At S&P Capital IQ, we offer a wide range of automated tools that can save treasurers and risk analysts precious time during their daily workflows for credit monitoring and surveillance purposes. Our tools combine a variety of credit risk metrics, including Standard & Poor’s Ratings, and S&P Capital IQ’s CreditModel, PD Model Fundamentals and PD Model Market Signals.
Sometimes we hear clients asking how to reconcile potential dissonance between various credit metrics: for example, a company may get a CreditModel score or a Standard & Poor’s rating in the high-end of the Investment Grade spectrum that is deteriorating over time, a PD Model Fundamentals value barely in the Investment Grade, that keeps improving, and a PD Model Market Signals value in the speculative grade range, that remains quite volatile. At S&P Capital IQ, we suggest to recall the different drivers of these models, and their dynamics.
S&P Ratings and statistical models trained on ratings (such as CreditModel) tend to produce stable views of credit-worthiness, so they change only when there are fundamental/structural changes in the company credit profile; so they are naturally more stable than models that are updated on a daily basis, and instead incorporate market information (such as PD Model Market Signals), and are thus affected by the volatility that sometimes arises from market expectations, fears and hopes.
In reality, long-term and short-term views of credit risk are both useful, and can coexist, and shall coexist even when they offer apparently conflicting signals. Let me draw a parallel case from the stock markets (see Fig. 1).
From this plot, we can immediately identify the long-term trends for the stock price: long-term (solid lines), mid-term (dashed lines) and short-term (in the ovals). The latest long-term trend actually developed since 2008 and it is bearish; still, within the long-term negative view, that lasts several years, there are positive and negative mid-term trends lasting several months and even short-term trends, lasting from a few days to a few weeks.
The short-term view always leads the other views, sometimes correcting back to the medium or long-term tendency, whilst in other instances it drives the long-term and medium term views, until they align with it.
Overall, it is possible for different trends and views to co-exist, and they will not always agree with each other. However, this does not mean that one view is wrong and the other is right; rather, they complement each other.
One conclusion I can derive from this plot is that when all views agree, like towards the end of 2014, this shall sound as an alarming signal and I may want to get out of that stock.
In fact, this example drawn from the stock market has far-reaching consequences, also in the space of credit risk indicators.
Table 1 shows the realized default rates, historically observed, for companies that are assessed with two models at the same time, one medium-term, such as PD Model Fundamentals and one short-term such as PD Model Market Signals.
Basically, we look at the companies that get a PD from both models, between Jan 2004 and Dec 2013, and bucket the companies in different cells, and then we record the observed default rates realized within each cell. Similar tables can be prepared to study all other combinations, such as Standard & Poor’s Ratings or CreditModel and PD Model Market Signals, or PD Model Fundamentals.
At the end, all tables convey the same message: the actual default rate increases when the PD generated by both models increases, and in some cases can even exceed the boundaries of the cell, like towards the right-bottom side of the table.
This confirms the intuitive idea that when companies receive a negative view from many perspectives, we should not ignore this, and the same usually applies for companies where all models give a positive perspective, in line with our example from the stock markets.
In the middle, we have the “controversial” cases, where a model may have a long-term positive view, and the short-term view may be negative; in these cases, we may want to flag these companies and include them in a watch-list, for further in-depth analysis, for example checking news and key developments collected by S&P Capital IQ or for further monitoring.
To support this process and automate it, S&P Capital IQ offers fully customizable credit risk tools, such as the “Credit Surveillance Template” and the “Credit Health Panel”, that enable users to slice and dice a portfolio of hundreds of companies and detect pockets of credit risk within a matter of seconds, or drill down to the individual company level and get early warning signals of deterioration/improvement, based on pre-defined thresholds, and identify suitable alternatives.
 CreditModel, PD Model Fundamentals and PD Model Market Signals are quantitative, statistical models that use socio-economic factors and company fundamentals or market equity information to generate estimates of creditworthiness, expressed as letter grades or probability of defaults.