應用機率神經網路與二元序列演算法建構財務預警模型 - 台灣電子業為例

Using Probabilistic Neural Networks and Binary Sequence Algorithm to Build Financial Prediction Models - A Case of the Electronic Industry in Taiwan

連崔立(Chi-Li Lian)

指導教授 ： 羅淑娟

國立臺北科技大學/管理學院/工業工程與管理研究所/碩士(2008年)

摘要

本研究試圖運用機率神經網路(PNN)結合二元序列演算法(BSA)建構財務預警模型，以上市公司為研究對象，取用三個年度的公司財務資料。而建構此財務預警模型之主要目的，在於提早發現企業內部潛在的財務危機，藉此提供投資人及電子產業一個參考警訊。
本研究分為兩階段建構模型，第一階段是利用兩種不同資料型態及四種不同期間的資料來建構財務分類模型，並選出最佳模型來產生分類值。第二階段是透過二元序列演算法從這些分類值中產生預測樣式，經由這些預測樣式進行預測。經實證結果來看，藉由適當預測樣式，能提供較佳的預測結果。

關鍵字

機率神經網路 ； 二元序列演算法 ； 財務預警模型

並列摘要

This research attempts to use probabilistic neural networks(PNN) and binary sequence algorithm(BSA) to build financial prediction models, regard listed company as the research object, take three annual financial materials of company. The main purpose to build this financial prediction models, lie in finding the potential financial crisis inside enterprises ahead of time, offer investors and electronic industry one to consult alert news by this.
This research is divided into two stages and built the model, the first stage is to use two kinds of data type and four kinds of period to build financial classification model, elect the best model to produce the classifying value, The second stage is to rise from these classifying value prediction pattern through BSA, predicting via these prediction patterns. Looked by the real example result, with appropriate prediction pattern, can offer better prediction result.

並列關鍵字

Probabilistic Neural Networks ； Binary Sequence Algorithm ； Financial Prediction Models

參考文獻

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