Do you want your Growth at a Reasonable or Unreasonable Price?

We show you how to improve on the basic GARP strategy by creating a model returning 18% a year.

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Written by Liam Flavelle on 22 August 2017

  • I examine a classic trading strategy and find some worrying definitions.
  • The two key factors traditionally associated with GARP investing are not enough to make a sustainable trading strategy.
  • I show you how to create a superior trading model that has returned 18% a year over the last 17 years.

Growth at a Reasonable Price, or GARP, is an investing style pouplarized by Peter Lynch, the legendary Fidelity manager. A GARP strategy looks to buy companies that show consistent earnings growth combined with a low relative price. A GARP strategy on paper looks to create a portfolio with the perfect blend of value and growth stocks.

But how do you define a GARP trading strategy? A Google search reveals that most commentators believe all you need to do is buy companies with a PEG ratio of less than 1, that have grown their profits recently and have a low price to book ratio.

The PEG, or price/earnings growth, ratio is calculated by dividing a company's P/E ratio by the estimated future earnings growth rate. If we take Occidental, its PEG ratio is 1.09:

Close 59.15   
EPS (last twelve months) 0.13   
Estimated EPS (next annual return) 0.67   
P/E 455.00  
Earnings Increase 415.38%
PEG Ratio 1.09   

A PEG ratio of less than 1 can indicate that a stock is undervalued, and that if the company continues trading in line with its growth rate estimate then its stock price will increase.

As usual, we have used the platform to backtest our trading ideas. Further graphs, charts and statistics, including position data, can be found by clicking here. To access the other versions of the model mentioned in this article, simply click on the history button in the left menu and select the desired version to view.

To kick off, let's see how a basic GARP strategy would have worked out. We'll assume the following rules:

  • We will start with US $10,000 in cash.
  • We will hold a maximum of 10 stocks at any one time.
  • The strategy rebalances its stocks on the first day of each month.
  • Each transaction will include a flat fee of US $7.
  • At each rebalance point, stocks with the following features will be selected for consideration:
    • Common stock or depository receipt.
    • Market cap greater than US $150m.
    • Price greater than US $2.
    • PEG less than 1. 
    • EPS Growth over the last 8 quarters of 15% or more.
  • We will buy the top 10 stocks that have the lowest PEG ratio and Price to Book value.

So, how would our basic strategy have fared over 17 years? Here are the backtested results:

Basic GARP Equity Curve

A 15% return isn't a bad headline figure with the strategy comfortably beating the S&P 500 which returned 3% a year over the same period. A couple of things stand out however - the graph seems to have a lot of whipsaws in it, and the maximum drawdown (Max DD, or the maximum peak to trough reduction in our equity) is an eye watering 73%. The S&P 500 had a maximum drawdown of 57% over the same period.

The drawdowns graph highlights the problem - the strategy dropped heavily in 2007-8 and didn't recover its highs until mid-2013. The oil crisis and taper tantrum also took its toll, with the model dropping by 30%. 

Basic GARP Drawdowns

Theme parks can be fun, but as investors we tend to try to avoid these sorts of roller coaster rides. So what can we do to tame the volatility on our model?

New, Improved GARP

In my research I have found a very simple way of improving our returns whilst reducing the risk we are exposing ourselves to by following a GARP strategy. 

At the moment our strategy ranks our selected stocks by their PEG ratio and Price to Book and selects the top 10 ranked stocks to open positions in. If we simply add the following 2 factors to our ranking:

  • Yield
  • Net Current Asset Value (or Current Assets - Total Liabilities - Preferred Stock)

We also flip round the sorting direction of the PEG ratio - the prevailing wisdom is that you want to choose stocks with the lowest PEG ratio. However in a previous study (To P/E or not to P/E) I found that this is not necessarily a great strategy. Choosing stocks with a PEG ratio closer to the top end of our requirements reduces our risk, which is exactly what a GARP strategy is supposed to do - we are not looking for dirt cheap valuations, just reasonable ones.

If we make one final change to allow our universe to include companies with a PEG ratio of less than 1.1 we come up with the following results:

InvestorsEdge GARPThe headline returns are now 18% a year for our 17 year test period, with the maximum drawdown reduced by a massive 20 points to 52%.

If you drill into the model's returns (click on the link above to see more details of the simulation on the InvestorsEdge website) you can see that our returns are two thirds capital gains and one third dividends and other distributions - indeed, our average annual yield would have been 5%.

Just comparing the equity curve graphs visually shows us that the equity curve is a lot less volatile, a fact confirmed when we examine the drawdowns chart:

InvestorsEdge GARP Drawdowns

Over the backtested period our drawdowns get to around 20% before reverting to growth. Even during the financial crisis our strategy would have returned to growth after 18 months.

Finally, we can see that the strategy would have typically invested in small- and mid-cap companies, i.e. companies with enough liquidity to enable us to sleep well at night:

InvestorsEdge GARP Market Cap

Recent Performance

So is this model robust enough to trade with? One way of finding this out is to examine the model's rolling 3 year returns. This tells us how profitable the strategy would have been if we had run it for each of the 15 3-year periods going back to 2000:

Period CAGR
2000-2 9%
2001-3 16%
2002-4 15%
2003-5 28%
2004-6 25%
2005-7 13%
2006-8 -6%
2007-9 -8%
2008-10 9%
2009-11 17%
2010-12 14%
2011-13 17%
2012-14 25%
2013-15 20%
2014-16 13%

What this table shows us is that if we had started using the system at any time in the last 17 years it would have made money, with the exception of investing during the financial crisis (2006-8 and 2007-9).

Would We Trade This Strategy?

For us to execute a trading strategy in real life, we need to have good reason to trust that it will continue to be profitable. To help us decide the likelihood of this, we have four-high level tests that our model should pass:

  • Investable - The strategy should be tradeable in real life and should scale. Our backtests include trading fees, so frictional costs are already taken into account. Pass.
  • Intuitive - There should be logical risk- or behavioral-based reasons why the strategy works. Our model invests in companies with high historical and estimated EPS growth, high yields and assets and that are cheap relative to their book value - all logical and understandable factors. Pass.
  • Persistent - The factors involved should work over long periods of time. Our tests have concentrated on the 2000-17 period. From an academic point of view, this would not be long enough to prove persistence. However, from our point of view at InvestorsEdge, we consider going through two major market shocks and a series of smaller downturns as enough to convince us the strategy works on a long-term basis. Pass.
  • Pervasive - Pervasiveness is more an ideal than a hard rule. Our model should work across countries, regions and sectors. Whilst running our strategy on companies from other countries didn't result in the same returns, we consider that a strategy that works across multiple time frames running in the country with the largest and most liquid set of exchanges in the world is acceptable. Pass.

Your Takeaway

Basing a trading strategy on prevailing wisdom and a few expert opinions without first checking how such ideas have performed in the past can be a dangerous idea. The basic GARP strategy, whilst showing acceptable headline returns, would have been a very hard trading strategy to follow because of its volatility.

What we have seen is that a few sensible tweaks can dramatically reduce this volatility whilst elevating our returns to 18% a year over the 17 year period we tested. Furthermore, while nothing is ever certain in the future, we have shown that the ability to backtest our trading ideas allows us to base our trading strategies on ideas that have actually worked well over multiple historical time frames and provides a sense of surety that they could well continue to do so.


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