VFMF (Vanguard Multifactor) == Closet Value Index Fund

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bogledogle87
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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by bogledogle87 » Thu Jul 11, 2019 2:27 pm

lazyday wrote:
Thu Jul 11, 2019 12:39 pm
Investing without following an index seems to offer significant benefits for a fund like this. Of course, the downside is that when the fund does poorly, it can be hard to know why. And if the fund isn’t index hugging, then there should be times where it underperforms.
I definitely agree. I now realize I grossly overestimated my own comfort level with this part of it.
VTWAX and chill

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hdas
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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by hdas » Wed Jul 17, 2019 9:29 am

vineviz wrote:
Wed Jul 03, 2019 7:54 pm
KyleAAA wrote:
Wed Jul 03, 2019 5:59 pm
Why such a large negative alpha? I would expect some, but -3ish% seems excessive. Is it due to reconstitution?
Usually this boils down to a systematic mismatch between the factor model the fund is using and the factor model used in the regression. In effect the regression is moving returns from beta to alpha.

However, it’s also possible that this is partly an intentional trade off: maintain higher factor loads at the expense of transaction costs.

Vanguard’s QEG is taking a bottoms up approach to their factor model, for better or worse, so we’ll likely always be a little uncertain about their process.
Check this out:

Image
"whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought" ET Jaynes

muffins14
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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by muffins14 » Wed Jul 17, 2019 10:54 am

Since this is an equity-only fund, it would be more appropriate to only use the equity factors (and exclude Term and Credit)

Re-running the regression there and using daily returns, one finds that USMF has statistically significant loadings on market, size, and BAB. It does result in positive alpha, but it's also not statistically significant

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hdas
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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by hdas » Wed Jul 17, 2019 11:11 am

muffins14 wrote:
Wed Jul 17, 2019 10:54 am
Since this is an equity-only fund, it would be more appropriate to only use the equity factors (and exclude Term and Credit)
People keep repeating this but I haven't seen a good reason for it. Do you have a reference from a credible source. Thanks :greedy
"whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought" ET Jaynes

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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by muffins14 » Wed Jul 17, 2019 11:28 am

No resource at my fingertips now, but I believe credit is to measure the premium of lower-credit bonds over higher-credit bonds, and term is to measure the premium of longer-term bonds over shorter-term bonds. In the case of a portfolio without bonds I don't think it would make sense to include those variables. But again I'm not an expert here, maybe someone else would chime in.

As an aside -- of course you can always include as many variables in any type regression as you want, but you'll start to lose interpretability, robustness against correlated features, and the ability to make good inferences about the meaning of the coefficients.

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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by hdas » Wed Jul 17, 2019 11:45 am

muffins14 wrote:
Wed Jul 17, 2019 11:28 am
No resource at my fingertips now, but I believe credit is to measure the premium of lower-credit bonds over higher-credit bonds, and term is to measure the premium of longer-term bonds over shorter-term bonds. In the case of a portfolio without bonds I don't think it would make sense to include those variables. But again I'm not an expert here, maybe someone else would chime in.

As an aside -- of course you can always include as many variables in any type regression as you want, but you'll start to lose interpretability, robustness against correlated features, and the ability to make good inferences about the meaning of the coefficients.
Thanks. I don't see how anybody thinks that the yield curve and credit spreads are not extremely relevant factors. I do agree with you regarding interpretability but perhaps you take one of the other factors out. If you run a PCA analysis you get about 2 independent sources of risk in stocks, and my personal experience running an actively managed portfolio of stocks sort of confirms this.
Term and credit risk based 2-factor model where the term risk premium is calculated as the difference between long-term treasuries and treasury bills and the credit risk premium is calculated as the difference between long-term corporates and long-term treasuries. This model is not adjusted to account for the differences in the interest rate sensitivities of long-term treasuries and corporate bonds (refer to the Hallerbach and Houweling, and Asvanunt and Richardson papers listed below).
"whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought" ET Jaynes

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vineviz
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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by vineviz » Wed Jul 17, 2019 12:10 pm

hdas wrote:
Wed Jul 17, 2019 11:11 am
muffins14 wrote:
Wed Jul 17, 2019 10:54 am
Since this is an equity-only fund, it would be more appropriate to only use the equity factors (and exclude Term and Credit)
People keep repeating this but I haven't seen a good reason for it. Do you have a reference from a credible source. Thanks :greedy
The reasons don't have anything specifically do with whether the variable is an "equity factor" or a "bond factor", but anytime you see a Fama-French regression with high values for unadjusted R2 or regression F statistic combined with a large number of statistically insignificant variables then you have a big red flag for over-fitting.

Over-fitting results from using more (correlated) independent variables than are necessary to explain the variance in your data.

There are plenty of formal tests for this (many econometrics books should cover it) but I don't have a solid reference handy. Look for a source that describes over-fitting, collinearity, and/or variance inflation.

What is happening is usually that the correlated variables are insignificant individually but significant in combination (because they are correlated), increasing the raw coefficient of determination (i.e. R2). That explanatory power has to go somewhere, so it (falsely) goes to alpha.

A quick and dirty check is to drop one or two of the least significant variables and see if the adjusted R2 and/or regression F-stat go up. If the adjusted R2 goes up while the raw R2 goes down, for example, chances are good that you have compromised your DoF somehow.

In this case with USMF reducing the factor set to RMF, SMB, HML, & MOM increases the adjusted R2 from 92.4% to 93.8% and the F-stat from 34.29 to 84.31. As a broad rule of thumb, that's a good indication that the smaller variable set is maximizing both explanatory power and degrees of freedom.
"Far more money has been lost by investors preparing for corrections than has been lost in corrections themselves." ~~ Peter Lynch

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hdas
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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by hdas » Thu Jul 18, 2019 3:49 pm

vineviz wrote:
Wed Jul 17, 2019 12:10 pm
hdas wrote:
Wed Jul 17, 2019 11:11 am
muffins14 wrote:
Wed Jul 17, 2019 10:54 am
Since this is an equity-only fund, it would be more appropriate to only use the equity factors (and exclude Term and Credit)
People keep repeating this but I haven't seen a good reason for it. Do you have a reference from a credible source. Thanks :greedy
The reasons don't have anything specifically do with whether the variable is an "equity factor" or a "bond factor", but anytime you see a Fama-French regression with high values for unadjusted R2 or regression F statistic combined with a large number of statistically insignificant variables then you have a big red flag for over-fitting.

Over-fitting results from using more (correlated) independent variables than are necessary to explain the variance in your data.

There are plenty of formal tests for this (many econometrics books should cover it) but I don't have a solid reference handy. Look for a source that describes over-fitting, collinearity, and/or variance inflation.

What is happening is usually that the correlated variables are insignificant individually but significant in combination (because they are correlated), increasing the raw coefficient of determination (i.e. R2). That explanatory power has to go somewhere, so it (falsely) goes to alpha.

A quick and dirty check is to drop one or two of the least significant variables and see if the adjusted R2 and/or regression F-stat go up. If the adjusted R2 goes up while the raw R2 goes down, for example, chances are good that you have compromised your DoF somehow.

In this case with USMF reducing the factor set to RMF, SMB, HML, & MOM increases the adjusted R2 from 92.4% to 93.8% and the F-stat from 34.29 to 84.31. As a broad rule of thumb, that's a good indication that the smaller variable set is maximizing both explanatory power and degrees of freedom.
Thanks for the comment. A couple of observations/questions:

1. In my portfolio of individual stocks, the situation is the reverse, I need to add more factors to increase the R^2, only Term and Alpha are significant.

2. A priori it seems to me that yield curve and credit spreads are more important (macro) factors than the style equity factors.

3. The Machine Learning tools seem a bit more useful than pure econometrics dealing with this stuff. Have you tried updating your toolkit?.

Cheers :greedy
"whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought" ET Jaynes

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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by vineviz » Thu Jul 18, 2019 4:19 pm

hdas wrote:
Thu Jul 18, 2019 3:49 pm
1. In my portfolio of individual stocks, the situation is the reverse, I need to add more factors to increase the R^2, only Term and Alpha are significant.
It's important to distinguish between unadjusted R^2 (which will ALWAYS go up with the addition of more independent variables) and adjusted R^2 (which will increase only so long as the added variable adds enough explanatory power to compensate for the smaller number of degrees of freedom).

For instance, using a random portfolio of 50 stocks I plotted the adjusted R^2 and unadjusted R^2 with various numbers of factors.

Image

You can see that unadjusted R^2 increases no matter how many variables are added (within the rounding error reported by PortfolioVisualizer, anyway) whereas the adjusted R^2 peaks with a four-factor model.
hdas wrote:
Thu Jul 18, 2019 3:49 pm
2. A priori it seems to me that yield curve and credit spreads are more important (macro) factors than the style equity factors.
That wouldn't be my prior belief, but it does raise a hypothesis that can be tested. As it turns out, term and credit factor are NOT more powerful at explaining the cross-section of stock returns than so-called equity factors (e.g. market, size, value, quality, etc.)
"Far more money has been lost by investors preparing for corrections than has been lost in corrections themselves." ~~ Peter Lynch

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hdas
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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by hdas » Thu Jul 18, 2019 5:03 pm

vineviz wrote:
Thu Jul 18, 2019 4:19 pm

It's important to distinguish between unadjusted R^2 (which will ALWAYS go up with the addition of more independent variables) and adjusted R^2 (which will increase only so long as the added variable adds enough explanatory power to compensate for the smaller number of degrees of freedom).
If you look at the link I sent, you'll notice that both the raw and adjusted R^2 go up as I add more factors. I start with 3 factors and keep adding one by one sequentially. Cheers :greedy
"whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought" ET Jaynes

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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by vineviz » Thu Jul 18, 2019 6:07 pm

hdas wrote:
Thu Jul 18, 2019 5:03 pm
vineviz wrote:
Thu Jul 18, 2019 4:19 pm

It's important to distinguish between unadjusted R^2 (which will ALWAYS go up with the addition of more independent variables) and adjusted R^2 (which will increase only so long as the added variable adds enough explanatory power to compensate for the smaller number of degrees of freedom).
If you look at the link I sent, you'll notice that both the raw and adjusted R^2 go up as I add more factors. I start with 3 factors and keep adding one by one sequentially. Cheers :greedy
Without access to the holdings and regression settings I can't help you further diagnose the causes, but I can tell you that's a problematic regression.
"Far more money has been lost by investors preparing for corrections than has been lost in corrections themselves." ~~ Peter Lynch

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hdas
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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by hdas » Mon Jul 22, 2019 1:23 pm

vineviz wrote:
Thu Jul 18, 2019 6:07 pm
Without access to the holdings and regression settings I can't help you further diagnose the causes, but I can tell you that's a problematic regression.
Vineviz,
I get the same effect when running the factor regression on my beta portfolio, here are the specs:

> Last 3 years
> VUG 30%, USMV 30%, IJS 20%, XSLV 20%
> You can start adding factors sequentially, start with AQR 3 factor, then 4 factor, then add quality, then BAB, finally Term + Credit.
> Notice how at every step you get higher adjusted R^2

Cheers :greedy
"whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought" ET Jaynes

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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by vineviz » Mon Jul 22, 2019 7:55 pm

hdas wrote:
Mon Jul 22, 2019 1:23 pm
vineviz wrote:
Thu Jul 18, 2019 6:07 pm
Without access to the holdings and regression settings I can't help you further diagnose the causes, but I can tell you that's a problematic regression.
I get the same effect when running the factor regression on my beta portfolio, here are the specs:

> Last 3 years
> VUG 30%, USMV 30%, IJS 20%, XSLV 20%
> You can start adding factors sequentially, start with AQR 3 factor, then 4 factor, then add quality, then BAB, finally Term + Credit.
> Notice how at every step you get higher adjusted R^2
For whatever reason, the regression on this portfolio (VUG/USMV/IJS/XSLV) is much more robust than whatever you were regressing in the other thread: R-squred in the 98% range instead of high 60s and low 70s, f-stat in the hundreds instead of the low-teens. T

Even so, the regression quality is marginally higher without including CDT (the adjusted R-squared stays at 98.2% but the f-stat is higher without than with). What makes the difference here, versus some other earlier examples, is that all 7 factors (once credit is excluded) are statistically significant.

With such a significant allocation to USMV and XSLV, I'm not surprised that TERM is statistically significant since the regression doesn't explicitly contain a low volatility or minimum variance factor: HML, MOM, and BAB are left to pick up the weight and they are too imperfect as proxies.
"Far more money has been lost by investors preparing for corrections than has been lost in corrections themselves." ~~ Peter Lynch

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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by jmk » Mon Jul 22, 2019 8:34 pm

vineviz wrote:
Thu Jul 18, 2019 4:19 pm
It's important to distinguish between unadjusted R^2 (which will ALWAYS go up with the addition of more independent variables) and adjusted R^2 (which will increase only so long as the added variable adds enough explanatory power to compensate for the smaller number of degrees of freedom).
What does adjusted r2 mean?

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Re: VFMF (Vanguard Multifactor) == Closet Value Index Fund

Post by hdas » Mon Jul 22, 2019 8:52 pm

jmk wrote:
Mon Jul 22, 2019 8:34 pm
What does adjusted r2 mean?
The adjusted R-squared is a modified version of R-squared that has been adjusted for the number of predictors in the model. The adjusted R-squared increases only if the new term improves the model more than would be expected by chance. It decreases when a predictor improves the model by less than expected by chance. The adjusted R-squared can be negative, but it’s usually not. It is always lower than the R-squared.
Cheers :greedy
"whenever there is a randomized way of doing something, then there is a nonrandomized way that delivers better performance but requires more thought" ET Jaynes

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