Theory Flashcards
(39 cards)
Edwin Elton and Martin Gruber (the main authors of our textbook) often heard investment managers say: „ I followed that rubbish CAPM theory and bought stocks with high betas last year and they did worse than stocks with low betas. This theory is useless:
Explain why the statement of the investment managers is perfectly consistens with the CAPM theory does not invalidate it.
The CAPM is an equilibrium relationship. High ß stocks are expected to give a higher return than low ß stocks because they are more risky. However, this does not mean that they will give a higher return over all intervals of time. In fact, if they always give a higher return, they would be less risky, not more risky than low ß stocks. Rather because they are more risky, they will sometimes produce lower returns. However over long periods of time, the they should on average produce higher returns
In their article Kothari, Shanken and Loan (1995) argue that the size and book to market effect in financial data are an artifact if problems inherent in the statistical test finding these effects, such as data mining and database biases.
Describe what they mean by data mining
Data Mining problem occuses if many acadamics use the same dataset to analyse the revaulation between one specific variable (E.g. the expected equity return) and countless other variables. Even if the expected equity return is unrelated to all of them, we would expect that in 95% of all cases there is a spurious relation at standart significance levels. In addition the relationship in the past might not hold in the future. For example. If stock A has high return in the past, it does not mean it will have good performance in the future. However, if stock A give the same level of return in the past and the present, it is just coincidence.
Sumarize the compustat biases that they discuss in their artice and that could lead to size and book to market effect.
One Problem with COMPUSTAT is that it only included a firm in the database, if its size crosses a specific threshold. When firm is included in COMPUSTAT, the database then also include the past five years of the which much have necessraly been good years for the firm. In the same vein, distressed firms do no longer need to provide accounting information and are hence not included in COMPUSTAT.
You are interest in testing a three facit linear asset pricing model using the method of fama and Macbeth (1973). You test asset are 64 portfolios three way sorted on book-to market, size and momentum. Your sample period ranges from Jauary 1963 to December 2008. What does the term “beta estimaton period” mean in the context of the fama mac beth method? How long is a reasonable beta estimation period?
The length of the beta estimation perdio is the number of ex ante observation used to estimate the beta parameters. It is normally between 4-8 years
You are interest in testing a three facit linear asset pricing model using the method of fama and Macbeth (1973). You test asset are 64 portfolios three way sorted on book-to market, size and momentum. Your sample period ranges from Jauary 1963 to December 2008. Explain the first stage (or fist pass) time series regressions, i.e., define the endogenous (Y) variable and the exogenous (X) variables for each time series regression. How many times series regression do you need to ruen? What is the purpose of the time series regressions?= Why is it beneficial to run the time series regression of portfolios?
In the time series regression, you regress the test asset, so there are 64 time series regressions in our case. The purpose of the time series regressions is to obtain beta estimates. The beta estimates usually suffer from lower standart erros thn those of individual equities
You are interest in testing a three facit linear asset pricing model using the method of fama and Macbeth (1973). You test asset are 64 portfolios three way sorted on book-to market, size and momentum. Your sample period ranges from Jauary 1963 to December 2008. Explain the second stage (or second pass) cross sectional regression, i.e., define the endorgenous (Y) variable and the exegonueos (X) variable in the one cross sectional regression. How can we intereprt the parameter estimates?
In the cross sectional regression, we regress the realized return of all test asset at the end of the first beta estimation period on the estimated betas.
You are interest in testing a three facit linear asset pricing model using the method of fama and Macbeth (1973). You test asset are 64 portfolios three way sorted on book-to market, size and momentum. Your sample period ranges from Jauary 1963 to December 2008. What is meant by rolling forward the beta estimation perdion by one month?
Rolling forward means using a new beta estimation period starting in February 1963 and ending in January 1967 ( if the length of the beta estimation period is four years).
You are interest in testing a three facit linear asset pricing model using the method of fama and Macbeth (1973). You test asset are 64 portfolios three way sorted on book-to market, size and momentum. Your sample period ranges from Jauary 1963 to December 2008. How can you obtain the final risk premia (lambda) estimates?
The final risk premia estimate are simply the averages of the cross sectional estimaktes.
You would like to test a linear asset pricing model using time series regression. As a result, you regress the realized excees return of one asset onto the pricing factor realization (e.g., in case of the CAPM, you regress the asset excess return onto the market portfolio return. In which case can you reject the asset pricing model? In which case can you not reject the asset pricing model (i.e. what is the statiscal test you use to test the model)?
You test wheter the incerpt is statically different from zero (alpha =0). If it is, you reject the asset pricing model. Otherwise, you cannot reject the model.
You would like to test a linear asset pricing model using time series regression. As a result, you regress the realized excees return of one asset onto the pricing factor realization (e.g., in case of the CAPM, you regress the asset excess return onto the market portfolio return. Can you use time series regressions to test all linear asset pricing model? Could you , for example, test the fama and French (1993) model using this methodology? Could you use this methododly to test macroeconomic factor model whose pricing factors are changes in growth expectatios, dedault risk and inflation? In general what is the rule to determine whether a linar asset prcing model can be testes using time series regression?
You can only use time series regression for asset prcing model featuring only traded assets as pricing factor. So you can test the fama and French model with this methodoly, but not a macroeconomic model.
You would like to test a linear asset pricing model using time series regression. As a result, you regress the realized excees return of one asset onto the pricing factor realization (e.g., in case of the CAPM, you regress the asset excess return onto the market portfolio return. What are the advantages and disadvantages of the time series methodology?
One advantage is that time series regressions avoid the errors in variables proble,. A disadvantage is that they can pnl ybe employed to one asset at the time. Another disadvantage is that they cannot always be used.
Do investors like or dislike increase in the mean return of an asset? Why?
They like increase in the mean, because the mean return can be seen as the expected compensation from an investment (the higher the better)
Do investors like or dislike increase in the variance of an asset? Why?
They dislike variance because of risk aversion (and higher probability of potential losses to suffer)
Do investors like or dislike increase in the skewness of an asset? Why?
They like positive skewness since positive skewness increases the change of very positive return
Do investors like or dislike increase in the kurtisos of an asset? Why?
They dislike kurtisos because the joy they experience from positive return does not coun as much as the pain they expierence from very large negative return.
Assume that you use the Elton, Gruber, Brown and Goetzmann (EGB&G 2003) method to find the weights of the optimal mean variance efficient portfolio (this is the method featuring the z-variables-our unknow paratameters, whose optimal values can be derivied from gaussian elimination!) What is the basic intuition behind the EGB&G Methid, i.e., which measure do we maximize to find the optimal portofilio weights= Why do we maximize this measure?
Using this methid, we maximize the sharp ratio, as the mean variance efficient portolio will always feature the highest possible sharp ratio.
Assume that you use the Elton, Gruber, Brown and Goetzmann (EGB&G 2003) method to find the weights of the optimal mean variance efficient portfolio (this is the method featuring the z-variables-our unknow paratameters, whose optimal values can be derivied from gaussian elimination!) Does this method (as discussed in the lecture) allow short sales?
The oprimal weights obtained from this method can be negative, which means that short sales are not ruled out
Assume that you use the Elton, Gruber, Brown and Goetzmann (EGB&G 2003) method to find the weights of the optimal mean variance efficient portfolio (this is the method featuring the z-variables-our unknow paratameters, whose optimal values can be derivied from gaussian elimination!) Assume we obtain Z(1) = 2 and Z(2) = 1, such that the ratio between the two variables is equal to 2. What implication can we draw from this ratio?
If the Z-ratio is 2, then this means that the second asset should obtain two times the weight of the first asset in the optimal portolio.
Assume that you use the Elton, Gruber, Brown and Goetzmann (EGB&G 2003) method to find the weights of the optimal mean variance efficient portfolio (this is the method featuring the z-variables-our unknow paratameters, whose optimal values can be derivied from gaussian elimination!) Why do we derive the optimal portfolio weights by deviding one z-variable by the sum of all z-variables, i.e., what additional constrain does this impost.
Dividing by the sum of the z-vatiables does automatically impost the constrain that the weights of the assets in the optimal portfolio have to sum up the unity
What is meant by the statement : “the portfolios must be investable”?
Investable portfolios are portfolios into which equity investors at the time could have put money, i.e., thest are portfolio not using any hinsight information.
According to the fama fench model answer the following questions:Carefully define each term in the fama fench formula. What do the beta exposdures stand for? What do the lambdas stand for?
The beta exposures measure comovement btween the asset return and the risk factors. The lambdas give the risk premia, i.e, the extra compensation for bearing one unit of beta risk. The beta coefficient can be obtaim from multiple time series regressions, whereas the lambda coefficnet are often estimated using crs sectional test.
According to the fama fench model answer the following questions:How are the SMB and HM zero investment portfolios defines?
HML is a poftfolio long on high book to market socks and short on low book to market stocks, keeping the size dimension constant. SMB is a portfolio long on small and short on large socks, keeping the book-to market deminsion constant.
According to the fama fench model answer the following questions: How well does the fama and French model perform in asset pricing tests?
The model performs very well in empirical asset pricing tests (which may not be very surprising)
19) Shares of small firms with thinly traded socks tend to show positive CAPM alphas. Is this a violation of the efficient market hypothesis?
Thinly traded stocks will not have a considerable amount of market research performed on the companies they represent. The neglected firm effect implies a greater degree of uncertainty with respect to smaller companies. Moreover, there might be liquidity premium for thinly traded stocks that is not captured by the CAPM. Thus positive CAPM alphas among thinly traded stocks do not necessarily violate the efficient market hypothesis since these higher alphas are actually risk premia, nit market inefficiencies.