Made available in DSpace on 2016-04-25T16:45:00Z (GMT). No. of bitstreams: 1 Adriano Mussa.pdf: 6575661 bytes, checksum: 672c8d0cf64d7127afdc1dcc22afb0ff (MD5) Previous issue date: 2007-11-28 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior / The purpose of this dissertation is to test the four-factor pricing model on the Brazilian stock market. This model is the Fama & French´s tree-factor pricing model augmented by a momentum factor. So, the four factors are: the market, as defined by the CAPM; the firm size, defined by the market value of equity; the book-to-market ratio, the relation between a company s book and market value of equity; and the momentum, defined by the stocks past return. The employed test methodology was the same used by Fama & French (1993). The database was composed by all stocks listed on the Bolsa de Valores de São Paulo BOVESPA, from 1995 to 2007. The significance of the model and of each factor was tested observing the adjusted determination coefficient, Adj. R2, of the temporal regressions and the t-Student statistics. The results indicated that the four-factor pricing model is valid for use on the Brazilian stock market, and is superior to the tree-factor pricing model and to the CAPM. The importance of each factor changes according to the portfolio characteristics. / O objetivo da presente dissertação é investigar a validade do modelo de precificação de ativos dos quatro fatores, no mercado acionário brasileiro. Este modelo é definido pela adição do fator de risco momento ao modelo dos três fatores de Fama e French. Desta forma, os quatro fatores são: o mercado, conforme indicado pelo CAPM; o tamanho da empresa, definido pelo valor de mercado do patrimônio líquido; o índice book-to-market ou B/M, definido pela relação entre o valor contábil e de mercado do patrimônio líquido; e o momento, definido pelo desempenho acumulado dos retornos das ações. A metodologia utilizada foi a mesma adotada por Fama e French (1993). Foram usadas as ações listadas na Bolsa de Valores do Estado de São Paulo - BOVESPA, no período de 1995 a 2007. Testou-se a significância de cada fator utilizando a estatística t de Student. A validade do modelo foi testada por meio da análise dos coeficientes de determinação ajustados, Adj. R2, das regressões temporais. Os resultados verificados apresentaram evidências de que o modelo dos quatro fatores é válido para o mercado acionário brasileiro, sendo superior ao modelo dos três fatores, e também ao CAPM, na explicação das variações dos retornos das ações da amostra. A relevância de cada fator de risco variou de acordo com as características das carteiras

The most widely used Cost of Capital model is the Capital Asset Pricing Model. The Beta, Which is a key input into the model has proven to be unreliable and provides no correlation with systematic risk. As risk increases, so should the cost of capital of the firm. The Beta is a historic measure of risk and does not capture the future outlook of risk. The future of an organisation and its risk may look very different to the past and therefore the need to calculate the Cost of Capital of a firm based on the future outlook of the firm. The aim of this research was to analyse the different methodologies used to determine the Cost of Capital of a firm in order to determine which models are better ex ante predictor of Cost of Capital in the South African context. Regression analysis was used to make statistical inferences between the measure of risk used and the Cost of Capital model in question. The results of the research has shown that Market Capitalisation and Price to Book ratio are the best proxies for risk when comparing it with the ex ante Cost of Capital models. However, the Three Factor Pricing Model is shown to be the best Cost of Capital model to capture the future outlook of risk. / Dissertation (MBA)--University of Pretoria, 2010. / Gordon Institute of Business Science (GIBS) / unrestricted

<div>In the first chapter, our empirical tests use data from three sources. First, we obtain the Loughran and McDonald’s (hereafter LM wordlist) positive/negative wordlist and from the authors’ website. Second, we obtain the monthly Fama and French (1993 and 2015) factors (i.e. SMB, HML, Rm-Rf, CMA, and RMW) and momentum factor (MOM) from Kenneth French’s website for the sample period January 1994 through December 2016. Third, we obtain the monthly stock returns, monthly index returns, month end market value from the Center for Research in Security Prices (CRSP) as well as accounting information such as annual book</div><div>I the second chapter, we utilize five main datasets in this study. The first dataset is the stock market transaction level data for S&P 500 stocks, obtained from Trades and Quote (TAQ). The second dataset is the corporate bond transaction data from Trade Reporting and Compliance Engine (TRACE) through Wharton Research Data Services (WRDS) for the S&P 500 firms. The TRACE data provides over the counter (OTC) corporate bond market real-time prices.To examine the price discovery of bonds in equity prices we use a sample period of over 1,000 trading days from January 2004 through December 2008.</div><div>Our third data source is the institutional level transaction data from ANcerno, which provides transactional level trade data for corporate bonds and stocks for the first quarter of 2006 through the third quarter of 2010. Several studies have used equity transaction dataset to examine the ANcerno institutional trading behavior. See for example Puckett and Yan (2011), Bethel, Hu and Wang (2009), Chemmanur, He and Hu (2009), Goldstein, Irvine, Kandel and Wiener (2009). Additionally, Hu, Jo, Wang and Xie (2018) provide a comprehensive review of ANcerno dataset. The fourth source of data comes from Mergent Fixed Income Security Database (FISD), which provides details of bond characteristics and credit ratings from standard and poor’s (S&P) and Moody’s. Finally, we obtain the daily stock returns data from center for security prices (CRSP) database and match it with the daily bond returns to examine the lead-lag relationships.</div><div><br></div>

本論文採用Fama and French[1993]所提出之三因子模式為基礎，以公司規模[firm size]、帳面淨值市價比[book to market ratio]、及市場超額報酬[market excess return]為三因子，配合動能因子[momentum]及三種不同的流動性指標[成交量，成交值，成交量週轉率]來延伸探討五因子的時間序列資產定價模式。 本文的研究資料為西元1992年1月到西元2000年12月間的452家上市公司週資料，期望能解釋月資料所無法包含的資訊內涵。 結論： (1.)台灣股票市場確實有規模效果，淨值市價比效果，動能效果，及流動性效果。 (2.)市場因子具有解釋能力。 (3.)小公司投資組合解釋效果不佳，在台灣股票市場可能有其他因素未放入評價模式中驗證。 (4.)流動性指標在台灣股票市場上，確實和股票報酬有負向的關係存在，且建議以成交量週轉率作為流動性的代表指標。 (5.)台灣股票市場有顯著的動能存在，投資者可藉由動能策略獲得更高的超額報酬。 / This article provides evidence that stock returns listed in the Taiwan Stock Exchange do have shared variation due to the “market anomalies”, such as size, book-to-market ratio, momentum, and liquidity, which have been argued by scholars and investment professionals for many years. The evidence shows that small-cap effect plays an important role in explaining the violation in stock returns after controlling for other determinants of stock returns. Besides, value, momentum, and liquidity effect do exist in the Taiwan stock market. Moreover, we suggest that turnover rate is a better proxy for liquidity in terms of its stronger relations with the stylized portfolio returns. We empirically estimate the intercepts of our asset-market models using weekly time-series data for individual securities over the sample period from 1992 to 2000 and across 452 securities. To emphasize particularly, our result does not imply that the Taiwan stock market is not an efficient market.

本研究考慮市場、規模、淨值市價比及前期累積報酬，這四個影響股票報酬的因子，分別以個股特徵之持股比例變動法與四因子評估模型，對共同基金風險調整後的報酬作績效評比，不但可以評估基金的選股能力與擇時能力，並進一步瞭解報酬之風險來源。最後討論這兩種評比方式的適用性，並藉由基準投資組合將市場股票區分成不同的風險類別，根據基金在各類別股票的持有比例，引伸出對基金持股風格的另一種看法。現將本篇研究結果整理如下： 1. 四因子模型對於資產的解釋能力比資本資產評價模型（CAPM）好；並且透過規模、淨值市價比、前期累積報酬之風險溢酬因子，可以瞭解報酬之不同風險來源。 2. 依照個股特徵為基準之持股比例變動法，計算出實際績效、特徵擇時、特徵選股及平均持股型態的績效。結果顯示共同基金多具有正的選股能力，擇時能力，但經過檢定，並沒有顯著的超額報酬。 3. 以四因子評價模型對共同基金績效做評估。結果發現幾乎不具有顯著的超額報酬；兩種方法的評比結果相類似。但是部份基金在規模、與前期累積報酬項有顯著異於零的結果，顯示基金在規模、量能操作上有穩定的績效表現，因此使得檢定的結果顯著。 4. 而以持股類型風格上來看，顯示部份基金會高度持有大型股、以及過去表現良好的股票，持股風險類群明顯而集中，屬於穩健、偏重長期，並配合量能操作的投資策略。 最後根據本文的實證結果，分別對投資人與基金經理人提出建議。而從持股比例計算的過程，對持股風格分析提供一個更簡易明瞭的看法，並將研究中發現的問題，一併列在建議中，提供給後續研究者作為參考。 第一章 緒論…………………………………………1 第二章 文獻回顧……………………………………4 第一節 風險調整因素……………………………4 第二節 四因子評估模型…………………………7 第三節 依個股特徵之持股比例變動法…………9 第三章 研究設計……………………………………13 第一節 研究假說…………………………………13 第二節 研究架構…………………………………14 第三節 研究範圍與期間…………………………16 第四節 變數定義與資料處理……………………18 第四章 實證結果與分析……………………………22 第一節 四因子評估模型…………………………22 第二節 共同基金績效評估………………………27 第三節 基金之持股類型比例……………………36 第五章 結論與建議…………………………………40 第一節 結論………………………………………40 第二節 建議…………………………………………41 參考文獻……………………………………………45