Written by Liam Flavelle on 8 June 2017
- Chasing the highest yielding stocks in the market is a foolhardy way of making money.
- What is a 'safe' yield to target?
- This is the first in a series of articles where we build a winning dividend strategy.
We have all been taught as investors that reaching for yield, or more formally the pursuit of higher yielding investments without regard to the added risks incurred, is dangerous to your wealth. Indeed, we have solid evidence for this in the financial crisis of 2007-8 whose catalyst was investors hunting for ever higher returns bid up the value of mortgage backed securities.
In this the first of a series of analysis of dividend-focused strategies, we will examine the risk factors involved in buying high-yielding stocks and identify the yield sweet spot that you as an income investor should be targeting to balance risk against reward.
Reaching for Yield
So, let's start off by seeing how reaching for yield would have worked out as a strategy in real life. We'll use the InvestorsEdge.net platform to backtest the following trading rules:
- Our tradeable universe will include all common stocks and depository receipts (ADRs) in the U.S. that have returned cash (in the form of dividends or distributions) to shareholders within the previous 12 months.
- We will rebalance our portfolio on an annual basis.
- At each rebalance point we will buy the 10 stocks with the highest trailing yield.
- Each transaction will cost a flat fee of $7.
- We will simulate using a Market On Close order to buy stocks at their closing price on the next trading day after the rebalance.
Here's how the model performed, benchmarked against the S&P 500: (click on the picture to view all the model results):
Our strategy would have returned 6.5% annually, compared to the S&P 500's return of 3% - better than we had anticipated before running the backtest. The average annual yield for the strategy was 11%, which sounds great but was accompanied by large and unhealthy doses of volatility.
Once you drill down to see the individual trading positions bought by the system it becomes obvious that we would have been fortunate to make any money at all - of the 164 positions entered, over a third show greater than 50% losses and the overall ratio of winners to losers (the win rate) was 37%!
So, what makes this strategy so risky? It comes down to fundamentals and market capitalization.
Firstly, the market capitalization chart above shows the mix of companies broken down into their size - Nano stocks are those with a market capitalization less than $50m, and typically display the highest price volatility out of all the categories.
Secondly, since the yield calculation itself is derived by dividing a stock's dividends per share by its share price, a company's dividend yield can go up because the amount of dividends it pays has gone up or its share price has gone down.
When you examine the mix of companies in our theoretical portfolio, you can see that the majority have problems that have negatively impacted their share price. In most cases those share prices have continued their descent after we bought them. In other words, a high dividend yield is more often a sign of junk-status than opportunity.
So, if a high yield doesn't provide us with a correspondingly great reason to buy a stock, what is a safe yield to reach for?
This chart shows an analysis of our returns if you amend our strategy and apply a maximum yield of between 1% and 10%.
The most obvious effect of limiting the yield is that all the models offer higher returns, which we expected since limiting the yield also reduces the number of higher risk stocks in our universe. The equity curves themselves also show an improvement in their volatility and when you drill into the models the predominant company size each version invests in is now Small (with values between $300m-2bn).
The standout winner of the bunch is picking companies yielding 5% or less:
Choosing stocks yielding 5% or less would have returned us 14% annually for the past 17 years, with a win rate of 60%. As you can see below the drawdowns (the peak to trough decline of the portfolio value) were less than 20% for most of the time, except for 2002 (the dotcom recession), 2008 (the great financial crisis) and 2015 (the oil crisis and taper tantrum). Interestingly the model was seemingly unaffected by the 2011 Euro crisis; we believe the reason for this was that the U.S. acted as a safe haven for foreign funds fleeing fears that the Euro was about to fail as a currency.
Drilling down into the details of the results, the strategy shows an average annual dividend yield of 4.35% and a loss in only 4 of the 17 years:
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. A key concern is in the scalability of the strategy as our backtest has a very small starting cash of $10k. However, increasing our initial investment to $100k and $1m would have returned 14.5% and 11.5% annually respectively. Increasing the number of stocks bought at each rebalance also showed an annual return of 15%, so Pass.
- Intuitive - there should be logical risk- or behavioral-based reasons that the strategy works. This model is really an analysis into where to focus our investigations and identify a yield sweet spot - no consideration is being made to select companies that are mis-priced compared to those that are junk. Fail.
- Persistent - The factors involved should work over long periods of time. While our tests have concentrated on the 2000-17 period, other research (including those highlighted in Ploutos' Seeking Alpha article The Updated Charts All Dividend Investors Must See) has found similar behaviors in studies from 1928 onwards. Pass.
- Pervasive - Pervasiveness is more an ideal than a hard rule - our model should work across countries, regions and sectors. A test across a number of other countries show that all except Switzerland show a similar set of returns and risk profiles.. Pass.
The intent of this study was to identify a sweet spot that offered the best risk/reward tradeoff when investing in high yielding stocks, which we have seen to be stocks in the U.S. yielding 5% or less.
While a trading strategy that returns 14% annually is nothing to sniff at, we believe that for a strategy to be considered robust it needs to include multiple factors. The next article in this series will endeavor to identify which other factors can be used to separate the quality high yielding companies from the junk.
Think you can improve on the model? Just Sign Up for a free account at InvestorsEdge.net, create a copy of the strategy and add your own ideas. See if you can beat us!