A common concept floats around stock market investors that risk and reward go hand in hand. Blue chips and income stocks carry less risk with a reduced upside potential, and small-cap high-growth stocks could double your investment quickly or just as quickly squander your capital.
This is the idea behind a beta: your risk/reward is linked to how leveraged (or de-leveraged) to the market your stock is. This concept has come under attack lately.
The abstract "Estimation of Expected Return: CAPM vs Fama and French," has the showdown between two styles of accessing risk, and neither are cast in extremely good light when trying to estimate future returns based on said risk. (Note though that the Fama French model is meant for a portfolio of value stocks and not for single stock risk analysis)
Instead of trying to fight for either camp, we will use the novel approach of using analyst estimates. (You might also like to read about "Growth Stocks That Analysts Love.")
How Analysts Estimates Can Lower Risk
The working abstract is called Analyst Forecasts and Stock Returns by Ang and Ciccone (2001). What did their empirical data reveal?
- Stocks with lower variance (dispersion) between analyst estimates perform better than ones with high variance (dispersion).
- Stocks with less error in forecasts (low standard deviation with earnings surprises) also had higher stock performance.
- Transparent stocks (as set out by two criteria above), outperform stocks with wide analyst forecasts and high error by 13.12% per year. Positive excess returns between these two types of stocks were witnessed in every year this study was carried out.
- This transparency phenomenon is true across all stock types and sizes (value, growth, small, big). It is not related to industry, liquidity, momentum, or price-to-book.
Why It Might Work?
The researcher is unclear why this is so. But glibly, it makes sense that if you have many analysts covering a stock that agree on a target, and historically have been able to hit pretty close to a bulls-eye, and future forecasts are positive, then investors could calculate returns confidently. The future risk would be reduced. It's an odd concept to base our stock recommendations on the analysts that cover them, but this is in fact what the paper suggests.
Furthermore, a company that is highly visible might be prone to communicate negative earnings well in advance thus preparing analysts and investors of a downturn. They could employ "income smoothing" techniques to meet analyst forecasts and pad less favorable times, which creates the illusion of less risk (but is merely averaging out earnings for a more stable appearance).
The net effect is lower risk for your reward, and this goes against the principle of beta. Of course, more research in this area is necessary as the paper suggests.
Scanning for Low Risk Stocks
To scan for suitable candidates I use the Zacks stock screener.
- We need at least 2 brokers to calculate dispersion and error.
- Average EPS surprises last 4 quarters is less than 10%.
- Annual EPS estimates with relatively low standard deviation.
- Long-term growth estimates will be above 20% per year.
- 75% or more of the ratings will be "buy" or "strong buy."
- The stock must have higher relative strength than the S&P 500.
The "Reduced Risk" Stock List
- (ALXN) – Alexion Pharmaceuticals, Inc.
- (KFRC) – Kforce Inc.
- (LNDC) – Landec Corp.
- (MG) – Mistras Group
- (PCLN) – Priceline.com
- (SLB) – Schlumberger Lt.
Keep in mind that some of these stocks have beta values below or above 1, but we do not equate that with high or low risk based on our "analyst estimates" approach. If the analyst transparency theory holds true, then even isolated stocks with high betas could be of lower risk. This analyst error and dispersion method ignores beta and judges total risk in a new light.
After you are finished reading the abstracts, I would be very interested in your opinion on the Fama French vs. CAPM vs analyst transparency theories.