# how can I calculate the factor loading (beta)?

I am writing my Thesis about hedge funds performance measurement and I want to use the seven factor model proposed by Fung & Hsieh (2004).

Now, I am struggling to find out how to calculate the factor loadings (beta) used in the regression model:

${R_i,_t}$ - ${R_f,_t}$ = $\alpha$ + $\beta_1$*$equity$ + $\beta_2$*$size$ + ... +$\beta_7$*$commoditytrend$ + $\epsilon$

and which results are shown in Table 1 in the end of the paper. My Software Preference would be "R".

Any help would be appreciated.

• Hi @srm and welcome to quant.SE! you're referring how to get the results shown in the the table 1 of the link I posted given a similar or equal dataset? Moreover, check that the paper version is the same you have, if not, replace that with the proper one. – Quantopik Apr 30 '15 at 17:12
• thanks for your quick response. Do you mind letting me know how I can access the link you're referring to? Sorry, but I'm new on this page. – srm Apr 30 '15 at 17:58
• Dont worry @srm! you can access it by clicking on "Fung & Hsieh (2004)" in the question, just that! On all stackexchange.com site, all link are in blue and you can access by clicking on. To change eventually the link, click on "edit", change the URL on the side of the words on which the link is attached or highlight the words, click CTRL+L and paste the link. – Quantopik Apr 30 '15 at 18:02
• yes exactly, I am referring to the Table 1. Do you know which is the procedure to get there? Thanks a lot!!!!!! – srm Apr 30 '15 at 18:18
• Yes, I do. Do you have any preferences about the software you've to use (STATA, R, Matlab,...)? Can you edit the question by inserting the software too? – Quantopik Apr 30 '15 at 18:28

## 1 Answer

The R function you have to use is the lm() function.

On QuickR you can find a simple and clear tutorial on how to estimate a linear (multiple) regression model generally using the lm(). As further reference, I suggest you to read the Introducing R tutorial about linear model by G. Rodriguez.

I did not read the paper you cited, but, anyway, you should estimate the model simply by running on the R shell the following command:

results = lm(dependent_variable_name ~ independent_var1_name + ... + independent_var7_name)


and run that by clicking on CTRL + ENTER.

Of course, replace dependent_variable_name with the name of the variable you gave previously; the same for the independent variables.

This command does not take into account the problems you should check in order to get reliable estimates, as, for instance, heteroskedasticity, autocorrelation, not normal residuals,... but it would be a too broad topic to deal with here.

You can find a lot of reference and tutorial on the links I posted above, but, you can find help on quant.SE too.

Hope this help.

• @srm, check the answer by clicking on the check, please, if you think I answered to the question, – Quantopik May 1 '15 at 10:12