By Edwin Burmeister; Richard Roll; Stephen A. Ross; Edwin J. Elton; Martin J. Gruber; Richard Grinold and Ronald N. Kahn
This monograph offers the paintings of 3 teams of specialists addressing using single-factor versions to provide an explanation for defense returns: Edwin Burmeister, Richard Roll, and Stephen Ross clarify the fundamentals of Arbitrage Pricing thought and speak about the macroeconomic forces which are the underlying assets of hazard; Edwin J. Elton and Martin J. Gruber current multi-index versions and supply suggestions on their reliability and value; and Richard C. Grinold and Ronald N. Kahn handle multiple-factor types for portfolio threat.
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Extra resources for A Practitioner's Guide to Factor Models
It rests on the choice of the test used to determine sigmficance. The answer would seem to be either four, ten, or more factors. This ambiguity illustrates how cautious one should be about placing too much reliance on statistical sigmficance in deciding on the number of factors. The next step is to use information from more than one group to decide on the number of factors. Mult$le-group tests. The intent of our analysis was to estimate a returngenerating process that describes the return on all stocks that are comparable to the stocks in the NRI 400 Index.
At the same time, it creates a manageable set of data that allows examination of average explanatory power and explanatory power across sets of stocks. Table 5 shows the sensitivities and adjusted R2s when the returns on each of the 20 portfolios are regressed against the four factors for the 15-year period April 1971 to March 1986. Across the 20 portfolios, the average adjusted R2 is 78 percent. Of the 80 different sensitivity estimates, all but 18 are significant at the 5 percent level. TABLE 5.
The conclusion from examining the number of factors present in the return-generating process of each sample separately is somewhat ambiguous. It rests on the choice of the test used to determine sigmficance. The answer would seem to be either four, ten, or more factors. This ambiguity illustrates how cautious one should be about placing too much reliance on statistical sigmficance in deciding on the number of factors. The next step is to use information from more than one group to decide on the number of factors.
A Practitioner's Guide to Factor Models by Edwin Burmeister; Richard Roll; Stephen A. Ross; Edwin J. Elton; Martin J. Gruber; Richard Grinold and Ronald N. Kahn