| Summary: | 碩士 === 大同大學 === 資訊經營學系(所) === 92 === Enterprise operation and social economic development are closely linked, once enterprise encountered with crisis and going to close down, not only the society will be disturbed and investors will have great loss. For example, Asia financial storm did impact Taiwan economic, and the follow-up business crises and bankruptcy events revealed that many enterprises were on the edge of financial crises due to recession and result in investors extremely loss in rights and interests. We know the investment markets are full of traps, and a wrong decision could lead to serious loss; therefore, main purpose of the investment risk prediction model in this research is hoping to establish one financial crisis prediction system by statistic method, and detect potential business operation crisis before the occurrence of financial crisis.
Samples of this research are the banks that are rated by Taiwan Ratings; in addition to eliminate the companies that do not have four years ratings and with incomplete company financial statements, and also refer to Taiwan Stock Exchange Corporation’s (TSEC) data to select 20 companies with complete information as final analysis samples. Furthermore, the experimental group’s samples are obtained from the research period of 2000 to 2002, and use to establish the total prediction model; and the control group’s samples are gathered from 2003, and use to verify model’s accuracy. There are 20 samples for research. Apply 15 financial ratios to make multiple stepwise regression analysis and extract 3 indicators with more influence to make statistical computing, such as discriminate analysis and multinomial logit regression, to get model’s accuracy. Moreover, compare the discriminant analysis and multinominal logit regression’s discriminate ratio, and use better Regression Model of business financial performance to design the decision-making assistant system of PDA. Conclusions formed from the research process are as below:
First, through multiple stepwise regressions analysis to extract three significant financial variables that could predict company ratings from the original fifteen financial indicator variables, and these three variables are liquidity reserve ratio, ratio of interest revenue to average loan and ratio of return on stockholders’ equity.
Second, the independent variables, which construct discriminant analysis model, are liquidity reserve ratio, ratio of interest revenue to average loan and ratio of return on stockholders’ equity, and its Fisher’s linear discriminate function is:
Discriminate function of company with good rating (good financial structure):
Y1= -16.96+0.181X4+4.123X8-0.133X11
Discriminate function of company with good average rating (Stable financial structure):
Y2= -20.688+0.134X4+4.852X8-0.197X11
Discriminate function of company with good bad rating (Unstable financial structure):
Y3= -21.723+0.09759X4+5.062X8-0.215X11
X4= liquidity reserve ratio
X8= ratio of interest revenue to average loan
X11= ratio of return on stockholders’ equity
Accuracy ratios of 60.0% and 65.0% could be obtained after substation experimental group and control group’s sample values.
Third, the independent variables, which construct Multinomial Logit Regression Model, are liquidity reserve ratio, ratio of interest revenue to average loan and ratio of return on stockholders’ equity; and the accuracy ratios of 68.3% and 75.0% could be obtained after substitution original sample and control group’s sample values to predict company financial structure’s Multinomial Logit Regression empirical model.
Forth, in terms of model prediction’s accuracy ratio, discriminant analysis’ accuracy ratios in original sample and control group are 60.0% and 65.0% spectively; and Multinomial Logit Regression’s accuracy rations in original sample and control group sample are 68.3% and 75.0%, respectively; the obtained accuracy ratios of the Multinomial Logit Regression are much higher than discriminant analysis’.
Fifth, the establishment of system is to use historical data as Multinomial Logit Regression’s sample, and through Multinomial Logit Regression to get optimized model to be system’s framework. Moreover, adopts eMbedded Visual Basic 3.0 of eMbedded Visual Tools 3.0 to develop program on Windows CE. While use the system, general investors could input liquidity reserve ratio, ratio of interest revenue to average loan and ratio of return on stockholders’ equity, and the system will evaluate these three simple financial ratios to predict company’s future operation performance.
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