本論文主要在探討損益表之重要揭露事項－－每股盈餘，面臨之種種問題。尤以企業 資本結構涵蓋淡化性證券時，如何計算及報導每股盈餘，更是學者間爭論之焦點。本 文乃試圖建立其計算準繩，俾供企業使用。 本文乙冊凡五章，約四萬言，第一章旨在闡明每股盈餘之意義、用途、應用上之限制 ，以及本文之研究範圍。第二卓旨在採討淡化性證券－－對每股盈餘有淡化效果之證 券。第三章旨在採討爭論頗多之準普通概念及次級地位之取決問題。第四章旨在評估 各科計算方法，進而建音計算準繩，共就我國現況提出檢討與建議。第五章結論。

吾人皆知, 每股盈余對投資人而言具有相當的重要性, 因為每股盈余可用來衡量股價 的高低, 以做為投資決策之參考。雖然我國證券市場目前正處於Agtmael 所謂的「投 機期」(Speculation),每股盈余對股價之衡量似乎不具價值, 但此乃一過渡現象, 將 來勢必歷經「整理( 崩盤 )期」(Consolidation(Crash)), 而達「成熟期」(Mature) 。屆時, 每股盈余之分析將是投資決策最有用的工具之一。 本研究之目的在於試圖為每股盈余發展一預測模型, 以便將來投資人做投資決策時之 參考。但為顧及投資人取得預測資訊之便, 乃利用我國企業公開說明書上所公布的16 個財務比率做分析, 試圖尋找較具解釋能力之比率建立模型。因此本研究之貢獻除了 幫助投資人做投資決策外, 尚可證明會計人員所編制的財務報表, 具有可用性。 本研究架構, 從問題之發生、認定, 并經過文獻之探討后, 決定以我國上市企業為樣 本, 并以公開說明書中有民國73年至75年之16個財務比率為準, 共取得110 家有完整 之資料。首先將此16個財務比率做主成份分析(Component Analysis), 抽取一組較少 數的財務比率來建立預測模型, 但為加強模型之預測能力, 另外加入總體經濟指數及 行業景氣指數於模型之中。待模型建立完成之后, 再以保留樣本(hold-out sample) 加以檢定該模型之預測能力。 本論文共分五章, 第一章緒論, 其中分四節, 分別就研究動機與目的, 研究的範圍與 限制, 并就預測的貢獻加以介紹; 第二章為文獻探討, 共分四節, 分別對多變量分析 , 時間序列模式, 比率模式及指數模式等文獻加以探討; 第三章為研究方法論, 分四 節加以敘述, 先對每股盈余加以定義, 再就財務比率加以介紹并予以設定, 最后初步 擬定預測模型; 第四章為實證分析, 亦分為四節, 其中對本研究所控制的外在變數及 樣本加以說明, 并就結果加以分析與驗證; 第五章則為本研究的結論及建議。 最后為求進一步推展本研究之模型, 乃將本研究之限制詳予說明, 寄望后續之研究者 能一一克服, 使本預測模型達到完美之境。

長期性資料(longitudinal data)，是指對同一個觀察個體(subject)或實驗單位(experiment unit)，在不同時間點上重複觀察或測量一個或多個變數。雖然觀察個體之間互相獨立，但就同一個個體而言，不同時間的觀察或測量常常是有相關性的。且觀察的個體之間可能由於一些無法測量的環境因素造成個體之間有差異，因此在傳統橫斷面分析中，假設其有相同迴歸係數的邊際模型可能不合理。隨機效果模型可以解決長期資料分析的相關，並假設每個個體的迴歸係數不同；此模型不但可以說明橫斷面資料的cohort效果，也可直接解釋長期資料的age效果；更可以區分個體之間與個體之內的變異。 本研究以1995年至2000年台灣11個產業中的100家公司之每股盈餘與各財務比率，作為實證分析的資料；分別配適每股盈餘與時間、產業別、時間產業別交互作用及財務比率及排除每股盈餘有異常值後之邊際效果模型(一般迴歸分析)及隨機效果模型，並比較其參數估計之異同。實證結果顯示，一般迴歸分析與假設誤差不相關且等變異下的隨機效果模型參數估計相似，但後者能區分變異為個體之間(between-subjects)與個體之內(within-subject)的變異。而假設誤差不相關且不等變異與假設誤差服從AR(1)且不等變異下的隨機效果模型估計相近。實證結果並顯示，在排除異常值後的模型參數估計，一般迴歸分析不論是估計值及顯著性大多沒有很大差別；而隨機效果模型的估計在排除異常值前後較有差別。特別是現金流量比率(CFR)原本為不顯著變數，在排除異常值後的模型配適全部變顯著性變數。 / The defining characteristic of a longitudinal study is that individuals are measured repeatedly through time. Although it is independent between subjects, the set of observations on one subject tends to be inter-correlated. Because there is some natural heterogeneity due to unmeasured factors between subjects, it is not corrected to assume they have the same regression coefficients. A random effect model is a reasonable description about the different regression coefficients, and it can resolve the inter-correlation of the observations on one subject. The major advantages of the random effect model are its capacity to separate what in the context of population studies are called cohort and age effects, and it can distinguish the variations between subjects and within subjects. This study describes the marginal model and random effect model, and shows their difference by real data analysis. We apply these models to the earnings per share (EPS) and other financial ratios of one hundred companies in Taiwan, which are distributed in eleven industries. The results show that the parameter estimates of the marginal model and random effect model are similar when error structure is independent and of equal variance. Furthermore, the latter can distinguish the variations between subjects and within subjects. However, the residual analysis reveals that the error structure may not be constant. Therefore, we consider heteroscedasticity error in random effect model. We also assume that error follows an autoregressive process (e.g. AR(1) model), which leads to the optimum among our results in terms of residual analysis. There are some observations that appear to be outlying from the majority of data. The results show little difference in the marginal models no matter whether those outliers are included. However, we obtain different results in the random effect models. Especially, the variable of “cash flow ratio” becomes significant once those potential outliers have been excluded, while it is not significant when all cases are fitted in the model.

每股盈餘是公司的重要財務資訊之一，它可以反應公司的經營績效，因此一方面可以提供給投資者作為投資決策之參考，另一方面提供給管理者作為管理評量的參考指標之一。過去在每股盈餘等財務預測往往以統計方法進行，因此在自變數選擇上常受到限制，同時有些預測模式其輸出結果往往只能以常長或衰退等二元式的結果表示。而另一方面，以類神經網路預測方式的預測模式可能因變數增加，使得網路變的較複雜。本研究嘗試以人工智慧中的遺傳演算法來作為預測的工具，發展財務預測模型，來預測每股盈餘，解決過去預測方式的限制或缺點。同時也將對過去的遺傳演算法稍做修正，並嘗試以實際值的編碼方式進行編碼，以符合需求。最後進一步比較遺傳演算法和其他預測方式，瞭解以遺傳演算法做於預測每股盈餘工具的特性及優缺點。 / Earnings per share (EPS) is one of the important financial indicators to a corporation. It reflects the operating performance of a corporation. On one hand, EPS provides information available to investors for decision making; on the other hand, it is an indexfor measurement of management. In the past, financial forecasting was often done by using statistical models. However, the input variables were limited by using these statistical models. Besides, some stastical models only provide dichotomy output ,such as either "grwoth" or"decline". The neural network forecasting model will be more of complexity, when the input variable increases. This research attempts to develop a financial forecasting model to forecast the EPS by using the Genetic Algorithms, which is a new topic of artificial intelligence. This model excludes both the limitations and disadvantages of the models mentioned above. Here, the genetic algorithms will be modified and the real number will be used to code as a gene of achromosome to meet the requirements of the finacial model. Finally,we compare the genetic algorithms financial forecasting model with the other ones in order to understand the features, advantages and disadvantages of genetic algorithms as being a financial forecasting tool .

民國91年1月2日啟動交易的「興櫃市場」，提供投資者一個合法、安全及透明的未上市（櫃）股票交易市場，而依台灣證券交易所(TSEC)上市審查準則及櫃檯買中心(OTC)上櫃審查準則之規定欲申請初次申請上市（櫃）股票之公開發行公司，必先登錄興櫃股票，並掛牌交易達六個月以上，先熟悉市場交易規則及充分透明的資訊揭露，進而才有資格申請成為上市櫃公司。然興櫃市場是否具有「價格發現」機制，又影響其申請上市櫃(IPOs)股票蜜月現象因素為何，係本研究之探討重點。 本研究主要探討興櫃公司初次申請上市（櫃）(IPOs)股票蜜月現象及其影響因素，研究目的在於「暸解檢驗興櫃公司從興櫃登錄至申請上市（櫃）（IPOs）期間是否存在超額報酬，並從公司基本因素、公司於興櫃市場表現因素、公司上市（櫃）時條件因素、市場景氣與制度因素與公司治理因素等五大類，瞭解其對於IPOs蜜月現象的影響。此外，並以實際登錄興櫃公司申請上市櫃進度，從興櫃股票在登錄興櫃日、上市（櫃）送件日、上市（櫃）審議委員會通過日等三個不同時點的兩個超額報酬，相對於上市（櫃）審議委員會通過日至上市（櫃）掛牌日這段期間之超額報酬的影響。另解析個案公司SWOT分析結果，探討個案公司相對於其所面對外部環境之機會與威脅，以及公司內部環境所具備的優勢與劣勢等相關影響因素。 本研究運用複迴歸分析，得到實證結果發現興櫃申請上市(櫃)公司： 1.「登錄興櫃日至上市（櫃）送件日之超額報酬」主要與產（行）業別、興櫃交易天數、登錄興櫃日至上市（櫃）送件日之大盤漲幅、會計師事務所聲譽等因素有關。 2.「上市（櫃）送件日至審議委員會通過日之超額報酬」受到公司成立年數、興櫃交易天數、稅後EPS、登錄興櫃日至上市（櫃）送件日之大盤漲幅、上市（櫃）送件日至審議委員會通過日之大盤漲幅的影響。 3.「審議委員會通過日至上市(櫃)掛牌日收盤價之超額報酬」與產（行）業別、興櫃個股日平均成交金額、興櫃交易天數、中籤率、承銷價、登錄興櫃日至上市（櫃）送件日之大盤漲幅、上市（櫃）送件日至審議委員會通過日之大盤漲幅、審議委員會通過日至掛牌日收盤價之大盤漲幅、首五日無漲跌幅限制、會計師事務所聲譽有關。 4.「議定承銷價至上市(櫃)掛牌日收盤價之超額報酬」則是受到公司成立年數、興櫃交易天數、承銷價、稅後EPS、首五日無漲跌幅限制等變數的影響。 除上述影響因素外，個案分析興櫃申請上市櫃(IPOs)公司可發現若該公司在產業皆具有領導地位、產品當紅有競爭利基（如ＩＣ設計等）具有國際競爭力及成長潛力、掌握關鍵研發創新技術、營收或獲利不斷成長、有較高毛利率及稅後每股盈餘、部份有富爸爸資源支持及垂直整合從與外資青睞，從登錄興櫃經審議委員會到上市（櫃）掛牌皆有超額報酬現象。 / Emerging Stock Market, which was started the transaction on January 2, 2002, provides the investors a legitimate, safe and transparent stock transaction market of unlisted company. According to the listed examination criterion of TSEC and OTC, an initial public offerings (IPOs) company has to be listed and transacted on Emerging Stock Market for over than six months to be familiar with the transaction rule of market and disclose the information of the company fully transparently. Then it has the admissibility of applying to transfer to listed market. This thesis mainly explores the honeymoon effect of IPOs stock and its influence factors. The five purposes including: 1. To examine whether there is exceed return when an emerging company transferring to an IPOs company. 2. To discuss the influence to the exceed return of IPOs by the five group factors, including the company fundamental, the performance in Emerging Market, the conditions of the company at IPOs, the market situations and systems, and the company governance. 3. To discuss the influence to the exceed return of IPOs by the two time period factors between the three time points: listed day in the Emerging Market, the apply day to TSEC and OTC, and the day the application approved by Examination Committee of TSEC or OTC. 4. To give SWOT analysis (combined with five forces analysis) in the case studies to explore, of the target companies, the opportunities and threats of the external environment and the strengths and weakness of the internal environment. 5. To apply the exploration in Purpose 1, Purpose 2, and Purpose 3 to the target companies in the case studies. By using multiple regression model, we found the following results: 1. The exceed return from the listed day in Emerging Market to the apply day to TSEC or OTC is related to the industry, the days staying in Emerging Stock Market, the performance of TSEC index, and the reputations of accountant business offices. 2. The exceed return from the day applying to TSEC or OTC to the day the application approved by Examination Committee is related to the company age, the days a company staying in Emerging Stock Market, the EPS after tax, and the performance of TSEC index. 3. The exceed return from the day the application passed by Examination Committee to the closing price at listing day is related to the industry, the average amount of transaction in Emerging Stock Market, the days a company staying in Emerging Stock Market, the transaction lot size, the stock offering price, the performance of TSEC index, the 7% price limit has been cancelled under the new underwriting system in five trading days after IPO, the reputations of accountant business offices. 4. The exceed return from the IPOs price to the closing price at listing day is related to the company age, the days a company staying in Emerging Stock Market, the stock offering price, the EPS after tax, the 7% price limit has been cancelled under the new underwriting system in five trading days after IPO. Besides the above factors, we found, from the case studies, that IPO companies have the exceed return when they possess: the leadership in the industry, the international competitiveness and growth potential of their niche products, the key creative technology of R&D, the continuation growing of revenue and profit, the higher profit rate and the EPS after tax, the support of the parent group, and the foreign capital investors.

本研究試圖瞭解投資人所關心的財務資訊，是否真能帶來超額報酬，亦即探討上市公司股票報酬率與各財務資訊之間的關係，包括每股盈餘成長率及益本比、市價淨值比、自由現金流量相關變數對長期股票報酬之影響，並嘗試對各變數給予經濟上的解釋。 　　其中並試圖找出財務資訊究竟是在何時反應於股票報酬與超額報酬上，因此將股票報酬與超額報酬以領先財務資訊一季、與財務資訊同季及落後財務資訊一季等三個時點加以衡量，並以表面無關迴歸模式(Seemingly Unrelated Regression model ; SUR)與混合橫斷面及縱斷面之迴歸模式進行統計分析。 　　結果發現益本比及每股盈餘成長率與股票報酬及超額報酬的關係並不顯著；市價淨值比與落後一季的股價報酬具有顯著的負相關，而與超額報酬不論衡量時點為何均存在有顯著正相關；自由現金流量與股價報酬及額報酬均無顯著關係；毛現金流量與經濟利益率與股價報酬在部分產業中具有顯著關係，而與當期超額股價報酬則存在顯著的關係，惟影響符號不一致。此外各自變數對於股票超額報酬的關連性較自變數與股票報酬間具有較顯著的關係。