| Summary: | 碩士 === 大同大學 === 事業經營研究所 === 88 === Ever since the retrieval of Taiwan in 1945, an obvious and vivid industrial leap has been observed at a ten years’ cycle. It has become the driving force of Taiwan''s economic growth, created and contributed to the huge amount of the country’s foreign exchange reserve. After Taiwan’s mainstream industry reached the peak and started to decline, it came to people’s attention and concern that while suffering the gradually but definitely continuing recession, manufacturers now face the inevitable down-size or even a total shut -down of their operations should there not be any new territories or products being expanded or developed. This study, therefore, attempted to set up a financial crisis prediction system through financial ratio models to detect and prevent whatever financial crisis might occur.
Motherboard companies listed in Taiwan Stocks Exchange and TAISDAQ were selected to be the research samples, taking their financial data during 1993 to 1998 to be studied while from within adopting data during 1993 to 1997 as the expertiment set. To establish a complete prediction model, a 5-year (from 1993 to 1997) average of each variable was selected, and the financial data of 1988 was singled out as the control set to verify the model’s ratio accuracy. A total of 28 sampling financial data were taken into the study. 23 of them were applied for factor analysis and as the resource of the model designing for the evaluating of enterprises’financial performances. Then, statistic calculations o f Regression Analysis and Discriminant Analysis were conducted to obtain the model’s accuracy ratios. Thereby reaching the following conclusions during the research process:
1. For motherboard industry, the components of financial evaluating factors are: profit factors, turnover factors, operating factors and non-operating factors. The explanatory ability of the four factors to the model is 75.313%.
2. The components of Independent variables of the Regression Analysis model are: ROA, assets turnover rate, operating profit growth rate, and net non-operation ratio. The Regression Analysis equation is:
Y=48.1433+6.7354*Xa+2.4046*Xb-0.1539*Xc-0.3718*Xd
Y: Total Value
Xa: ROA
Xb: Total Asset Turnover Rate
Xc: Operation Profit Growth Rate
Xd: Net Non-Operational Ratio
Inputting original samples and contrast samples into the equation, accuracy ratios of 71.4% and 60.7% could be obtained respectively.
2. The component of Independent variables for Regression Analysis are:ROA, assets turnover rate, operating profit growth rate and net non-operation ratio. The Regression Equation is
Y=48.1433+6.7354*Xa+2.4046*Xb-0.1539*Xc-0.3718*Xd
Y: Total Value
Xa: ROA
Xb: Total Asset Turnover Rate
Xc: Operation Profit Growth Rate
Xd: Net Non-Operation Ratio
3. The component of Independent variables of Discriminant Analysis model are: ROA, assets turnover rate, operating profit growth rate, and net non-operating ratio. The Discriminant Analysis Equation is:
Z1=0.831534Xi+0.860127Xii-0.1895Xiii-0.26617Xiv
Z2=-0.91511 Xi+0.545814 Xii+0.68398 Xiii+0.08937 Xiv
Z3=0.054794 Xi-0.17553 Xii+0.932483 Xiii-0.00437 Xiv
Z1~Z3: Total Value
Xi: ROA
Xii: Total Asset Turnover Rate
Xiii: Operation Profit Growth Rate
Xiv: Net Non-Operation Ratio
Inputting original samples and contrast samples, accuracy ratios of 100% and 96.4% could be obtained respectively.
4. For the accuracy ratios of model estimation, the accuracy ratios of original and contrast samples were 71.4% and 60.7% respectively; in Regression Analysis, the accuracy ratios of original and contrast samples were 100% and 96.4% respectively. It was observed that the accuracy ratios obtained from Discriminant Analysis was higher than that of Regression Analysis.
5. Based on this study, general investors can refer to a company’s ROA, assets turnover rate, operating profit growth rate and net non-operation ratio to evaluate its operating efficiency.
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