The Variance Of The Unexpected Market Returns Essay

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

It is believed that Efficient Market Theory is based upon some fallacies and it does not provide strong grounds of whatever that it proposes. More importantly the Efficient Market theory is perceived to be too subjective in its definition and details and because of this it is close to impossible to accommodate this theory into a meaningful and explicit financial model that can actually assist investors in making the investment decisions (Andresso-O’Callaghan, B., 2007).

As Chapter 10 questions, if further evidence continues to surface that capital markets do not always behave in accordance with the efficient market hypothesis, then should we reject the research that has embraced the EMH as a fundamental assumption? In this regard we can return to earlier chapters of this book in which we emphasised that theories are abstractions of reality. Capital markets are made of individuals and as such it would not (or perhaps, should not) be surprising to find that the

4) Using the stock price and return data in Exhibits 5 and 6, estimate the CAPM beta

1. You have a portfolio with a beta of 3.1. What will be the new portfolio beta if you keep 85 percent of your money in the old portfolio and 15 percent in a stock with a beta of 4.5?

* Stock Beta: Exhibit 5 shows a detailed measurement of the company’s stock returns in relation to the rest of the market through 5-year historical price and index data. The analysis includes monthly returns of both the NYSE and the S&P 500 index in order to capture a comprehensive view of the market return. In each comparison, the monthly returns of the Target stock and market are plotted on Y-axis and X-axis respectively to get the regression line’s slope or beta. The analysis arrives at an average beta of 0.988 which indicates a similar movement of Target stock’s returns in comparison to the whole market over time.

We use the equation ri=(Pt-Pt-1+Dt)/Pt-1 to calculate the monthly return of stock of Charles Schwab Corp, Quick & Reilly Group and Waterhouse Investor Srvcs. Then we have two methods to calculate the Beta of Equity for each company.

Under the idea that markets are efficient, stock prices reflect new information quickly and accurately. Furthermore, Morningstar (n.d.) contributes details on the strongest supportive theory of efficient markets, EMH exists in three forms: weak, semi-strong and strong. The hypothesis calls for the existence of informationally efficient markets, were current stock prices reflect all information, and attempts to outperform the market will only come in the form of riskier investments. Also, because of a large number of independent investors actively analyzing new information simultaneously as it enters the market, investors react accordingly and is immediately reflected in the stock

By using the company return and the Value Weighted Returns of the Market, we derived the companies’ levered equity betas and then unlevered them.

To find the asset Beta (βa), we need to find the weighted average β of equity and the weighted average β of debt. We consider the β of debt to be 0, as debt has no relationship with market risk and it is evident from the balance sheet that Ameritrade had no interest bearing debt in 1997[1].

For estimation of betas, the above equation was run for the period from Jan, 2003 to Dec, 2006. Based on the estimated betas we have divided the sample of 63 stocks into 10 portfolios each comprising of 6 stocks except portfolio no.1, 5 and 10 having seven stocks each. The first portfolio 1 has the 7 lowest beta stocks and the last portfolio 10 has the 7 highest beta stocks. The rationale for forming portfolios is to reduce measurement error in the betas.

Efficient capital market “It was generally believed that securities markets were extremely efficient in reflecting information about the stock market as a whole” (Fama 1970). To extent that when there is new information about stock rise, the news was dispersed immediately and it affects the security 's price at that time.

In order to answer the question a numerical example will be provided. In table 2 the original data are reproduced (Ceramic Coatings product of percentage of corporate assets times estimated divisional beta => 0,10 * 0,95 = 0,095 => this reduces weighted average corporate beta to 0,795).

Richard Roll, and University and Auburn, University of Washington, and University of Chicago educated economist, began his career researching the effect of major events of stock prices. This experience likely helped him reach the two conclusions he makes in his 1977 “A Critique Of The Asset Pricing Theory’s Tests”, one of the earliest and most influential arguments against CAPM. In the paper, Roll makes two major claims: that CAPM is actually a redundant equation that just further proves the concept of mean-variance efficiency, and that it is impossible to conclusively prove CAPM. His first claim relates to mean-variance efficiency: the idea that mathematically one must be able to create a portfolio that offers the most return for a given amount of risk. Roll claims that all CAPM is doing is testing a portfolio’s mean variance efficiency, and not actually modeling out projected future returns. The second claim in the paper is that there is not enough data about market returns for CAPM to ever prove conclusive. Even if modern technologies could help alleviate some of the burden of testing market returns for publicly traded equities, there is still no way to account for the returns of less liquid markets, where there is less public information. This means it is impossible for

I am interested in examining the size factor for two reasons. Firstly, there has always been disparity with regard to the persistence of the size factor in literature. Secondly, the discussion has a strong practical implication in constructing asset pricing model and determining the cost of capital.