A Neural Network Approach for Prediction and Analysis in the Taiwan Stock Market --- Stock Price and TSEWPI

• Main Authors: Cheng, Chi-Wei, 鄭志偉
• Other Authors: Hung-Chung Lee
• Format: Others
• Language: zh-TW
• Published: 1997
• Online Access: http://ndltd.ncl.edu.tw/handle/84882368526384217481

碩士 === 淡江大學 === 資訊管理學系 === 85 === This study attempts to select the significant ones from various offinancial ratios and indicators about sale events from the viewpoint offundamental analysis . After interviewing with investment experts and referencing the results from related studies , some indicators withcorrelation were eliminated and a group of indicators was reserved . Back-Propagation Network (BPN) approach is used in this study. We collectretrospective data as BPN''s training samples and testing samples from four listed companies that are with similar industrial architectures . To prevent the noise from the effects of whole market and sector (consists of companies which have similar products) , we proposed amodule which is used to predict the relative trends between individual stock price and sector it belongs . Results indicate that the hit ratiosare about 59% in the individual stock module and about 67% in the sector-relative module on the end of the first month in which companiesopen their accounting information , and there are no obvious predictingcapacities after the second and the third month . This research also present a model that discusses the mutualrelations among GNP growth rate , interest rate and weighted EPR(Earning-Price Ratio) of whole listed companies on Taiwan Stock Exchange Company .We adopted neural network approach to identifying the forecasting - capacities of this model on long-term fluctuation of TSEWPI . The modelwe have proposed use retrospective date to discuss the relations betweenGNP growth rate and aggregate profits of whole listed company , the cyclic phenomenon within interest rate and PER of listed company , andtime-lagging effect . We hope this model can be applied on real world like TSEWPI trend prediction , for example , forecasting the peak and trough of TSEWPI curve .