Building A More Reasonable Growth Strategy

by: InvestorsEdge

Summary

We respond to a challenge from one of our readers to improve our GARP strategy.

Adding a handful of extra factors can make a big difference to your returns.

Our new strategy shows that a GARP strategy would have been a successful one to employ over the last 17 years.

Our last article, Do You Want Your Growth At A Reasonable Or Unreasonable Price? demonstrated a trading strategy that invests in companies showing historic and estimated growth that traded at a reasonable price (a GARP strategy). This strategy showed 18% annual returns over the last 17 years with very little volatility and has made a good addition to our book of robust and profitable trading models.

One of our readers, shaxmatist1, pointed out that our strategy relied on higher P/E to Growth (PEG) ratios, which could theoretically push the model to invest in higher growth securities over and above the safe, boring companies it should be selecting.

Whilst we believe the safeguards within the strategy will prevent that from happening, shaxmatist1 does make a good point that preferring higher PEG ratios does reduce the model's potential returns. A PEG ratio of 1 indicates a fairly valued company, whilst less than 1 shows a company may be undervalued. Our strategy works on the premise that a firm's PEG ratio will mean-revert to 1 over time.

This means that our current GARP strategy, by over-weighting securities with higher PEG ratios, trades a reduced potential return for a less risky investment, which will suit some investors. But what if you want to take a little more risk? Can you improve your potential returns by selecting companies which have more room to grow without taking on too much risk? We have come up with a strategy to do just that.

A Better GARP Strategy

As usual, we have used the InvestorsEdge.net 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.

Our improved strategy gives us the following simulated results when backtested over the last 17 years:

Equity Curve Our new model shows a big improvement in annual gains - 29% compared to our original strategy's 18%, while the risk indicators (the Sharpe and Sortino) are also showing healthy increases. Volatility has crept up during times of market turmoil, so you can see that drawdowns following the market dips of 2002-3 and 2011 are more pronounced:

Drawdowns The strategy would have invested in predominantly small companies (those with market capitalizations of between US $250m and $2bn), so liquidity levels would have been adequate to trade our model:

Market Cap

How does the Strategy Work?

The strategy is based on two key building blocks - collating a universe of stocks and then ranking them to sort the most desirable companies to the top of our list. Our basic parameters are:

  • We will start with U.S. $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 U.S. $7.
  • We will only consider common stocks or depository receipts with a market cap greater than U.S. $150m and a price greater than U.S. $2.

At each rebalance point, our strategy's universe needs to consists of companies that are sound, growing and priced below fair value.

  • Our fair value is derived from the PEG ratio - we will only consider stocks with a PEG of less than 0.9. We are hoping that companies selected, who exhibit a low P/E compared to their growth prospects, will experience an increase in price as the market realises the stocks are mispriced.
  • Our growth check forces us to only include companies in our universe that have shown EPS Growth over the last 8 quarters of 10% or more and an increase in Gross Income over the most recent quarter.
  • Our soundness test checks that a company's Net Current Asset Value (NCAV, or current assets less total liabilities and preferred stock) is greater than US $-750m. This selects stocks that are not overly indebted.

Once we have a list of stocks to consider buying, we need to sort and rank them on the following factors:

  • Yield, higher is better (44.4% weighting).
  • Price to Book Value, lower is better (22.2% weighting).
  • Price to Free Cashflow, lower is better (22.2% weighting).
  • PEG ratio, lower is better (11.2% weighting).

The aim of the ranking is to sort our universe of stocks so that the ones most likely to increase in price rank highest. Our research has shown that, with our stock selection, a company's yield is the largest factor affecting future returns, followed jointly by price to book and free cash flow ratios. The PEG Ratio, whilst a major deciding factor in including a stock in our universe actually only contributes a small amount to our ranking.

Once we have ordered our universe, we then buy the top 10 companies with the highest overall rank.

Will the Strategy Work in the Future?

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 18%
2001-3 41%
2002-4 51%
2003-5 52%
2004-6 31%
2005-7 20%
2006-8 4%
2007-9 19%
2008-10 26%
2009-11 34%
2010-12 17%
2011-13 32%
2012-14 40%
2013-15 25%
2014-16 23%

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 over a 3 year period, including during the financial crisis of 2007-8.

What are the Risks?

Investing in any trading plan involves taking on risk, and this strategy is no different. We believe that the model has 3 key risks to you as an investor:

  • Specific to designing a strategy using historical data, a key problem can be curve fitting where a model works with historical data but fails under future market conditions. We mitigated this risk by designing the strategy using an in sample dataset of January 2013-April 2017 and only checking against the full dataset once we were happy with our in sample results. Additionally, the 3-year rolling tests also also provide a series of small samples that individually perform well and provide further evidence that we have developed a robust trading system.
  • The strategy may underperform, as traditional value strategies have over the last 2-3 years. It is also not unknown for models to become less successful as more people use them (Joel Greenblatt's Magic Formula is a good example of this phenomenon). There are no guarantees in investing, but the factors we have used in deciding what to trade and when are simple and logical, and the rolling returns provide some level of comfort that even during the 2007-8 crisis the system would have made money for the patient investor.
  • This strategy typically opens positions in companies with market capitalizations of US $250m - $2bn and may become illiquid very quickly in a financial downturn. The backtester used incorporates a liquidity check to ensure there should have been ample trading volume to enter and exit positions, but the bottom line is that this strategy trades in companies that may be too small for some investors to feel comfortable with.

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. Increasing our starting capital to US $1m also results in the same strategy returning 22% a year, so we have room to grow and still make decent returns. Pass.
  • Intuitive - There should be a 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 and free cash flows, and that are showing improving gross income figures - 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 (Canada was the best at 15% CAGR), 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

This version of a Growth at a Reasonable Price strategy has historically delivered robust and consistent compound annualised returns of 29% over the last 17 years. To do that it takes on a little more risk during market downturns when compared to our previous version, however, the returns have always reverted to an upwards trend during those periods.

I will be tracking this strategy in real time from 1st September and will provide monthly updates on its profitability, in tandem with the lower-risk version from the previous article.

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it. I have no business relationship with any company whose stock is mentioned in this article.