Advertising a Few Systematic ETFs (Strictly Of My Own Volition)

This post will introduce several ETFs from Alpha Architect and Cambria Funds (run by Meb Faber) that I think readers should be aware of (if not so already) in order to capitalize on systematic investing without needing to lose a good portion of the return due to taxes and transaction costs.

So, as my readers know, I backtest lots of strategies on this blog that deal with monthly turnover, and many transactions. In all instances, I assume that A) slippage and transaction costs are negligible, =B) there is sufficient capital such that when a weighting scheme states to place 5.5% of a portfolio into an ETF with an expensive per-share price (EG a sector spider, SPY, etc.), that the issue of integer shares can be adhered to without issue, and C) that there are no taxes on the monthly transactions. For retail investors without millions of dollars to deploy, one or more of these assumptions may not hold. After all, if you have $20,000 to invest, and are paying $50 a month on turnover costs, that’s -3% to your CAGR, which would render quite a few of these strategies pretty terrible.

So, in this short blurb, I want to shine a light on several of these ETFs.

First off, a link to a post from Alpha Architect that essentially states that there are only two tried-and-true market “anomalies” when correcting for data-mining: value, and momentum. Well, that and the durable consumption goods factor. The first, I’m not quite sure how to rigorously test using only freely available data, and the last, I’m not quite sure why it works. Low volatility, perhaps?

In any case, for people who don’t have institutional-grade investing capabilities, here are some ETFs that aim to intelligently capitalize on the value and momentum factors, along with one “permanent portfolio” type of ETF.

Momentum:
GMOM: Global Momentum. Essentially, spread your bets, and go with the trend. Considering Meb Faber is a proponent of momentum (see his famous Ivy Portfolio book), this is the way to capitalize on that.

Value:
QVAL: Alpha Architect’s (domestic) Quantitative Value ETF. The team at Alpha Architect are proponents of value investing, and with a team of several PhDs dedicated to a systematic value investing research process, this may be a way for retail investors to buy-and-hold one product and outsource the meticulous value research necessary for the proper implementation of such a strategy.

IVAL: an international variant of the above.

GVAL: The Cambria Funds quantitative value fund.

Asset Allocation (permanent portfolio):

GAA: Global Asset Allocation. My interpretation? Take the good old stocks, bonds, and real assets portfolio, and spread it out across the globe.

Now, let’s just do a quick rundown and see how these strategies have performed over the small time horizon the latest one has been in existence.

symbols <- c("GMOM", "QVAL", "IVAL", "GVAL", "GAA")

getSymbols(symbols, from = "1990-01-01")
prices <- list()
for(i in 1:length(symbols)) {
  prices[[i]] <- Ad(get(symbols[i]))  
}
prices <- do.call(cbind, prices)
colnames(prices) <- gsub("\\.[A-z]*", "", colnames(prices))

coolEtfReturns <- Return.calculate(prices)
coolEtfReturns <- na.omit(coolEtfReturns)
charts.PerformanceSummary(coolEtfReturns, main = "Quant investing for retail people.")

stats <- rbind(table.AnnualizedReturns(coolEtfReturns),
               maxDrawdown(coolEtfReturns),
               CalmarRatio(coolEtfReturns),
               SortinoRatio(coolEtfReturns) * sqrt(252))
round(stats, 3)
                           GMOM  QVAL  IVAL  GVAL   GAA
Annualized Return         0.038 0.237 0.315 0.323 0.106
Annualized Std Dev        0.082 0.138 0.123 0.192 0.066
Annualized Sharpe (Rf=0%) 0.466 1.709 2.556 1.680 1.595
Worst Drawdown            0.039 0.046 0.046 0.069 0.028
Calmar Ratio              0.981 5.189 6.816 4.678 3.737
Sortino Ratio (MAR = 0%)  0.665 2.598 3.742 2.274 2.407

In other words, aside from momentum, which is having a flat-ish series of months, the performances are overall fairly strong, in this tiny sample (not at all significant).

The one caveat I’d throw out there, however, is that these instruments are not foolproof. For fun, here’s a plot of GVAL (that is, Cambria’s global value fund) since its inception.

And the statistics for it for the whole duration of its inception.

                          GVAL.Adjusted
Annualized Return                -0.081
Annualized Std Dev                0.164
Annualized Sharpe (Rf=0%)        -0.494
Worst Drawdown                    0.276
Calmar Ratio                     -0.294

Again, tiny sample, so nothing conclusive at all, but it just means that these funds may occasionally hurt (no free lunch). That stated, I nevertheless think that Dr. Wesley Gray and Mebane Faber, at Alpha Architect and Cambria Funds, respectively, are about as reputable of money managers as one would find, and the idea that one can invest with them, as opposed to god knows with what mutual fund, to me, is something I think that’s worth not just pointing out, but drawing some positive attention to.

That stated, if anyone out there has hypothetical performances for these funds that goes back to a ten year history in a time-series, I’d love to run some analysis on those. After all, if there were some simple way to improve the performances of a portfolio of these instruments even more, well, I believe Newton had something to say about standing on the shoulders of giants.

Thanks for reading.

NOTE: I will be giving a quick lightning talk at R in finance in Chicago later this month (about two weeks). The early bird registration ends this Friday.

14 thoughts on “Advertising a Few Systematic ETFs (Strictly Of My Own Volition)

  1. Pingback: Quantocracy's Daily Wrap for 05/13/2015 | Quantocracy

  2. Pingback: 05/14/15 - Thursday Interest-ing Reads -Compound Interest Rocks

  3. Thanks Illya for highlighting these ETFs. Your right about the potential usefullness of ETFs like these to us small investors. I am confused about the discrepancy in annualized returns for GVAL. In the first table it’s 0.323 but in your last table it’s -0.081.

      • Thanks I see that now. What a difference the March to the start of December 2014 makes.

      • Ilya,

        Great blog and recommendations. Any other active ETF (or mutual funds) that pass the sniff test for further investigation?

        I see so many great DIY asset allocations on the Internet but seemingly so few being translated into ETFs. I hope the little guy (and non quants) have more options in the future.

  4. Hello Ilya,
    I am getting: Error in `[.xts`(x, seq_len(xlen – n)) : subscript out of bounds,
    in the line coolEtfReturns <- Return.calculate(prices)

    any recommendation?

    Thanks,
    Alexandra

  5. Hi Ilya,
    Thanks very much, I enjoyed reading.
    I’m always worried with back-testing that we might get biased results, especially with quantitative approaches, as the strategy was developed based on past results.

    Do you have a good recommendation on how to account for that?
    (maybe randomly discard half of the stocks that pass the screening? or see if there’s any statistically meaningful deviation in results at different point in time?)

    • It’s really hard to say how biased a backtest on marketing materials may be. There’s always the joke of “you never see a bad backtest”. However, the way to avoid fooling *yourself* is by using Marcos Lopez De Prado’s Deflated Sharpe Ratio formula (among many others).

Leave a comment