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This is where the differences inherent in the underlying nancial instruments start to create differences in the analytics. When we analyze a xed income manager, we want to review the underlying portfolio holdings and make assessments based on individual positions, as well as speci c groupings. In that respect, there is no difference between equity and xed income manager analysis. However, equities and xed income securities have different investment characteristics and, as a result, need to be analyzed in different ways. As the table in Exhibit 14.4 illustrates, the real differences between equity and xed income manager analysis occur at the characteristics level. Like equities, we should analyze xed income portfolios based on a geographical, sector, and industry basis. Unlike equities, we are not concerned with things like P/E, P/B, and ROE. Those are mainly equity characteristics. When we review a xed income portfolio and look to analyze it based on discrete fundamental characteristic ranges, we need to focus on things like maturity, duration, and quality. Maturity. The maturity of a bond is the amount of time left until it matures. For example, assuming today s date is January 1, 2004, a bond with a maturity date of December 31, 2005, would have a maturity of

Where n is the number of digits of the maximum absolute value. For example, if the largest number is 9948 then n would be 4. 9948 would normalize to 9948/104, 9948/10,000, or 0.9948. The normalization process is illustrated using the data in Table 3.4. To calculate the normalized values using the min-max equation, rst the minimum and maximum values should be identi ed: OriginalMin 7 and OriginalMax 53.

your knowledge of the data may allow you to create multiple but simpler models. For example, a project to model factors that contribute to the price of real estate may use a data set of nationwide house prices and associated factors. However, your knowledge of the real estate market suggests that factors contributing to house prices are contingent on the area. Factors that contribute to house prices in coastal locations are different from factors that contribute to house prices in the mountains. It may make sense, in this situation, to divide the data into smaller sets based on location and to model these locales separately. When doing this type of subsetting, it is important to note the criteria you are using to subset the data. These criteria will be needed when data to be predicted is presented for modeling by assigning the data to one or more models. In situations where multiple predictions are generated for the same unknown observation, a method for consolidating these predictions will be required. This topic will be discussed further in the chapter on prediction.

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price. A short position in interest rate futures would similarly hedge the portfolio against changes in the convertible bond s value due to changes in interest rates. The Staples example omits items such as transaction costs and focuses on only one security at one point in time. In a broader context, and accounting for transaction costs and other factors, a convertible hedging strategy should yield no excess returns if markets are ef cient. If markets are ef cient, all assets are fairly priced and there are no arbitrage opportunities offering abnormal returns.

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Ribisl, K M, Walton, M A, Mowbray, C T, Luke, D A, Davidson II, W S and Bootsmiller, B J (1996) Minimizing participant attrition in panel studies through the use of effective retention and tracking strategies: review and recommendations Evaluation and Program Planning, 19, 1 25 Schrapler, J P (2002) Respondent behavior in panel studies a case study for income nonresponse by means of the German Socio Economic Panel (GSOEP) Paper presented at the 5th International Conference of German Socio Economic Panel Users, July 3 4, Berlin Serfling, O (2004) The interaction between unit and item nonresponse in view of the reverse cooperation continuum: evidence from the German Socioeconomic Panel (GSOEP) Paper presented at the 6th International Conference of German Socio Economic Panel Users, June 24 26, Berlin Spie , M and Kroh, M (2004).

AIMR S PERFORMANCE PRESENTATION STANDARDS (PPS)

Note: All trade summaries include $100 round-turn trade deductions for slippage and commissions. 2004 CQG, Inc. All rights reserved worldwide.