The Structural Changes In The Ff Three Factor Model And Its Robustness In The Bear Bull Market Periods

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.

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.

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 Efficient Market Hypothesis believes that it is impossible for an investor to outperform the market because all available information is already built into stock prices. However, some anomalies could persist in stock markets while some other anomalies could appear, disappear and re-appear again without any warning. A Special Issue on "Efficiency and Anomalies in Stock Markets" will be devoted to advancements in the theoretical development of market efficiency and anomaly in the Stock Market, as well as applications in Stock Market efficiency and anomalies.

This empirical study is conducted to apply the Fama French three-factor model in the Australian context using the most recent daily data for the period of 5 years from July 2009 to May 2015. The focus of this study is on various approaches of portfolio formation adopted in previous empirical studies in Australia and overseas. Three scenarios are constructed to assess the robustness of the estimated coefficients from the model. The approach proposed by Fama and Macbeth (1973) - the two-stage cross-sectional regression technique is adopted in this paper. The findings from this study under various scenarios and various approaches to portfolio formations across portfolios are mixed. This study finds that only book-to-market factor is priced in Australia. However, there is a negative relationship between this factor and a return of a stock which is in contrast with an expectation of the Fama French three-factor model. As such, a claim from a recent study in Australia that for the first time, Fama French three-factor model produces a consistent outcome is simply exaggerated. It can be argued that this new finding is an outcome of another “data mining” which is generally labelled for the findings from the Fama French three-factor model.

Chapter four proposes a generalized impulse response function for joint bi-factor model which can be extended to the Bivariate Two-Markov-Chain VAR model, while the concept of the generalized impulse response is introduced by Koop et al. (1996) which can be used for both linear and nonlinear multivariate models. Compared with current popular generalized impulse response function in Markov-switching vector autoregressive models by Karame (2010, 2012), the joint bi-factor model contains two first-order Markov chains which can capture two different but related markets well, for instance, stock market and bond market. The empirical illustration of this generalized impulse response function makes use of estimation of the joint bi-factor model from chapter three. The generalized impulse response graphs show that both stock and bond markets react most strongly in the Bear market and react to the weakest in the Bull market when a unit stock shock occurs; both stock and bond markets react most strongly in the high bond return phases and react to the weakest in the low bond return phases when a unit bond shock occurs.

Seminar paper from the year 2012 in the subject Business economics - Investment and Finance, grade: 1, University of Leicester (School of Management), language: English, abstract: Scientists use factor models to try to understand the relationship between risk and asset returns and to make estimations of the likely development of the returns in the future (Sharpe 2001, p.1). Today, two of the most renowned factor models to estimate expected returns of an asset or a firm are the Capital Asset Pricing Model (CAPM), introduced by Treynor (1962), Sharpe (1964), Lintner (1965) and Mossin (1966), and the three-factor model of Fama and French of 1992 (Bartholdy and Peare 2004, p.408). While the CAPM claims the existence of a positive linear relationship between the volatility/risk (market beta) and expected returns (Bali and Cakici 2004, p.57), Fama and French state that their three-factor model (3FM) has an improved performance in estimating returns as – so they claim – size and book-to-market equity have significant predictive power, too (Fama and French 1992, p.427).

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.

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.