TAILIEUCHUNG - Knowledge-Based Approaches for Evaluating Municipal Bonds

Municipal bond analysis requires a sophisticated mix of quantitative and qualitative techniques for trading and management activities. Even though much of the quantitative component is automated and enriched by a tremendous amount of data, much of the critical buying and selling decisions are based on individualized, qualitative judgments. The actual use of formal quantitative portfolio models vary from institution to institution. At one extreme, typified by rigorous investment constraints, the model recommendations are followed precisely. In most cases, however, the model recommendations are just — recommendations. The model outputs, based on information available to it, are “guidelines.” All decisions relies on the judgment, experience and intuition of the. | To Appear in The Handbook of Municipal Bonds Probus Publishing Chicago 1994. pp. 441-450. Knowledge-Based Approaches for Evaluating Municipal Bonds Roy S. Freedman William P. Stahl Introduction Municipal bond analysis requires a sophisticated mix of quantitative and qualitative techniques for trading and management activities. Even though much of the quantitative component is automated and enriched by a tremendous amount of data much of the critical buying and selling decisions are based on individualized qualitative judgments. The actual use of formal quantitative portfolio models vary from institution to institution. At one extreme typified by rigorous investment constraints the model recommendations are followed precisely. In most cases however the model recommendations are just recommendations. The model outputs based on information available to it are guidelines. All decisions relies on the judgment experience and intuition of the analyst or portfolio manager. To be successful a municipal bond portfolio manager must maximize return and minimize risk in a dynamic world. Events that can trigger a portfolio rebalancing include i changes in the yield curve ii changes in trading relationships such as beta and duration iii changes in credit quality. Of these three events events involving credit quality are less quantitative and involve subjective knowledge. The first two are more quantitative and more amenable to successful models and practical computer assistance. When concerned with numerical quantities like yield curves and duration mathematical models perform better than human judgment. On the other hand judgment must be better since in most cases the quantitative model outputs are guidelines to final decisions. Just because a process is qualitative does not mean that a model and computer system cannot be built to improve that process. It is possible to build computer-based models that improve the qualitative decision-making component as well as the quantitative

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