The mortgage servicer company I worked with was frustrated with the custodian’s tracking system. Frequently the servicer company would inquire about where the documents were located and the custodian couldn’t tell them. The firm of mortgage custodians felt they had a good process for identifying and retrieving documents and didn’t want to hear complaints from the servicer. Nancy, the custodian CEO, told me, “Those mortgage servicers are never happy, no
matter what we do!” In time, however, this servicer got a bad reputation among closers and lenders. It seemed that every time there was a closing and this particular company was involved, documents arrived late—causing late closings, scheduling nightmares, and angry buyers and sellers.Gradually closers and lenders started to avoid this servicer.

When I discussed the issue with Bob, the servicer CEO, he said the custodian was trying to drive them out of business. I thought this was a bit paranoid—especially since Nancy, the custodian leader, had told me several times she had tried to improve the situation between the two groups. But, Nancy said, “mortgage servicers just aren’t interested in partnering to fix the process.”

In another example, Xcel Energy, the fourth-largest energy company in the United States, with a delivery network in thirteen states, outsources its IT functions to IBM.Why would Xcel Energy duplicate the capabilities of one of the largest information technology companies in the world when they can partner with them, receive their expertise and knowledge, and spend less for the service?

Let me cite one more example. I once worked with two partners who were in the mortgage banking business. In that business there are many players, each with a specialized role. There are closers who close a real estate transaction, lenders who loan the money, mortgage servicers who handle the loan, and mortgage custodians who manage the documents. This case involved mortgage servicers and mortgage custodians.

The servicers handle customer calls, accept and apply payments, and pay escrowed taxes and insurance for the mortgagee. The custodians file and store the documents and then, when they’re needed, ship them to appropriate locations—a cumbersome process considering the thousands of real estate transactions that occur every day.

In the Bank of America/Exult case, the bank was looking to get out of the human resource transaction business. They looked to Exult to help them manage their 120,000 associates. To be successful at this, the bank needed an entire infrastructure including an 850-person call center, telecommunications network, and Web site to manage the volume. With the transfer of the bank’s HR call center to Exult, which had the infrastructure already in place, the bank could focus its energy on servicing its customers’ financial needs.

NASA is very capable of launching probes toward the sun. However, APL at Johns Hopkins has a track record of being able to launch targeted missions more economically than NASA. NASA’s real expertise lies in its ability to manage multiple missions while making sure taxpayers’ monies are being spent wisely. It makes sense that NASA, with its strong management competencies, provides mission oversight while others contribute based on their strengths.

Today in the United States, a company using its workforce as Ford did would find it tough to stay afloat. Such labor-intensive processes would be too expensive. And besides, people are generally more educated today and want work that’s more meaningful.What robots can’t do is the creative, team-oriented work that requires sophisticated communication and decision making. Managing creative teams is different from managing mechanical processes. Consequently, when the way the workforce operates begins to change, management’s thinking also needs to change. In many organizations, however, workers are constantly asked to change and upgrade their skills—only to be led by people using the same style of management that was in vogue fifty years ago.

Management’s job today is to build partnerships within organizations. In an information-based economy, no single group can be the sole purveyor of the “raw material” needed to manufacture the product. In fact, after decades of diversification, many businesses are actively reverting to their core business competencies and outsourcing those portions of their business that are not designed to satisfy their customers needs.

So far we have described a rather intuitive way of combining individual views in a portfolio. Top-down and bottom-up analyses have determined the overall strategy for the portfolio, spread class and sector selection and finally issuer weightings. This qualitative methodology does not require estimates of returns, risks and correlations between the investments, and therefore is easy to implement. Yet, it is not able to capture the full benefits of diversification and to tailor the expected risk/return profile of the portfolio to the preferences of the investor. Since the seminal work of Markowitz (1952), diversification is a central tenet of modern investment theory. In the context of credit portfolios it plays a crucial role, because it helps to control downside risk arising from single issuer credit events. Since the mid-1990s debt-financed M&A activities, share buybacks, and the introduction of new technologies have fueled the new issue pipeline and broadened the corporate bond universe. Meanwhile, the European corporate bond market offers sufficient market breadth and depth for institutional investors to construct thoroughly diversified portfolios. If the portfolio manager is capable of quantifying the risk and return characteristics of his investment alternatives, portfolio optimization approaches present a formalized and thus objective way of deriving investment recommendations. This applies irrespective of the performance target of the investor. Portfolio optimization can be used with respect to portfolios that are managed in absolute risk/return terms as well as portfolios that are managed relative to a benchmark index. Various constraints can be included, for example a short sales restriction for real money investors, maximum concentration limits or esired duration ranges.

The rating agencies have been criticized for being too slow to react to changes in the credit quality of an issuer, leading to serially correlated rating patterns and limiting the value of ratings as a risk management tool. As a reaction, Moody’s decided to put its rating process under review, and acquired KMV to be able to provide investors with additional, marketbased assessments of an issuer’s credit quality. The feedback from market participants was surprising. Since investors themselves tend to use spreads and spread volatility as indicators for credit risk, the vast majority does not want Moody’s or the other rating agencies to switch to a more marketbased approach when assessing the credit quality of an issuer. There is really a need for, according to the feedback, more transparency with regard to the rating process. This would allow investors to use rating agency information in their risk management most efficiently.

For purposes of risk management bonds are often grouped according to agency ratings based on the assumption that bonds with similar ratings tend to show a high degree of comovement. Breger et al. (2003) examine whether the correlation between individual bonds increases if they are grouped by implied ratings, that is by spread classes rather than by agency ratings. The rationale for this would be that market valuations are a better indicator for the drivers of credit spread changes, namely perceived credit quality and risk exposure, than are agency ratings. In their empirical study they find that bonds of the same spread class are more similar than bonds with the same rating from a risk/return perspective. Breger et al. (2003) conclude that the classification of bonds based on market data provides a more reliable basis for modeling return relationships than does a classification by agency ratings. However, one has to note that the motivation behind this study differs significantly from the rating agencies’ approach. The objective is not to predict default risk, but rather to improve the classification of corporate borrowers and provide a basis for reliable spread risk forecasts.

The addition of the individual contributions to expected excess return in Our study yields an expected 1-year excess return of 88.2 bp for A-rated corporate bonds with a maturity of 5-years. This is significantly below the initial spread of 100 bps. The difference reflects the fact that a downgrade is more probable for A-rated corporate bonds than an upgrade, and that the associated spread changes are not symmetric. The magnitude of spread widenings due to downgrades is usually much higher than the spread tightening after rating upgrades. It is interesting to note that among investment grade bonds the ratio of upgrades to downgrades is most favorable for Baa-rated bonds. However, in the case of a downgrade these bonds often suffer massive price declines, because they fall below investment grade levels.

A strong negative correlation between default rates and economic growth could always be expected, but during the economic crisis in the United States in the 1970s and 1980s a sharp increase of the default rates could not be observed. It was the economic slowdown in 1990, which was accompanied by extremely increasing default rates. The same acceleration of default rates occurred in 2001 due to a prolonged downturn of the economy, high political uncertainty after September 11, 2001, company accounting scandals, and aggregate weak credit fundamentals. Structural changes in the credit markets can explain the weaker than assumed relationship between the default rates and the economic cycle. This leads to the conclusion that the relationship between default rates and the spread level in the corporate bond market is a very complex one and that a good performance in the corporate bond market (tightening of credit spreads) can be consistent with increasing default rates at times where the market anticipates future decreasing default rates.

The following paragraph will deal with default rates because they are of central importance for high-yield investors. We compare the three major default peaks since 1920. Default rates have to be distinguished between “issuer-weighted” and “dollar-weighted”. The increasing amount of Fallen Angels in 2002 resulted in a sharp increase of the “dollar-weighted” default rate.

High-yield spreads tend to lead default rates, which means that a tightening will occur prior to a fall in the default rate because market participants will already anticipate the future development of the default rate. This relationship broke down in 2002 for a couple of months due to the large divergence of the “dollar-weighted” from the “issuer-weighted” default rate. As we can see cumulative default rates tend to increase progressively with a decreasing rating class.