| Summary: | 碩士 === 國立政治大學 === 資訊管理學系 === 84 === Financial Statements are very important information
indicating performance of corporations. Managers and investors
use financial ratios as vital indexes to evaluate and predict
operating results of corporations, and make their decisions.
ategy, and compute CAR for each investment strategies. At last,
I analyze the investing results of the four strategies for
individual industry. ANN ( Artificial Nerual Network) shoot a
new direction on researching application of abstracting
accounting information which can efficiently predict earnings.
According to results of relative researches, financial
statements from different industries present and implicate
different accounting information. If we further apply ANN on
financial statement information to predict earnings of
corporations, we can use the results as bases of analyses and
comparisons among industries. Because ANN model has many
advantages, in this research, I use financial statements and
return on stocks from corporations as researching samples to
construct prediction models and compute CAR(Cumulative Abnormal
Return) on investments. These samples are chosen from 15
different industries and covered from the first quarter of 1981
to the third quarter of 1993. This research consists of three
parts: 22 financial ratios selected by MANOVA First, I use the
general market samples to adjust and predict the vital
parameters of ANN models, such as the selection of input
variable, the number of hidden node, and finally pick better
setups for the prediction model. Second, I use this model to
train and test samples from the general market, the textile ,
and the electron industry, and research the variation of
predicting results by different models made up different
industries by means of evaluation indexes . Third, I use the
results predicted by the three different industry models, in
spect of risk and return, to define four types of investment
strategies -- "the general", "the high return", "the low risk",
and "the high return - low risk" strategy, and compute CAR for
each investment strategies. At last, I analyze the investing
results of the four strategies for individual industry. After
researching, I find:s of the textile and electron industry are
better than the general markets''. 1.The better setups of ANN
predition models are :industries are: (1)the selection of input
variable:the 22 financial ratios selected by MANOVA (2)the ANN
model topology(input node - hidden node - output node):22-22-1
rategy (3)the range of initial connection weights:-0.1~0.1
return - low risk strategy 2.The analyses of results predicted
by the three different industry models are: (1)the predicting
abilities of the textile and electron industry are better than
the general markets''. 3.The proper investment strategies of
individual industries are: (1)the general market:the general
and the low risk strategy (2)the textile industry:the high
return and the high return - low risk strategy (3)the electron
industry:the low risk and the high return - low risk strategy
|
|---|
