X hits on this document

PDF document

DFA Insurance Company Case Study, Part I: - page 8 / 40





8 / 40

In the case of the potential acquisition of DFAIC, the goals and objectives for the analysis would be set by Falcon's board and senior management team5. As such, Falcon's objective is to undertake strategies that will maximize the economic value of the company at the end of a five-year time horizon.

The five-year horizon was chosen since it is consistent with Falcon senior management's business planning horizon and it allows them to benefit from time diversification. This also gives Falcon a competitive advantage over those companies that are forced to operate on a year-to-year basis due to shareholder, regulatory or company-imposed constraints.

Extending the time hodzon beyond the company's planning horizon increases the risk that business does not develop as planned, and can thus reduce the effectiveness of the analysis. Risk is defined as the standard deviation of economic value, as Falcon management believes that this is an indicator of true economic risk.

One criticism of economic value as an insurance company objective is that it does not reflect statutory or regulatory constraints. Further, it is not part of the required annual financial reporting of insurance companies and is therefore not standardized or completely understood. Thus, a second risk measure, which was treated as a constraint, dealt with the reality of statutory reporting and regulatory oversight. This was reflected in the calculation of the probability of the statutory surplus falling below a minimum threshold.

Step 2: Data Collection

Data collection and evaluation is a time-consuming but important part of DFA. Since DFA deals with all financial aspects of insurance company operations the data requirements can be significant~. Published financial information, similar to data used in this case study, is readily available from organizations such as A.M. Best, One Source, shareholder reports, the SEC and numerous other sources. These sources streamline the data gathering and data entry required to feed (parameterize) DFA models. However, analyses based solely upon public databases and published financial information risk misinterpretation of events, trends, and on-going company operations. As such, DFA studies limited to public data are sub-optimal and if not carefully implemented and documented, can lead to inappropriate conclusions.

In this section, we discuss some of the.problems of using public data for the DFAIC case study. However, we must be careful to separate the real pitfalls of public data from the

s Since no specific guidelines for measuring the effectiveness or efficiency of the strategic initiatives have bean communicated in the instructions to the case study, we were free to elect measures that were in accordance with the holisti¢ nature of our newly capitalized holding company.

eThe data collection for DFAIC was made simple in that it was completely provided by the CAS with instructions that no additional information would be made available. In the case of DFAIC only a smell portion of the plethoraof publicly available data was provided.


Document info
Document views127
Page views127
Page last viewedThu Jan 19 19:42:30 UTC 2017