Title: 應用基因法則最佳化類神經網路建立財務分析模式之研究
Applying the Genetic Optimal Neural Networks to Build Financial Analysis Model
Authors: 林逢煥
Forng Huan Lin
陳安斌;李慶恩
An Pin Chen;Ching En Lee
工業工程與管理學系
Keywords: 基因法則;類神經網路;最佳化類神經網路;財務分析;財務體質;Genetic Algorithms;Neural Networks;Optimal Neural Networks; Financial Analysis;Financial Health
Issue Date: 1994
Abstract: 本研究以類神經網路為基礎,結合同樣源自生物科學,普遍用作最佳佳化
工具的基因法則,建立一最佳化類神經網路財務分析模式,對台灣股票市
場進行基本面的財務報表分析。期望在類神經網路的最佳化下,徹底的揭
露財務報表所隱藏的重要資訊。本研究所提出的財務分析模式是根據上市
公司的財務報表,建立財務結構、償債能力、經營能力、獲利能力、成長
能力等五個財務項目的評分表。然後經由最佳化類神經網路的學習,掌握
評分表內的知識規則,繼而將上市公司實際的財務指標值輸入類神經網路
,以得到各上市公司在不同財務項目上的得分,並綜合各財務項目的得分
,評估上市公司財務體質的良窳。最後,進一步利用財務分析的結果預測
下一年度公司的每股盈餘。本研究並以電子業的十家上市公司為研究的對
象,以實際的財務資料說明如何以基因法則最佳化類神經網路進行財務分
析。而由研究的結果,則驗證了本研究所發展的財務分析模式的可行性。
The Study is applied the genetic optimal neural networks to
develop financial analysis model based on the company financial
statements in Taiwan Stock Market. It is expected to completely
disclose the important information hid in financial statements
by combining the power of genetic algorithms and neural
network. According to the financial statements, the financial
analy- sis model proposed by the study constructs five scoring
tables of financial items related to capital structure,
liquidity, operating perforemance, return on investment and
growth analysis respectively. After the knowledge-rules shown
in scoring tables through trianing optimal neural networks
being entirely captured , the data of financial ratio collected
in this study is input to obtain each company's score in each
financial item. Finally, each company's score is used to
evaluate company financial health and forecast the earning per
share next year. Ten electronic companies in Taiwan Stock
Market are selected to study as subjects. And the actual
financial data of those ten companies is utilized by the
genetic optimal neural networks to explain how to perform
financial analysis. From the results of this case study, the
financial analysis model proposed by the study is verified
which is feasible to evaluate company financial health.
URI: http://140.113.39.130/cdrfb3/record/nctu/#NT830030029
http://hdl.handle.net/11536/58793
Appears in Collections:Thesis