Fama and French’s three factor model attempts to explain the variation of stock prices through a multifactor model that includes a size factor and BE/ME factor in addition to the beta risk factor. Fama-French model essentially extended the CAPM (which breaks up cause of variation of stock price into systematic risk which is non-diversifiable and idiosyncratic risk which is diversifiable) by introducing these two additional factors. Fama and French find that stocks with high beta didn’t have consistently higher returns than stocks with low beta and this indicates that beta was not a useful measure under their model. Their model is based on research findings that sensitivity of movements of the size and BE/ME factor constituted risk, and
The extracted data used includes monthly returns from January 1972 to July 2011. The assets are selected so that the portfolio contains the largest, most liquid, and most tradable assets. The choice of such a variety of assets across several markets was used in order to generate a large cross sectional dispersion in average return. It helped to reveal new factor exposure and define a general framework of the correlated value and momentum effects in various asset classes.
Here we choose VW NYSE, AMEX, and NASDAQ data as market returns, because it’s value weighted and more reliable. The results show CSC’s equity beta = 2.27, QRG’s equity beta = 1.79.
Same as the results of 10-year period, the healthcare excess return is positively correlated with the utility excess return while negatively correlated with the material excess return. Compared to the 10-year results, it is found that beta of X1 decreases from 0.2750 to 0.2045 and beta of X2 decreases from -0.3165 to -0.3382. Also, beta of X0 drops which indicates without incorporating the event risk, more other variable is explained by the explanatory variables. As the p-value is less than 5% significance level, the relationships are both statistically significant. The standard errors of X1 and X2 are both small enough to indicate that the observations are close to the fitted
(1) What is a beta coefficient, and how are betas used in risk analysis? (2) Do the expected returns appear to be related to each alternative’s market risk? (3) Is it possible to choose among the alternatives on the basis of the information developed thus far? Use the data given at the beginning of the problem to construct a graph that shows how the T-bill’s, High Tech’s, and Collections’ beta coefficients are calculated. Discuss what beta measures and explain how it is used in risk analysis.
9. (10 points) You are provided with the following monthly expected returns, each of which is represented by E(Ri), and betas for the following stocks. Please estimate the capital asset pricing model and draw conclusions about the significance and realism of the results. (Note: Please use conventional tests of the R-squared and coefficients.) On the basis of your results, please name at least three of the stocks that you would recommend as “buys.”
When investing in a company, the goal is to buy shares at a low price and then sell them at a higher price. Individual stocks may go up or down independent of how “The Market” is doing overall. Stock market indices such as the Dow Jones Average, the NASDAQ, and the Standard and Poors 500 report how “The Market” is doing “on average.” To check
19. Each stock’s rate of return in a given year consists of a dividend yield (which might be zero) plus a capital gains yield (which could be positive, negative, or zero). Such returns are calculated for all the stocks in the S&P 500. A weighted average of those returns, using each stock’s total market value, is then calculated, and that average return is often used as an indicator of the “return on the market.”
A macroeconomic item (or factor) may be generally categorized as anything that influences the direction of a particular large-scale market (Investopedia, n.d.). For this analysis, the macroeco-nomic factor shall be interest rate (i.e. discount rate). The following sections that scrutinize the implications of changes to interest rates to the Company’s Present Value* (PV), based on its Free Cash Flows* (FCF); discuss the impact of an issue within the overall stock market on the Com-pany’s stock valuation numbers, other financial variables, or its overall portfolio management; and analyze and discuss the impact of an external factor to the Company.
The risk premium is equal to the difference between the risk-free rate and the expected market return. The case study provides two historical equity risk premiums; the geometric and arithmetic mean. The conventional wisdom is that the geometric mean is considered a better estimate for valuation over long periods, while the arithmetic mean a better estimate for valuation over shorter periods. To coincide with the choice of the 20yr yield on U.S. Treasuries, the geometric mean was therefore chosen for this analysis i.e. (Rm-Rf) = 5.9%
Using time series regression on the monthly returns we have estimated the beta coefficient for each stock. Using the market model of CAPM i.e., regressing each stock’s monthly returns against the market index (Nifty100) we have estimated individual stock beta’s.
As indicated by the case study S&P 500 index was use as a measure of the total return for the stock market. Our standard deviation of the total return was used as a one measure of the risk of an individual stock. Also betas for individual stocks are determined by simple linear regression. The variables were: total return for the stock as the dependent variable and independent variable is the total return for the stock. Since the descriptive statistics were a lot, only the necessary data was selected (below table.)
Randolph Corporation is a multidivisional company. Due to frictions among the divisions, Randolph’s stock has not performed according to expectations. In order to improve Randolph’s financial situation and position among its competitors, a number of questions need to be answered. We will discuss these questions separately below.
Even though there are flaws in the CAPM for empirical study, the approach of the linearity of expected return and risk is readily relevant. As Fama & French (2004:20) stated “… Markowitz’s portfolio model … is nevertheless a theoretical tour de force.” It could be seen that the study of this paper may possibly justify Fama & French’s study that stated the CAPM is insufficient in interpreting the expected return with respect to risk. This is due to the failure of considering the other market factors that would affect the stock price.
As a result, many different factors have been tested across different markets. Historically, most studies have relied on general economic theories or empirical observations in selecting Factors. Benaković and Posedel (2010) emphasize Interest rates, oil prices, and industrial production. Chen, Roll and Ross (1986) use industrial production growth, inflation, bonds spread, NYSE stock market returns, oil prices, interest term structure, and consumption to decompose returns of a portfolio with general securities. Bodurtha, Cho and Senbet (1989) even uses international factors. Most of literatures have focused on applying different factors with a portfolio of many securities, as it is widely known that idiosyncratic risks diversifies away as the number of