The Ivy Portfolio

Several months ago I finished reading a very interesting book called, “The Ivy Portfolio.” This book was written by two money managers, Mebane Faber and Eric Richardson, who work at Cambria Investment Management. The authors wanted to answer the question of why money managers who manage some of the world’s best Ivy League schools produce such consistent results. Routinely Harvard and Yale endowments produce double digit annual returns. Since 1985 Yale University has returned around 16% annual returns and Harvard over 15% annual returns. Not only did they produce outstanding returns, but they did it by also reducing volatility and drawdown.

Wouldn’t it be nice to mimic the investing strategy utilized by these endowments? Well, the authors do just that. Faber and Richardson set out to explore how these endowments produce such great returns and minimize both volatility and drawdown. They go a step further by providing several simplified, yet effective, models to mimic the trading results of these professionals. The heart of one of their proposed models is a simple relative strength, asset allocation strategy using ETFs.

This really caught my attention. Such a simple model could be a great way to invest within a retirement account as most people have access to ETFs. It’s a long-only strategy (no shorting) and you don’t need to place trades very often.

In this article I want to create models based upon Faber’s and Richardson’s recommendations. To do this I’m going to use the fantastic ETF service called, ETF Replay. What this web service allows you to do is create trading strategies and backtest them on a portfolio of ETFs. The website charges a monthly fee to use their service, but it’s very reasonable. Between the book’s recommendation and ETF Replay service, we can produce a trading model.

The Ivy Trading System

We are going to trade a basket of ETFs. Exactly which ETFs will be explained later. We are not going to simply trade all the ETFs at once. We will instead rotate into the three best performers every month. The concept behind the top three performers is they will likely continue to perform into the near-term future. However, we also wish to reduce drawdowns and avoid holding our ETFs when within a bear market because even the best ranked ETFs during this environment will most likely be falling. Sure they are falling slower than the others in the basket, but we don’t want to be holding any of our ETFs if they are in a bear market. In other words, we wish to preserve our capital during a bear market. Remember, this is a long-only strategy. To avoid holding positions in a bear market we use a 100-day (5-month) simple moving average to filter our trades. We will only buy an ETF if it’s above this average.

Here is a summary of the relative strength ranking model:

1) Rank the ETFs based upon their relative strength. There are many ways to do this, but a very straightforward method is to simply rank each ETF based upon two historical results, a 3-month return and a 1-month return. ETFs are ranked for each of these two returns. A weight is then factored for each rank to compute an overall rank. You can find an example on the ETF Replay website. By using two historical returns, we are taking into account both a short-term return and a longer term return.  The ranking score is computed as the sum the equal weighting of the 20-day return and the 3-month return. These numbers are completely arbitrary. They are not optimized.

Overall Rank Score =  ( 20-Day Return ) *.5 + ( 3-Month Return ) *.5

2) Apply regime filter and only hold ETFs within a bull market regime. This is our old familiar regime filter that I’ve written about time and time again. The authors of the book use a 10-month simple moving average. This is similar to a 200-day simple moving average if you estimate about 20 trading days in a month. However, I choose to pick half that value simply because I would like my model to be a bit more responsive when taking into account the possible onset of a bear market. This value is not optimized.

Bull Market = Close > Average( Close, 100 days);

3) Re-balance the portfolio on a monthly schedule. At this time we evaluate our entire basket of ETFs based upon the two methods above. We then take action.

a. SELL all ETFs that either no longer rank in the top three positions and/or who have fallen below the regime filter.
b. BUY the top three ranking ETFs who are within a bull regime. Each ETF will be dedicated to 1/3 of the available account equity.

4) Our money remains in “Cash” (SHY) whenever not being allocated to a specific asset class.

Given this model it’s possible to hold 1, 2, 3 or zero positions. When money is not allocated to an ETF we move it into cash.

Ivy Five Trading System

We have our trading model ready to go, but what basket of ETFs are we going to trade? The authors first start us out with a very simple basket of five ETFs. These ETFs represent the broadest asset classes we wish to diversify over. Our relative strength rotational model will allow us to ride the best performing asset classes while perserving our capital during a bear market. The Ivy Five are:

BND – Vanguard Total bond market (4-5 year)
DBC – PowerShares DB Commodity Index
VEU – Vanguard FTSE All-World ex-US
VNQ – Vanguard MSCI U.S. REIT
VTI – Vanguard MSCI Total U.S. Stock Market

This basket of ETFs gives us a broad exposure to corporate/credit bonds (BND), commodities (DBC), international equity (VEU), REITs, Preferreds and MLPs (VNQ) and U.S. Equity (VTI). Let’s now use the ETF rewind website to test our Ivy Five Portfolio (green line) on our model. We will use the SPY ETF (blue line) as our benchmark. Returns include dividends but exclude commissions and slippage.


We can see our portfolio outperforms the benchmark in several ways. First, it produces a higher total return of 204% vs. 95%. But maybe even more important is during the bear market of 2008 we can see a significant difference in the drawdown levels. While the benchmark was down around 55% and our portfolio was down about half that value at 21%. Overall volatility is also significantly reduced with our portfolio. In the end our portfolio returns a 11.8% CAGR while the benchmark returns a 7.0% CAGR. Our model increases returns while reducing both drawdown and volatility.

Ivy Ten Trading System

Trading only five ETFs is rather restrictive when you consider the vast number of ETFs available. Let’s continue to work with the same asset classes but introduce a few specialized ETFs to expand our diversification.The authors recommend the following ten ETFs:

BND – Vanguard Total bond market (4-5 year)
DBC – PowerShares DB Commodity Index
GSG – iShares S&P Commodity-Indexed Trust
RWX – SPDR DJ International Real Estate
TIP – iShares Barclays TIPS (4-8 years)
VB – Vanguard MSCI U.S. Small Cap
VEU – Vanguard FTSE All-World ex-US
VNQ – Vanguard MSCI U.S. REIT
VTI – Vanguard MSCI Total U.S. Stock Market
VWO – Vanguard MSCI Emerging Markets

The results of running this portfolio through out model is below. Again, the SPY ETF is our benchmark. Returns include dividends but exclude commissions and slippage.

We can see our portfolio, once again, outperforms the benchmark in several ways. First, it produces a higher total return of 291% vs. 95%. Notice this is signifcannly higher than our Ivy Five Portfolio. In regards to drawdown, the benchmark was down around 55% while our portfolio was down 29%. It’s interesting to note we do generate a higher return when compared to our Ivy Five Portfolio, but it comes at a cost of more drawdown. In the end our portfolio returns a respectable 14.7% CAGR while the benchmark returns a 7.0% CAGR. We double our returns while significantly reducing drawdown.

So there you have it. The Ivy Ten trading system in a nutshell. It’s dead simple with some very nice returns.  The authors also demonstrate a 20-ETF portfolio that I may look at in a later article. For now, this should give you some insight to how to mimic the returns and low drawdown of the best Ivy League schools. Can you trade this in your retirement accounts? Well, that’s up to you but I’ve been seriously considering something like this. I find the simplicity and monthly balancing very convenient. Fundamentally, I’ve always liked the idea of momentum investing and knowing when to exit a position during a bear market. This seems to do a decent job of capturing these two aspects rather well.

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Ivy Portfolio Book Cover


About the Author Jeff Swanson

Jeff is the founder of System Trader Success – a website and mission to empowering the retail trader with the proper knowledge and tools to become a profitable trader the world of quantitative/automated trading.

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