完整後設資料紀錄
DC 欄位語言
dc.contributor.author王華頤en_US
dc.contributor.authorWang, Hwa-Yeeen_US
dc.contributor.author陳安斌en_US
dc.contributor.authorAn-Pin Chenen_US
dc.date.accessioned2014-12-12T02:17:20Z-
dc.date.available2014-12-12T02:17:20Z-
dc.date.issued1996en_US
dc.identifier.urihttp://140.113.39.130/cdrfb3/record/nctu/#NT850396013en_US
dc.identifier.urihttp://hdl.handle.net/11536/61843-
dc.description.abstract在證券市場中,股價的變動對於投資者或投機者而言,是他們所最想掌握 的分析點。而股價的變動卻又與公司的獲利能力與經營方針有著重大的關 連性。因此,若能進一步掌握並運用個別產業之財務報表以預測未來公司 經營的績效,將更能夠提供一個就各產業間預測結果上進行分析與比較的 基礎。本研究運用類神經網路,來掌握重要財務指標變動率的行為模式, 並以重要的財務指標變動率為因子,以驗證股價的變動是否領先於財務報 表公佈之前。同時本研究也以系統整合之觀點,運用改良式主從架構資料 庫與網際網路的資訊技術,將資料庫與上述所得財務指標變動率的行為模 式進行結合,使可運用於網際網路之中。以供大眾運用此財務股價分析系 統,查詢自己所需要的財務資訊。本研究結果發現:1.以本月的財務變動 率學習本月股價變動率的方式,預測股價漲跌的準確率最高,而用本月的 財務變動率對下個月股價漲跌作預測的準確率最低。此結果告訴我們,財 務資訊公佈後,對於股價並無太大的關連性,也就是說,股價的漲跌已經 在財務資訊公佈前,就有先行反應的情行產生。2.對於一個異動作業頻繁 ,管理複雜的主從架構的資料庫環境中,利用散播者、發行者、訂閱者的 改良式主從架構環境,不但可降低許多中心資料庫的負荷,增進整個系統 的績效,同時對於系統的轉換與資料的交換,更可提供方便而又簡易的方 法。 To the investors and speculators in the uncertain stock market, the most they desire to know is the analysis point of stock volatility. However, the volatility is critically related to profits and strategies of companies they invested. Therefore, if we can further hold and make use of the financial reports of individual industry to predict the future operation performance which will become the basis of comparison and analysis.This research is based on the neural network to control the behavior mode of volatility rate under important financial index, and use the volatility rate of financial index as a factor to verify if the volatility is ahead of the financial reports announced.At the same time, the research is also from the viewpoint of system integration to combine the database with the outcome of behavior mode described as above and apply to the internet under the modified infrastructure of client-sever database and the World- Wide-Web for the public to operate this analysis system and access what they need.We attribute two consequences after research 1、By the way of financial volatility rate this month to learn the stock volatility rate this month, the hit rate is the highest in predicting stock volatility. However, on the other side, we get the lowest hit rate by this month to predict next month. This result shows there is not much relativity between share price and financial information after financial reports announced. In other words, share prices have fluctuated before financial reports announced.2、For the complex and fast- updating environment under the client-server database system, we use the communicators and publishers and subscribers to modify the client-server infrastructure, not only to reduce the load of core database, but also enhance the performance of system, moreover, we can provide the more convenient and easier way for system conversion and data exchange.zh_TW
dc.language.isozh_TWen_US
dc.subject類神經網路zh_TW
dc.subject主從架構zh_TW
dc.subjectNeural Networken_US
dc.subjectClient-Serveren_US
dc.title在主從架構下運用類神經網路於財務變動率對股價漲跌之分析zh_TW
dc.titleThe Analysis of The Financial Volatility Against Stock Price Based on Neural Network Under Client-Sever Infrastructureen_US
dc.typeThesisen_US
dc.contributor.department資訊管理研究所zh_TW
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