# The Texas Ratio Helps you Evaluate Banks

## Texas Ratio Formula

To calculate the Texas Ratio, divide the bank’s bad assets by the assets available to cover those losses. More specifically:

• Divide nonperforming assets by tangible common equity and loan-loss reserves.

Nonperforming assets include defaulted loans and real estate that the bank has taken possession of through foreclosure. Those assets are risks that could potentially become expenses for the bank. However, some loans might be government-backed loans, and the bank will be reimbursed if those loans default. When doing your own calculations, be sure to separate loans issued under government programs.

Next, you’ll want to know how easily the bank can handle those expenses. When calculating tangible equity, be sure to remove intangible assets like goodwill—since the bank can’t write a check out of the “goodwill” account to pay creditors.

A bank with a high Texas Ratio—especially if the ratio approaches 1 (or 100%)—is riskier than a bank with a lower Texas Ratio.

As an example, assume a bank has nonperforming assets of \$90 billion, and tangible common equity plus loan-loss reserves of \$100 billion. Divide \$90 billion into \$100 billion for a result of .9 or 90%. This is a relatively high ratio, and you should only use this bank with caution. For example, you might consider this bank if the ratio is clearly decreasing, you’re staying below FDIC coverage limits, and you know that there’s a solid plan in place to further reduce the ratio.

If you don’t like the idea of calculating the ratio yourself—remember that you’ll have to dig through filings and separate out government-guaranteed loans—find out if somebody has already done the work for you. Websites might publish Texas Ratios (or lists of banks with the highest and lowest ratios, which might provide enough information to make a decision).

## Conclusions and implications

Using a sample of 63 European banks and quarterly data over the period 2005–2018, this paper investigates the relationship between capital and asset quality with bank risk and performance. Our main contribution to the literature consists in disentangling the complex concepts of capital and asset quality as well as bank risk and performance by employing several definitions of each of these banking dimensions, thereby providing both a more detailed and comprehensive picture of the connections between them and an interpretative framework which accounts for the recent NPL’s regulation overhaul.

Regarding the capital measures employed in this study, our findings point out a significant and negative relationship between the simple book leverage ratio and equity volatility which is consistent during the crisis period and for large banks. In addition, the effect of the leverage ratio on equity volatility is significant only in relation with the 25th quantile of the latter’s distribution. Our interpretation of the result is that the information content of the book leverage is negligible and mostly referred to a dimensional feature [58].

Our risk-based capital measure, the total capital ratio is instead a poor predictor of idiosyncratic risk, systematic risk and equity volatility. Nevertheless, it shows a positive and statistically significant relationship with both the Z-score and the distance to default. As such, risk-based capital ratios are effective in enhancing bank stability and reducing its distance to default. These findings hold during the crisis period with respect to insolvency risk whose relationship with the total capital ratio does not appear to be moderated by size contrary to the relationship between the total capital ratio and the Z-score which appear to be significant only for small banks which we attribute to the TBTF bias. In addition, the quantile regression analysis does not point out marked differences regarding the effect of the total capital ratio on the risk and stability variables above-mentioned between the lowest and highest percentiles of their distribution. With respect to performance variables, instead, the total capital ratio shows a positive and statistically significant relationship with all the measures employed which is further confirmed for the crisis period, as regards ROA and PBR. Size does not moderate the relationship between the total capital ratio and ROA but moderates that with EVA and PBV for large banks which we attribute to their excess capital management dynamics. In addition, the quantile regression analysis does not bring out marked differences as regards the effect of the total capital ratio on ROA and PBR between the lowest and highest percentiles of their distribution.

As regards asset quality variables, the Texas ratio spread shows a consistent and negative relationship with both stability measures which holds during the crisis period. Bank size does not appear to moderate the relationship between the Texas ratio spread and the distance to default but moderates that with the Z-score for small banks which we attribute to the excess capital harnessed by large banks which may offset the negative impact due to deviating from the equilibrium. In addition, the quantile regression analysis does not report marked differences between as regards the effect of the Texas ratio on both stability measures between the lowest and highest percentiles of their distribution.

Consistently, the LLP ratio shows the same relationship and effects on both stability measures. However, differently from the Texas ratio spread, size does not play a moderating role in its relationship with the Z-score. Coherently, the LLP ratio shows a significant and positive relationship with the equity beta and equity volatility in the baseline model which holds during the crisis period for equity volatility. As regards bank size, it appears to moderate all the relationships between the LLP ratio and the risk variables suggesting a propensity for large banks to merely abide by provisioning rules in contrast with small banks’ propensity to harness provisioning as a mechanism for signalling about future earnings. The LLP ratio also shows a negative and consistent relationship with ROA and PBR which is consistent during the crisis period and not moderated by bank size and further does not show marked differences in terms of effect of the LLP ratio on performance variables with respect to their distribution. The LLP ratio and EVA, instead, shows a positive relationship, consistent across the crisis period, not moderated by size and with a significant effect across all the EVA’s distribution which, however, is confuted by the results on the negative and significant relationship between the coverage ratio and EVA. The result is that we cannot draw robust conclusions about the signalling effect suggested by the relationship between the LLP ratio and EVA nor about the negative signal suggested by the relationship between the coverage ratio and EVA. Finally, the coverage ratio does not show a consistent relationship with the variables considered in this study but the scattered relationships highlighted mainly support the conclusions drawn by the LLP ratio as regards provisioning policies.

As regards the liquidity and funding measures, both show a consistent and positive relationship with equity volatility across all analyses employed therefore suggesting that a lack of liquidity and a strong reliance on short-term funding both increase risk. Coherently, both measures also show a significant and negative relationship with the distance to default, as regards stability measures, and ROA and PBR, as regards performance measures for baseline results. These relationships mainly hold also for the crisis period as both liquidity and funding measures are negatively related to ROA and the funding measure still shows a negative relationship with the distance to default and the Z-score as well. As regards the moderating effect of size, the relationships between the distance to default and the funding measure and between the PBR and the funding measure are not moderated by size, whereas for the same dependent variables, size moderates the negative relationship with the liquidity measure for large banks because of their higher sensitivity to fluctuations of deposits and short-term funding. Both the liquidity and funding measures are, instead, significantly moderated by size when related to the ROA for small banks due to the intrinsic characteristics of their business models. The effect of the liquidity and funding measures on the dependent variables abovementioned mainly does not show marked differences with respect to their distributions.

According to our results, the negative implications that severe provisioning policies may have on bank stability and performance should warn policymakers about reconsidering the measures deployed to overhaul the NPLs’ regulation, especially in the light of the outbreak of the pandemic which is likely to jeopardize the efforts made so far to counter the NPL issue in Europe. In detail, the supervisory expectations regarding the level of coverage on non-performing exposures may cause unprofitable rushed disposals which would affect the capital base. In addition, such approach would also have negative repercussions on profits and the bank’s capital generation ability as emerges from our in-depth analysis of the Texas ratio. In short, forcing banks to pursue more severe hefty provisioning policies could undermine bank stability and performance, especially in a context already characterized by a weak level of profitability [45]. Moreover, the results highlighted by our capital measures cast some shadows on the purpose of the minimum coverage level of making the risks associated to NPLs better reflected in the CET 1 capital ratios. In detail, our results about the leverage ratio point out that it is a poor predictor of bank risk and suggests that it may be perceived only as measure of size by investors. Conversely, the total capital ratio appears to be a crucial driver of bank stability. However, risk-weighted capital ratios are exposed to manipulation by management that struggles to comply with capital regulation [27]. We, therefore, raise some concerns regarding their ability to accurately reflect the real risk exposition of banks. This ultimately undermines regulatory purposes of increasing CET1 sensitivity to the risks associated to NPLs.

Finally, we provide additional evidence of the positive relationship between capital and bank performance and further suggest it as leading driver of regulatory purposes of higher coverages and more stability.

## What is the Texas Ratio?

The Texas Ratio is a financial metric used by analysts to predict which banks have the potential to experience “credit” related issues, meaning a lot of customers who are unwilling or unable to make their loan payments.  The ratio was originally developed by Gerard Cassidy and his colleagues at RBC Capital Markets and they found that when the ratio exceeds 100% or “1”, banks tend to fail.

The formula used to calculate the Texas Ratio is:

Non-Performing Assets are loans for which payments are not being made in a timely manner.  But, just because a borrower misses their loan payment does not automatically mean the loan is non-performing.  In most cases, a loan will not receive this status until the principal and interest are 90+ days past due.  So, in short, the numerator in this equation is the sum of the principal balance of all loans that are 90+ days past due.

Tangible Common Equity is one measure of a bank’s physical capital and it is calculated by subtracting a bank’s intangible assets (like Goodwill) and Preferred Equity from the bank’s book value.  And, Loan Loss Reserves is money that is set aside in anticipation of loans that move from non-performing into default.  Another way to think about this is that it’s money set aside for expected future losses.

So, the intent of the Texas Ratio is to see if a bank has enough equity and money set aside to cover any loans that go bad.  Or, put another way, does a bank have enough money to absorb potential loan losses while continuing to meet their other deposit obligations.  Ultimately, one way that a bank can fail is that they make too many bad loans and don’t have enough money to cover the losses.  As was the case with Washington Mutual, if this happens, it can cause depositors to lose faith in the bank and withdraw their funds en masse in a “run on the bank.”  When the money runs out, the bank collapses.

## How the Texas Ratio Works

The Texas ratio was developed as an early warning system to identify potential problem banks. It was originally applied to banks in Texas in the 1980s and proved useful for New England banks in the early 1990s. The Texas ratio was developed by Gerard Cassidy and other analysts at RBC Capital Markets. Cassidy found that banks with a Texas ratio of greater than 100 tend to fail.

During the 1980s Texas saw an energy boom. Banks financed the surge, but soon the oil surge died down and banks started to struggle. As a result, Texas saw the greatest number of bank failures from 1986 to 1992 in the nation.

As part of the Texas ratio, non-performing assets include loans that are in default or real estate the bank has had to foreclose on. These could become expenses for the bank. On the other side, tangible equity does not include intangibles that cannot be used to cover losses, such as goodwill.

## Why Does the Texas Ratio Matter?

We have established that the Texas Ratio is a predictor of potential bank failure, but how does this relate to real estate development and operation? Here are two ways:

First, for banks that continue to invest in their branch network, they could be a tenant in a commercial office or shopping center.  If a property owner is considering leasing space to a bank, the owner would be wise to review the Texas Ratio for the potential tenant as an indicator of financial strength.

Two, commercial property developers tend to carry large deposit balances in their checking accounts.  As such, it is wise to review the Texas Ratio for the bank where these balances are held to ensure they are not in danger of failing.  In the event of a collapse, FDIC insurance only covers balances up to a certain amount.  Above that, the funds could be at risk.

## Why Texas?

In the 1980s, the state of Texas experienced an economic boom largely driven by energy, but the party couldn’t last forever. Banks helped finance the boom, and they didn’t always get repaid when things went bust. While banks in other states experienced similar results, Texas was remarkable: According to the Federal Reserve Bank of Dallas, “the state led the nation in bank failures every year from 1986 through 1992.” Gerard Cassidy then developed the calculation and coined the phrase “Texas Ratio.”​

Texas got a bad rap because of timing: The ratio was invented during an oil boom. Other regions have seen their own boom and bust economic cycles.