The purpose of this study was to examine whether structural change modeling procedures improved the predictive ability of quarterly earnings-per-share across industries. The structural change detection methods derived by Chen (1984), Chang, Tiao, and Chen (1988), Chen and Tiao (1990) and Lee and Chen (1990) were employed in order to determine the time points when structural change occurred. These interventions were then included as intervention transfer functions in ARIMA models. / The structural change models generally outperformed their non-structural change counterparts. However, this finding did not hold for all 9 industry definitions. Value Line was superior to the structural change models on a full-sample basis. However, in several industries there were no significant differences among Value Line and the structural change models. / This research also examined the predictive ability of composite forecasting models. Non-structural change as well as structural change composite models were constructed. In general, the non-structural change equally-weighted composites were not superior to financial analysts. In addition, the structural change composites outperformed the non-structural change composites for some models. / This study has shown that structural change modeling procedures offer improvements in the prediction of quarterly earnings-per-share. This result may be viewed as an initial step in examining many future research issues. These issues include the differential forecasting abilities of analysts versus statistical models, the variables which partition these aforementioned differential forecasting abilities, the linkages of management decisions (or random events) with a firm's earnings structure, and others. / Source: Dissertation Abstracts International, Volume: 55-07, Section: A, page: 2040. / Major Professor: Kenneth S. Lorek. / Thesis (Ph.D.)--The Florida State University, 1994.
| Identifer | oai:union.ndltd.org:fsu.edu/oai:fsu.digital.flvc.org:fsu_77194 |
| Contributors | Wilder, Wallace Mark., Florida State University |
| Source Sets | Florida State University |
| Language | English |
| Detected Language | English |
| Type | Text |
| Format | 298 p. |
| Rights | On campus use only. |
| Relation | Dissertation Abstracts International |
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