BFF5040 ASA Group 18 Assignment FINAL 1

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

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.

As the return on market calculated is smaller than the risk free rate, creating a negative risk premium, this outcome is illogical. This can be expected when calculating return on market as it is based on past values and therefore is an estimate. For the purpose of this assignment, the time horizon for this calculation has been expanded to include the month of july in order to create a sensical risk premium. In making this variance it is noted that reliability has been slightly

The last five years’ worth of monthly returns for the Vanguard 500 Index Fund, Hasbro and R.J. Reynolds

To justify whether it works, we first ran a regression on the Three-Factor Fama French Model (Market Excess Return, SMB and HML) from Jan 1972 to Dec 2012 respectively for the High-, 5-, Low-, and High-Minus-Low Portfolio. Then, we added momentum into the model to create a four-factor model (Momentum, Market Excess Return, SMB and HML) for the same period on a monthly basis. (See Exhibit 2).

Some financial experts argue that certain macroeconomic variables can be used for predicting stock returns. One such macroeconomic variable is the Consumption to Wealth ratio. I will now examine how effective this variable is in predicting future stock market returns. The expected excess returns vary with the growths and dips in the business cycle, therefore, they should be predictable at different stages of the cycle. To examine the relationship, we note that aggregate consumption, asset holdings and labour income all share the same long term trend, but in the short term they may deviate substantially from one another. According to Lettau Ludvigson 2001, “we study the role of these transitory deviations from the common trend in

    Today’s stock market offers as many opportunities for investors to raise money as jeopardies to lose it because market depends on different factors, such as overall observed country’s performance, foreign countries’ performance, and unexpected events. One of the most important stock market indexes is Standard & Poor's 500 (S&P 500) as it comprises the 500 largest American companies across various industries and sectors. Many people put their money into the market to get return on investment. Investors ask themselves questions like how to make money on the stock market and is there a way to predict in some degree how the stock market will behave? There are lots and lots of

To determine if the low risk phenomenon exists in the selected research universe for the selected time period, we quintile the stocks (Quintile 1 = High Volatility, Quintile 5 = Low Volatility) by trailing 250 day price return annualized volatility at each month end for the entire selected time period. We then calculate the subsequent one month average return of each quintile. The one month average return of the volatility quintiles are presented in Exhibit 1.1. Quintile 5 (lowest volatility quintile) outperforms Quintile 1 (highest volatility quintile) by 63 bps per month on average. The Quintile 5 to Quintile 1 spread of 63 bps is statistically significant at the one percent level. Exhibit 1.2 shows the risk/reward payoff of the volatility quintiles.

First time this phenomenon was presented by the economists Rajnish Mehra and Edward Prescott in 1985. They discovered that the return from US equity investments in comparison to the return from a risk free government securities had been much far above during the twentieth century to be interpreted by the traditional economic theories (Siegel and Thaler, 1997).

The CAPM states that the securities plot on the Security Market Line (SML) in equilibrium. We do cross-sectional test is to identify whether the above statement is true with our two data set and whether or not it rejects the hypothesis that the slope is zero. In the equation 3, the gamma 0 is the excess return on a zero beta portfolio and gamma 1 (the slope of the regression) is the market portfolio's average risk premium.

From Q6, all regressions models ran by Single Index Model, a model helps to split a security’s total risk into unique risk and market risk, α is the intercept of the single index model in which evaluate the expected excess return of the security , and β is the security’s sensitivity to the market index, while e represents the unsystematic risk of the security:

Empirical studies show that these factors have exhibited excess returns above the market. For instance, the seminal Fama and French (1992) study found that the average small cap portfolio (averaged across all sorted book-to-market portfolios) earned monthly returns of 1.47% in contrast to the average large cap portfolio’s returns of 0.90% from July 1962 to December 1990.

The purpose of this paper is concentrated on relationship between Vietnamese stock price relative to exchange rate and United State stock market. In order to have a better view about this relationships, the suitable econometrics model will be used in the research are OLS and ARMA. To determine the correlation, coefficients among the variables from the test we will be able to find out the β, R2, P-value, Standard Error, Durbin-Watson stat statistic etc... With the time series dataset, in other to get a good forecast, the regressions will be run and tested on EVIEW program. The main model will be use is:

Weekly returns of the universe of Thai open-end, domestic equity funds which have been operated during 2000 to 2007 period.

What is more important, the results of the current section appear to be the same compared to the related studies devoting themselves to considering the aforementioned question. Practical evidence tends to prove that the Islamic mutual funds generally display continuous and sustainable under-performance once they are assessed from the perspective of Islamic and traditional financial indexes. Still, it is worth