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the marketplace? If investors focus on ultimately arriving at their destina-
tion and are indifferent to how they get there, then they will find great value
in a market risk orientation to investing. If the “destination” is high capital
exposure/shoot-for-the-stars, then a variety of investment products could fill
the needs, products that cut across traditional lines separating bonds from
equities (and other conventional product categories).
Issuers and investors, as well as regulators and rating agencies, will
increasingly ask these questions and creative responses will need to be pro-
vided. For example, one approach might be construct and maintain a com-
parative total return table that would provide total return and risk profiles
as sliced by credit risk as opposed to product labels. How do total returns
of junior subordinated debt issued by a double-A-rated company stack up
against the senior debt of a triple-B-rated company, for example, and how
do these compare with a preferred stock? Much exciting work lies ahead.

This chapter showed how combining various legs of the product and cash
flow triangles can facilitate an understanding of how various strategies can

Ecuador™s Brady bonds, which were backed by U.S. Treasuries, nonetheless went
into default in 1999.


be developed and how products can be created. How credit can be a key
factor within the product creation process was considered. There are hun-
dreds upon thousands of actual and potential products and strategies in the
global markets at any given time. The purpose here is simply to provide a
few examples of how that creative process might be organized in a straight-
forward and meaningful fashion. Finally, the chapter presented an overview
of relative versus absolute return objectives and discussed a few portfolio
types that might be found under the heading of relative (capital preserva-
tion) or absolute (long/short).

Financial Engineering


Relative Return Investing Strategies
Many portfolio managers have their performance evaluated against a bench-
mark or index. The goal with such an exercise is generally either to match
the portfolio™s performance with the benchmark™s or to beat the benchmark.
Either objective is wrought with unique challenges. Indeed, it is the rare and
quite the exceptional fund manager who can successfully outperform the
market year in and year out and in a variety of market environments. For
such investors who have identified a systemized way of investing, their suc-
cess can be reflected in their fund™s alpha.
In the finance industry the term “alpha” denotes returns generated in
excess of a market index. For example, if the S&P 500 returns 10 percent
and a stock portfolio returns 11.2 percent, then 120 basis points of alpha
can be said to have been generated. Since alpha is typically used as a refer-
ence to excess returns, investors tend not to refer to negative alpha. In short,
either alpha has been generated or it has not. Recognizing that returns and
especially excess returns typically are generated in tandem with at least some
measure of risk, the financial industry uses the term “sigma” to denote vari-
ability of returns or the notion that returns can be negative, just as they can
be positive. It is certainly possible for a return to be negative yet also be a
contributor to positive alpha. For example, if the return of the S&P 500 is
8 percent while the return of the stock portfolio is “7.5 percent then it can
be said that 50 bps of alpha has been generated.
The notions of risk and reward, or sigma and alpha, are seen as insep-
arable and of great relevance when evaluating market opportunities. At many
firms these functions are called trading and risk management respectively,
and each area has detailed roles and responsibilities. For example, the trad-
ing function may be responsible for achieving the best possible execution of
trades in the marketplace, while the risk management function may be
responsible for overseeing the overall profile of a portfolio. Arguably, the
more successful firms are those that have found ways to marry these two
key areas in such a way that they are seen as complementary and reinforc-
ing rather than competing and at odds.
This appendix highlights some strategies that can be used to eke out a
few extra basis points of return for a benchmarked portfolio. Broadly
speaking, such strategies may be categorized as:

Stepping outside of benchmark definitions
Leveraging a portfolio
Capitalizing on changes within a benchmark™s parameters


For the equity markets, benchmarks are fairly well known. For exam-
ple, the Dow Jones Industrial Average (DJIA or Dow) is perhaps one of the
best-known stock indexes in the world. Other indexes would include the
Financial Times Stock Exchange Index (or FTSE, sometimes pronounced
foot-see) in the United Kingdom and the Nikkei in Japan. Other indexes in
the United States would include the Nasdaq, the Wilshire, and the Standard
& Poor™s (S&P) 100 or 500.
In the United States, where there is a choice of indexes, the index a port-
folio manager uses is likely driven by the objectives of the particular port-
folio being managed. If the portfolio is designed to outperform the broader
market, then the Dow might be the best choice. And if smaller capitalized
stocks are the niche (the so-called small caps), then perhaps the Nasdaq
would be better. And if it is a specialized portfolio, such as one investing in
utilities, then the Dow Jones Utility index might be the ticket.
Indexes are composed of a select number of stocks, a fact that can be a
challenge to portfolio managers. For example, the Dow is composed of just
30 stocks. Considering that thousands of stocks trade on the New York Stock
Exchange, an equity portfolio manager may not want to invest solely in the
30 stocks of the Dow. Yet if it is the portfolio manager™s job to match the per-
formance of the Dow, what could be easier than simply owning the 30 stocks
in the index? Remember that there are transaction costs associated with the
purchase and sale of any stocks. Just to keep up with the performance of the
Dow after costs requires an outperformance of the Dow before costs. How
might this outperformance be achieved? There are four basic ways.

1. Portfolio managers might own each of the 30 stocks in the Dow, but
with weightings that differ from the Dow™s. That is, they might hold
more of those issues that they expect to do especially well (better than
the index) while holding less of those issues that they expect may do less
well (worse than the index).
2. Portfolio managers might choose to hold only a sample (perhaps none)
of the stocks in the index, believing that better returns are to be found
in other well-capitalized securities and/or in less-capitalized securities.
Portfolio managers might make use of statistical tools (correlation coef-
ficients) when building these types of portfolios.
3. Portfolio managers may decide to venture out beyond the world of equi-
ties exclusively and invest in asset types like fixed income instruments,
precious metals, or others. Clearly, as a portfolio increasingly deviates
from the makeup of the index, the portfolio may underperform the index,

Financial Engineering

and disgruntled investors may withdraw their funds stemming from dis-
appointment that the portfolio strayed too far from its core mission.
4. When adjustments are made to the respective indexes, there may be unique
opportunities to benefit from those adjustments. For example, when it is
announced that a new equity is to be added to an index, it may enjoy a
run-up in price as investors seek to own this newest member of a key mar-
ket measure. Similarly, when it is announced that an equity currently in
an index is to drop out of it, it may suffer a downturn in price as relative
return investors unload it as an equity no longer required.

In the fixed income marketplace, it is estimated that at least three quar-
ters of institutional portfolios are managed against some kind of benchmark.
The benchmark might be of a simple homegrown variety (like the rolling total
return performance of the on-the-run two-year Treasury) or of something
rather complex with a variety of product types mixed together. Regrettably
perhaps, unlike the stock market, where the Dow is one of a handful of well-
recognized equity benchmarks on a global basis, a similarly recognized
benchmark for the bond market has not really yet come into its own.
Given the importance that relative return managers place on under-
standing how well their portfolios are matched to their benchmarks, fixed
income analytics have evolved to the point of slicing out the various factors
that can contribute to mismatching. These factors would include things like
mismatches to respective yield curve exposures in the portfolio versus the
benchmark, differing blends of credit quality, different weightings on pre-
payment risks, and so on. Not surprisingly, these same slices of potential mis-
matches are also the criteria used for performance attribution. “Performance
attribution” means an attempt to quantify what percentage of overall return
can be explained by such variables as the yield curve dynamic, security selec-
tion, changes in volatility, and so forth.
Regarding a quantitative measure of a benchmark in relation to port-
folio mismatching, sometimes the mismatch is normalized as a standard devi-
ation that is expressed in basis points. In this instance, a mismatch of 25 bps
(i.e., 25 bps of total return basis points) would suggest that with the assump-
tion of a normally distributed mismatch (an assumption that may be most
realistic for a longer-run scenario), there would be a 67 percent likelihood
that the year-end total return of the portfolio would come within plus or
minus 25 bps of the total return of the benchmark. The 67 percent likeli-
hood number simply stems from the properties of a normal distribution. To
this end, there would be a 95 percent likelihood that the year-end total return


of the portfolio would come within plus or minus 50 bps of the total return
of the benchmark and a 99 percent likelihood of plus or minus 75 bps.
Another way of thinking about the issue of outperforming an index is
in the context of the mismatch between the benchmark and the portfolio that
is created to follow or track (or even outperform) the benchmark. Sometimes
this “mismatch” may be called a tracking error or a performance tracking
measure. Simply put, the more a given portfolio looks like its respective
benchmark, the lower its mismatch will be.
For portfolio managers concerned primarily with matching a bench-
mark, mismatches would be rather small. Yet for portfolio managers con-
cerned with outperforming a benchmark, larger mismatches are common.
Far and away the single greatest driver of portfolio returns is the duration
decision. Indeed, this variable alone might account for as much as 80 to 90
percent of a portfolio™s return performance. We are not left with much lat-
itude to outperform once the duration decision is made, and especially once
we make other decisions pertaining to credit quality, prepayment risk, and
so forth.
In second place to duration in terms of return drivers is the way in which
a given sector is distributed. For example, a portfolio of corporate issues may
be duration-matched to a corporate index, but the portfolio distribution may
look bulleted (clustered around a single duration) or barbelled (clustered
around two duration values) while the index itself is actually laddered
(spread out evenly across multiple durations).
A relative value bond fund manager could actively use the following

Jump Outside the Index
One way to beat an index may be to buy an undervalued asset that is not
considered to be a part of the respective benchmark. For example, take
Mortgage-backed securities (MBSs) as an asset class. For various reasons,
most benchmark MBS indices do not include adjustable-rate mortgages
(ARMs). Yet ARMs are clearly relevant to the MBS asset class. Accordingly,
if a portfolio manager believes that ARMs will outperform relative to other
MBS products that are included in an MBS index, then the actual duration-
neutral outperformance of the ARMs will enhance the index™s overall
return. As another consideration, indexes typically do not include product
types created from the collateral that is a part of the index. For example,
Treasury STRIPS (Separately Traded Registered Interest and Principal
Securities) are created from Treasury collateral, and CMOs (Collateralized
Mortgage Obligations) are created from MBS collateral. Accordingly, if an

Financial Engineering

investor believes that a particular STRIPS or CMO may assist with out-
performing the benchmark because of its unique contributions to duration
and convexity or because it is undervalued in some way, then these prod-
ucts may be purchased. Treasuries are typically among the lowest-yielding
securities in the taxable fixed income marketplace, and a very large per-
centage of Treasuries have a maturity between one and five years. For this
reason, many investors will try to substitute Treasuries in this maturity sec-
tor with agency debentures or highly rated corporate securities that offer a
higher yield.

Product Mix
A related issue is the product mix of a portfolio relative to a benchmark.
For example, a corporate portfolio may have exposures to all the sectors
contained within the index (utilities, banks, industrials, etc.), but the per-
cent weighting actually assigned to each of those sectors may differ accord-
ing to how portfolio managers expect respective sectors to perform. Also
at issue would be the aggregate statistics of the portfolio versus its index
(including aggregate coupon, credit risk, cash flows/duration distribution,
yield, etc.).


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