Comparison Of The Capm The Fama French Three Factor Model And Modifications

Seminar paper from the year 2014 in the subject Economics - Finance, grade: 6,0 (Schweizer Notensystem), University of Liechtenstein, früher Hochschule Liechtenstein, language: English, abstract: This paper is focused on comparing the Capital Asset Pricing Model, the Fama-French Three Factor model and two modified versions of the Fama-French Model in their ability to explain excess returns. The first modified model contains the same explanatory variables as the Fama-French Model but with an additional AR(1) process. The second modification contains instead of an additional AR(1) an AR(2) process. Evaluated by the adjusted R2 and the Akaike information criterion, the Fama-French model yields a higher model-fit than the CAPM. The modified Fama-French Model with an AR(2) process leads to significant results for the twice lagged return in the model in four out of six tested portfolios. Therefore, the in-sample regression reveals a higher model-fit of the modified Fama-French model with AR(2) in comparison to the other three models. Since the results differ from a regression in the subsequent period, the results are most likely spurious. Nevertheless, the authors show the high-er model-fit of the Fama-French Three Factor Model in relation to the CAPM.

Seminar paper from the year 2013 in the subject Business economics - Investment and Finance, grade: 8.5, Maastricht University, language: English, abstract: The CAPM model was developed by Sharpe (1964) and tries to give insight into the relation of risk and return characteristics of assets, in particular how risk adjusted excess returns of securities are influenced by the market. Fama and French (1996) further developed the CAPM to a three-factor model. Their aim was to enhance the explanatory power of the CAPM, thereby including the size (SMB) and book to market (HML) effect to achieve more explanatory insight of what drives returns. Carhart (1997) even included a fourth factor, namely the momentum anomaly (WML) as found out by Jagadeesh and Titman (1993), to further resolve the CAPM pricing error of not fully predicting returns, and add explanatory power. Additionally, we retrieved the sentiment index from Datastream to test a fifth explanatory factor. This research paper tests these four different models based on historical European data from the Kenneth R. French website and 50 European stocks and one European real estate index from Datastream. The structure of the research is closely tied to the set up used by Wang. The paper continues with a short literature review on the CAPM, the three-factor model, the four-factor model, and the sentiment index. Next, a description of the data and methodology is given. Then first the CAPM is tested, followed by the three-factor model, four-factor model and lastly the sentiment index is included. The results are discussed individually in each section. Finally, we draw an overall conclusion and include some limitations.

This paper compares the performance of the Fama-French three-factor model and the Capital Asset Pricing Model (CAPM) using two data sets. One set of portfolios is formed on size and the book-to-market equity ratio and another set is formed on industry. Using these two sets of portfolios, time series and cross-sectional tests are conducted over two different periods. The tests cannot unambiguously conclude that the three-factor model is better than the CAPM. Moreover, different data sets and periods yield different test results.

The aim of this paper is to examine the validity of the four-factor asset pricing as a comparison the standard Fama-French three factor model using U.S. monthly stock return data from period January 1963 to December 2010. Monthly stock return are constructed into 25 portfolio while the four-factor model includes the market factor (beta), the size factor (SMB), the book-to-market factor (HML), and the 'momentum' factor (MOM) which represents winners minus losers in terms of returns. Time series regressions following Fama and French (1993) are employed which includes the three-factor model as well as the four-factor model. Results indicated that the four-factor model to some extent have significant capability in explaining the variations in average excess stock return which consistent with Carhart (1997). R2 from the four-factor model is just slightly higher than the three factor model yet it provides indicative for the robustness of the model. Meanwhile, the January seasonals are also able to be absorbed by the risk factors including the market, SMB, HML, and MOM. Since the four-factor model seems capable in explaining the variation of the stock returns then application of this model in emerging markets may provide guidance for investor in understanding the market condition.

The issue of whether the Fama and French (FF) three-factor model is consistent with the propositions of Modigliani and Miller (MM) (1958, 1963) has received surprisingly little attention. Yet, unless it is so, the model is at variance with the foundations of finance. Fama and French (FF) (1993, 1995, 1996, 1997) argue that their three-factor asset pricing model is representative of equilibrium pricing models in the spirit of Merton's (1973) inter-temporal capital asset pricing model ICAPM or Ross's (1976) arbitrage pricing theory (APT) (FF, 1993, 1994, 1995, 1996). Such claims however are compromised by the observations of Lally (2004) that the FF (1997) loadings on the risk factors lead to outcomes that are contradictory with rational asset pricing. In response, we outline an approach to adjustment for leverage that leads by construction to compatibility of the FF three-factor model with the Modigliani and Miller propositions of rational pricing.

One of the main reasons to name this book as Financial Management from an Emerging Market Perspective is to show the main differences of financial theory and practice in emerging markets other than the developed ones. Our many years of learning, teaching, and consulting experience have taught us that the theory of finance differs in developed and emerging markets. It is a well-known fact that emerging markets do not always share the same financial management problems with the developed ones. This book intends to show these differences, which could be traced to several characteristics unique to emerging markets, and these unique characteristics could generate a different view of finance theory in a different manner. As a consequence, different financial decisions, arrangements, institutions, and practices may evolve in emerging markets over time. The purpose of this book is to provide practitioners and academicians with a working knowledge of the different financial management applications and their use in an emerging market setting. Six main topics regarding the financial management applications in emerging markets are covered, and the context of these topics are "Capital Structure," "Market Efficiency and Market Models," "Merger and Acquisitions and Corporate Governance," "Working Capital Management," "Financial Economics and Digital Currency," and "Real Estate and Health Finance."

By using the methodology of the Cointegration test to focus on the long run relationship and conditional volatility by GARCH model to focus on risk relationship, the results suggest that i) the value premium is related to the changes of fundamental risk; ii) there is an asymmetric effect on the price of the value stock and growth stock under different business conditions; iii) and the three risk factors are driven by a similar source of macroeconomic activity change, but the interactive relationship between these three risk factors is essential in explaining the rates of return, thus, they should be used together. Overall, the results in this thesis support the view that the Fama and French three-factor model is a strong model in explaining rates of return, and that the value premium is generated from systemic risk and should be used in the equilibrium asset pricing model. The finding is useful for academics and practitioners alike.

We examine the robustness of the Fama-French three-factor model in several bear and bull market periods. Data on bull and bear market periods are from the website of Global Financial Data. The data on the monthly returns of the 25 Fama French portfolios and the explanatory variablesmR, SMB, and HML are taken from the website of Dr. Kenneth French. We test for the significance of the individual regression parameters as well as for the equality of the coefficient vectors in each of the adjacent bear-bull periods. To make sure that our tests are not influenced by heteroskadisticity we use Toyoda's test to test the equality of the coefficient vectors in each of the adjacent bull-bear periods. We find that the model performs equally well in both bear and bull periods. In comparison to earlier bull-bear periods, however, the coefficient of determination decreases significantly in later periods. Furthermore, using cumulative sum of squares of recursive residuals and log likelihood ratio techniques, we find a structural change in the model in the year 2000. We use the Welch test to identify which regression parameters induce this structural change. We find that all the coefficients associated with explanatory variables undergo significant changes; however, the constant term remains insignificant. We conclude that the parameters of the Fama-French three-factor model are generally not influenced by bear and bull market conditions. This finding may make the FF three-factor model more usefulőthe prediction of future bull-bear market period may become redundant in estimating the risk premium. The regime change in the FF three-factor model in the year 2000 indicates that one should use post-1999 data to compute the parameters of the FF three-factor model to estimate the risk premium.

This study investigates the performance of four-factor asset pricing model using Hong Kong stock returns. Our four-factor model is constructed by adding a momentum facgtor into the Fama and French's (1993) three-factor model. We find that the four-factor model does well in explaining return variation using Hong Kong data. Our results show evidence that all the four factors are significant in the model and intercepts are not significant. In addition, the reasonably high values of adjusted R squared and the insignificance of an additional explanatory variable of residula standard deviation provide supportive evidence to the model. The robustness of the model is also checked for two effects: up- and down-market conditions and seasonal behavior.