No description available.

本研究以Ohlson (1995) model為發展基礎，並將盈餘定義為（１）剩餘淨利及（２）盈餘水準，分別就此二種不同定義下的盈餘，以年度盈餘時間序列來測試其是否符合線性資訊動態假設，及針對各模式對股權價值估計與預測之結果做比較，探討何種評價方法或模式對於估計真實價值、解釋價格及預測報酬之效果較佳。 研究結果發現，我國上市公司之剩餘淨利及盈餘水準時間序列皆符合線性資訊動態假說。剩餘淨利線性資訊動態模型較能正確預估次一期的盈餘。相較於單獨以帳面價值來估計股價，考慮線性資訊動態模型所建立之各評價模型所預測之估計股價皆未能正確預測權益價值及解釋權益價值之波動。在投資策略方面，因剩餘淨利模型之投資績效最為穩定，在該模型之投資策略下，V/P比率愈低（高）之投資組合獲得之平均股票報酬愈低（高），代表股價愈是被低估的投資組合可賺取更多之投資報酬，這說明剩餘淨利模型預測次期投資報酬之能力最佳，投資人可以其做為建立投資策略的參考。 / Based on Ohlson (1995) model, this study specifies earnings variables as both residual income and earnings levels to test the linear information dynamic (LID) models per se and the ability of competing valuation models to value the contemporaneous stock prices. A comparison of future stock return predicting capability of competing models is also conducted. By using both residual income time series and earnings levels time series for examining the issue on the firms listed on the Taiwan Stock Exchange (TSE), the empirical results support Ohlson’s information dynamics. However, when estimated as a time series, the linear information models using either residual income or earnings levels variables provide value estimates no better than book value does alone. From the investment strategy aspect, the superior predictive ability of the residual income valuation model with respect to future stock returns demonstrates that high (low) V/P ratios gets high (low) investment returns. It implies that the underpriced portfolio makes high investment returns. Accordingly, the residual income valuation is good for estimating returns on the following year and is therefore a valuable investment reference..

研究指出，在不同人類個體的DNA序列中，只有0.1%的基因組排列是相異的，而其餘的序列則是相同的。這些相異的基因組排列則被稱為單一核苷酸(SNP)。Affymetrix公司發展出一種DNA晶片技術稱之為Affymetrix GeneChip Mapping 100K SNP set，此晶片可用來決定單一核苷酸資料的基因類型(genotype)。Affymetrix公司採用預設「動態模型演算法」(DM)來決定基因型態。本論文的研究目的是探討與示範對於DM方法中預設的S值的四種修正方式。而這四種修正的方法分別是： (1) Standardized L value，(2) Median-polished L value，(3) Median-center L value，和(4) Median-standardized L value。為了比較S值與四種改進方法，本研究藉由SNP的模擬資料來進行比較。資料的模擬是基於利用改寫過的階層式之Bolstad模型（2004），而模擬模型的參數估計是利用華人細胞株及基因資料庫中95位台灣人的100K SNP資料。根據AA模型與AB模型模擬資料的基因型態正確率，Standardized L value是最好的判斷基因型態之方法。在另一方面，因為DM方法並不是設計來決定Null模型的基因型態，因此對於Null模型模擬資料的基因型態判斷會有問題。關於Null模型的基因型態判斷，本論文提供了一些簡短的討論與建議。然而，依然需要進一步的研究探討。 / It is known there is only 0.1% in the DNA sequences that is different among human beings, and the rest of them are the same. These differences in DNA sequences are defined as SNPs (Single Nucleotide Polymorphism). The Affymetrix, Inc. had developed a DNA chip technology called Affymetrix GeneChip Mapping 100K SNP set for SNP data used to determine the genotype call. The default algorithm applied by Affymetrix, Inc. to decide genotype calls is the Dynamic Model-based (DM) algorithm. This study aimed to investigate and demonstrate four different ways to modify the basic component used in DM algorithm, namely, the S value. These four modified methods include: (1) Standardized L value, (2) Median-polished L value, (3) Median-centered L value, and (4) Median-standardized L value. In order to compare the S value with the four modified L values, a simulation study was conducted. A hierarchical version of Bolstad’s model (2004) was adopted to simulate the SNP Data. The parameters for the simulation model were estimated based on 95 Taiwanese 100K SNPs data from Taiwan Han Chinese Cell and Genome bank. The Standardized L value was proven to be the best method based on the accuracy of the genotype calls determined according to the simulated data of AA model and AB model. On the other hand, the genotype call for simulated data under Null model is problematic since the DM approach is not designed to determine the Null model. We have given some brief discussion and remarks of the genotype call for Null model. However, further research is still needed. /

銀行承載許多公司借款、各式擔保貸款及各式信用貸款等，使金融機構面臨龐大各式信用風險問題。在新版巴塞爾資本協定針對信用風險之計算方法做了重大修正，其中信用衍生性商品已具有信用風險抵減之功能。故本研究將針對一籃子信用標的針對信用結構式商品中具有量身訂作的單一分券信用違約交換與單一分券擔保債權憑進行更深入之研究並使用加入Vasicek Model特例Ornstein-Uhlenbeck process表示違約強度之隨機動態過程利用類似風險性債券之概念求得出封閉解以替代存活函數，來為簡化起見在無風險利率假設為一固定常數使用Copula方法評價單一分券信用違約交換與單一分券擔保債權憑。 　　在數值模擬部分，本篇利用實際市場資料建構出一合成單一分券擔保債權憑證產品，先針對違約動態模型與Copula函數之相關參數以實際市場資料做計與校正，再以評價公式以計算出合理信用價差，其結果可知當Copula函數越能描繪具有信用違約相關之信用違約事件，則當發生信用標的資產先後違約聚集情形會越高，以本研究實際產品資料特性而言Clayton Copula最能表現出違維聚集之情形，但在反應在第一次發生違約的權益分券上反而沒有其他兩種Copula函數用蒙地卡羅法所模擬出之違約次數高反而更低，做所求出來的信用價差也相對來的低，反而在反應違約聚集部分的先償違約交換具有較高信用價差。而在VaR值之衡量上可能因信用標的資產比較少，並沒有明顯之差異。