mean V L outcomes for the small firms. Except for the downward bump for an A1 credit rating for the small firm trajectory, the three trajectories in Figure 2 are concave downward with the small firm and large firm trajectories manifesting some flatness around their OTRs. Noteworthy, the OTR for all three trajectories using mean V L outcomes is A3. This differs from that found for individual tests presented in Table 2 where only large firms had an A3 outcome for both nongrowth and growth tests.

DISCUSSION
In this section, we discuss the results presented in the previous section.
From Figure 1, we see that the use of real world data shows strong support for trade-off theory that predicts the downward concave shape found when plotting G L versus DV. The general downward concave shape also holds for other tests. For example, as seen in Figure 2 where we plot mean V L outcomes against credit ratings, we also find downward concave shapes.
For small nongrowth firms in Table 2, we find that max V L is associated with an OTR of A3, which is the second lowest of the four medium IG credit ratings used by Damodaran (2019). For small growth firms, we discover that max V L is affiliated with an OTR of A2, which is a notch above the nongrowth rating. Thus, managers of small nongrowth firms should aim for a higher ranked OTR if they contemplate growth. All four of the IG medium rating tests for small growth firms have greater V L outcomes compared to their nongrowth max V L outcome. In terms of how much it pays, the max V L for small growth firms is 5.13% greater than their nongrowth max V L . We conclude: It pays for managers of small firms to aim for growth regardless of which one of the four medium IG ratings they think is achievable.
For large nongrowth firms in Table 2, we find an OTR of A3, which is the same as that found for small nongrowth firms. Despite having the same OTR, large nongrowth firms have a 5.87% firm valuation advantage over small nongrowth firms when comparing max V L outcomes. Like large nongrowth firms, large growth firms have an OTR of A3, which is a notch below the OTR of A2 found for small growth firms. The max V L for large growth firms is 2.76% better than that for small growth firms. The max V L for large growth firms is only 2.04% greater than that the max V L for small nongrowth firms, which is less than 5.13% found for the same small firm comparison. We conclude: Growth for large firms is not as lucrative as that for small firms despite the fact that large growth firms have a valuation advantage over small growth firms.
For nongrowth financial service (FS) firms in Table  2, we find that max V L occurs for an OTR of Baa2, which is a notch below the OTR of A3 found for small and large nongrowth firms. FS nongrowth firms have 39.35% and 31.61% valuation advantages over small and large nongrowth firms, respec-tively. In contrast, the max V L for FS growth firms is 20.11% and 16.89% better than the small and large growth max V L outcomes, respectively. For FS growth firms, we find that max V L identifies A3 as the OTR, which is the same as large growth firms. However, the least V L for FS growth firms occurs for the highest medium IG rating of A1. This contrasts with the findings for small and large growth firms where the lowest medium IG rating of Baa2 generates the least V L . Of importance, the four FS growth tests produce V L outcomes that are inferior to their nongrowth max V L . We conclude: Compared to managers of small and large firms, FS managers can profit more from using their operational cash flows for interest payments on debt rather than for retained earnings earmarked for growth.
In terms of optimal debt-to-firm ratios (ODVs) that correspond to OTRs, FS firms have the greatest ODVs with large firms placing a distant second and small firms placing an even more distant third. For example, growth ODVs have respective outcomes of 0.2059, 0.3575, and 0.8212 for small, large, and FS firms at OTRs. In terms of the gain to leverage (G L ) and the percentage increase in unlevered firm value (%∆E U ), FS growth firms are the clear winner over small and large firms even though FS firms are still better off not growing. The results of the %∆E U for small and large firms are consistent with the empirical research (Graham, 2000;Korteweg, 2010;Van Binsbergen et al. 2010). Finally, small firms utilize the highest plowback ratios and FS firms the lowest.
While we do not report detailed outcomes for the two high IG ratings (Aaa or Aa2) of Damodaran (2019) for growth tests, we can point out that a growth firm can achieve a greater V L outcome if they can achieve either of these higher ratings while sustaining a growth rate of 3.12%. In contrast, an OTR is not achieved with a high IG rating for our nongrowth tests.
Finally, Figure 2 reveals that a Moody's credit rating of A3 is the dominant OTR. This is consistent with the results in Table 2 where A3 is the most common OTR with these two exceptions: the OTR attains a higher quality rating of A2 for small growth firms and a lower quality rating of Baa2 for FS nongrowth firms.

CONCLUSION
In this paper, we explore the relation between credit ratings and firm valuation. We do this by using data supplied by Damodaran (2019) within the framework of the Capital Structure Model (CSM) where we are able to compute firm values matched to credit ratings. From the firm value computations, we can determine the maximum firm value (max V L ) and optimal target rating (OTR) thereby offering insight to US C corp managers. This insight covers nongrowth versus growth situations and three firm categories of small, large, and financial service (FS). This is the first study to use real world data along with the CSM's levered equity growth rate to identify OTRs. This enables us to provide unique findings on the topic of target credit rating and firm valuation. Focusing on medium investment grade ratings that contain over 80% of the recent ratings, we show which of these ratings offer superior valuation outcomes for growth based on its firm category. For growth tests, we use a long-run sustainable growth rate of 3.12% that is consistent with seventy years of annual growth in real US GDP. We show that firm valuation outcomes that are a by-product of the target credit rating, firm characteristics (small, large, and financial service), and the capacity of a company to grow.
In terms of future research, this study can be extended by using a greater growth rate than the historical sustainable rate of 3.12%. For example, the Tax Policy Center (2018) and Tax Foundation (2018) predict that lower taxes under TCJA are expected to lead to an increase in growth of 0.8% and 1.7%, respectively. Future research can investigate the relation between a credit rating and firm value for other US ownership types besides C corp, namely, pass-throughs and nonprofits. In addition, future research can perform comparative studies between ownership types where we assume similar risk classes so that differences in OTRs are a function of the way different ownership forms are taxed. Future research can also explore ownership types in other countries to find what credit ratings are associated with OTRs. Researchers needs to continue to explore credit ratings for future years as ratings and spreads change over time. This will tell us how sensitive a current OTR is to changes in credit ratings and spreads. Finally, this study can be duplicated with other reasonable tax rate scenarios besides those corporate and personal tax rates that this study used.