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CROSBY: Financial data interpretation as model-based diagnosis

CROSBY: Financial data interpretation as model-based diagnosis Analyzing the recent financial history of a firm is an important step in the annual audit cycle and in many other financial reasoning tasks. Auditors and other analysts performing this task have available to them a variety of mathematical models to fit to a set of historical and current period financial statement data. They also have expectations about the likelihoods and magnitudes of errors in that current period financial statement. Given these models and expectations, they must find the combination of models and statement errors that is most consistent with the numbers in the current period statement. A crucial difficulty in this task is in keeping track of the various models being tried and coping with the multiplicity of consistent combinations. This is a hard and general problem, and an important one in any uncertain environment in which human analysts must “juggle” many incompatible possibilities to arrive at a consistent view of the world. The program discussed in this paper prototypes a solution mechanism with potential to deal with this class of problems. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Mathematics and Artificial Intelligence Springer Journals

CROSBY: Financial data interpretation as model-based diagnosis

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References (14)

Publisher
Springer Journals
Copyright
Copyright
Subject
Computer Science; Artificial Intelligence; Mathematics, general; Computer Science, general; Complex Systems
ISSN
1012-2443
eISSN
1573-7470
DOI
10.1007/BF01530757
Publisher site
See Article on Publisher Site

Abstract

Analyzing the recent financial history of a firm is an important step in the annual audit cycle and in many other financial reasoning tasks. Auditors and other analysts performing this task have available to them a variety of mathematical models to fit to a set of historical and current period financial statement data. They also have expectations about the likelihoods and magnitudes of errors in that current period financial statement. Given these models and expectations, they must find the combination of models and statement errors that is most consistent with the numbers in the current period statement. A crucial difficulty in this task is in keeping track of the various models being tried and coping with the multiplicity of consistent combinations. This is a hard and general problem, and an important one in any uncertain environment in which human analysts must “juggle” many incompatible possibilities to arrive at a consistent view of the world. The program discussed in this paper prototypes a solution mechanism with potential to deal with this class of problems.

Journal

Annals of Mathematics and Artificial IntelligenceSpringer Journals

Published: Apr 5, 2005

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