貝氏網路於輔助盈餘預估分析之研究

Earnings Analysis Using Bayesian Network

蘇祐萱(Yu-hsuan Su)

指導教授 ： 盧以詮

元智大學/資訊學院/資訊管理學系/碩士(2000年)

摘要

近來不少研究提出貝氏網路研究方法，結合資料與事前知識所建立的貝氏網路架構圖，不僅具有預測能力，並可以推論變數之間的因果關係，特別是在財務金融領域的應用上，貝氏網路能夠輔助分析師預測市場未來走向、解釋市場的主要影響因素、進而協助分析師做出投資決策，據此，本研究提出以貝式網路為基礎，透過基本分析理論探討盈餘變動的影響因素，藉由貝氏網路架構呈現財務指標與每股盈餘的因果關係，並且經由模式推論結果解釋機率的不確定性，經由本研究實證結果，財務指標所建立的貝氏網路預測模型具有近八成的預測能力。

關鍵字

貝氏網路 ； 基本分析

並列摘要

Recently, several approaches have been proposed for learning Bayesian networks. These methods combine prior knowledge with data to produce one or more Bayesian networks. The resulting networks can be used for prediction, or, in special cases, to infer causal relationships among variables. In particular, in financial market applications, the resulting model will be able to help analysts predict future market direction, understand critical market parameters, and make investment decisions. By using Bayesian network techniques, this thesis aims to discover network representations for analysis earnings from the viewpoint of fundamental analysis. Based on Bayesian network learning algorithms, the causes (financial ratios) and effect (earning per share) network representations are generated for encoding uncertain beliefs and drawing inferences from such representations. The empirical results show that the accuracy is about 80%.

並列關鍵字

Bayesian network ； Fundamental analysis

參考文獻

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