Earnings surprises occur when a company reports earnings dramatically above analyst estimates.
We have identified 4 simple factors which when combined create a successful strategy returning 20% annually since 2010.
Our strategy also performs well during periods of extreme financial stress.
A positive Earnings Surprise occurs when a company’s reported quarterly or annual profits are above analysts expectations. We are told that earnings surprises can have a dramatic impact on a company’s stock price, and the logic of this argument is difficult to argue with – companies are often valued at a multiple of their expected profits, and if those earnings estimates are exceeded then investors valuations will also increase.
As usual I would rather base my investing decisions on evidence rather than suppositions and have been investigating this phenomenon. In the process I have discovered a very simple strategy that has been remarkably successful over the last 18 years.
If you’d like to jump straight in and see the backtested results of this strategy, you can do so by clicking on this link
Regular readers of my blogs will know that I use a four-step process to backtests trading ideas:
Step 1 – Build a universe
Our universe consists of all U.S. common shares and depository receipts with a market capitalization of US$ 50m or more that reported their latest quarterly financials on the previous working day.
The companies selected must also have earnings per share more than 15% above consensus analyst estimates and have shown positive price momentum over the last 10 working days.
Step 2 – Define how our strategy rebalances
Since this strategy is designed to scan the market for new earnings surprises it works best rebalancing every day, with a maximum number of 10 stocks held at any one time. For testing purposes we have selected a starting cash budget of $100,000.
Step 3 – Rank our companies
Each day we sort our universe of stocks by their earnings surprise – in this case we calculate the surprise factor by dividing earnings per share by the analyst consensus estimates for the same period. This ensures that when one or more slots are available the companies selected are the ones that have shown the biggest earnings surprise.
Step 4 – Create buy or sell rules
Our stocks are sold once we have held them for over 15 working days, and each transaction attracts a commission of $7.
How the strategy works
Each day our strategy closes any positions that have been held for more than 15 working days and then checks for U.S. stocks that yesterday saw an earnings surprise of 15% or greater and have shown positive price momentum over the last 10 days. It will then fill any available positions, up to a maximum of 10, with the securities that have shown the greatest earnings surprise.
There is only one check and balance in this strategy – only stocks with enough liquidity at the time will be opened. I have set the system to only buy stocks when we are opening position sizes less than 5% of the average volume * price traded over the last 20 days, ensuring that the backtester only simulates trades when there is a good likelihood that the required shares could have been bought at the time without dramatically affecting the market. This is particularly important in strategies such as this one that focus on small-cap companies and smaller.
Here are the backtested results over the last 8 years, which show a strong performance from our strategy:
Click image to view the strategy
As you can see, the strategy’s blue equity line comfortably beats the benchmark and would have returned 20% annually. The maximum drawdowns for this period, which encompasses the Euro crisis of 2011, the oil price drop of 2014 and the taper tantrum of 2016, are remarkably low at 21%. In fact if you examine a backtest that covers the 2008-9 financial meltdown you will see a maximum drawdown of 35%, compared to around 55% for the S&P500 at the time. I rarely see drawdowns at this low level for this time period that don’t incorporate some serious timing elements, so to see it on such a simple strategy is a big tick in the box.
Our annual performance chart shows a fairly consistent series of returns (rather than one huge year and lots of poor ones), which is positive. We can also see that the average company that the strategy invests in is small (between $250m and $2bn).
What could possible go wrong?
My biggest concern with this strategy is over one measurement – Profit Factor. Profit factor is defined as gross profits divided by gross losses, so is a pretty simple measurement. Our profit factor for this strategy since 2010 has been 1.39, so for every $139 we make in profit we make a $100 loss, far below the magic number of 2 that most traders prefer as a minimum.
This goes some way in explaining the difference between the equity curves of the strategy and its benchmark – while the benchmark is fairly smooth our strategy is ragged and volatile.
This concern is tempered by the reduced drawdowns – yes, you are making less profit for each dollar risked but you are in and out of the market quickly and only investing in companies that have exceeded analysts expectations, so it would seem logical to assume that such companies are less likely to be affected by a drop in the market and therefore entail less risk when it comes to owning them.
My second concern is the level of turnover inherent in the strategy – a very high 1,250% annually, meaning that each position is swapped out around 12 times a year on average. High turnover goes hand in hand with high commissions, so using a broker that minimizes your trading costs is essential to the success of this strategy for investors with smaller pots to invest with.
Earnings surprises are a well documented phenomenon that can be easily understood and acted upon by investors, so its no surprise that such a simple system should be so successful. If you can stomach the increased risks that come with lower profit factors and are willing to trade on a daily basis with limited holding periods then this could well be a good strategy for you.
I’ll be continuing this series of blogs and investigating if the earnings surprise factor works in other countries, and also seeing if there are any other factors that can be used to enhance backtested returns.