P2.T6.706. Heuristic approach versus neural networks (De Laurentis)

Nicole Seaman

Director of CFA & FRM Operations
Staff member
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Learning objectives: Describe the use of a cash flow simulation model in assigning rating and default probability, and explain the limitations of the model. Describe the application of heuristic approaches, numeric approaches, and artificial neural networks in modeling default risk and define their strengths and weaknesses. Describe the role and management of qualitative information in assessing probability of default.

Questions:

706.1. The new credit department of your firm is evaluating three different methodological approaches to the assignment of credit ratings: structural, reduced form, and cash flow simulation. In regard to these approaches, each of the following considerations is true EXCEPT which is false?

a. The least expensive model to build and maintain is the cash flow simulation
b. Cash flow analysis is effective in rating companies whose track records are meaningless or non-existent
c. Structural approaches are often applied to listed companies because the required input data is available
d. Structural approaches tend to be more objective and homogeneous than reduced form approaches


706.2. If we want to compare a heuristic approach to a numerical approach for the purpose of developing internal credit ratings, each of the following is a true statement according to De Laurentis EXCEPT which is not accurate?

a. A key risk in the application of neural networks is the model risk of over-fitting
b. Neither heuristic nor neural networks are well-suited to fuzzy logic and fuzzy environments
c. A major limit of neural networks is that we have to accept results from a so-called black box
d. From the perspective of credit rating assignment, heuristic approaches have the advantage of giving order, objectivity, and discipline to the rating process


706.3. In regard to the role and management of QUALITATIVE information in the development of internal credit ratings, which of the following pieces of advice does De Laurentis give?

a. Try to avoid qualitative information
b. Binary variables are better than nominal and ordinal variables
c. A good collection survey contains several open-ended questions
d. Gather only qualitative information that is not collectible in quantitative terms.

(Source: Giacomo De Laurentis, Renato Maino, and Luca Molteni, Developing, Validating and Using Internal Ratings (West Sussex, United Kingdom: John Wiley & Sons, 2010))

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