The Value Trap Conundrum – And Six Criteria to Help Deal With It

The Value Trap Conundrum – And Six Criteria to Help Deal With It

By Jack Forehand, CFA, CFP® (@practicalquant) —

Value traps are the bane of a value investor’s existence.

We build our strategies around buying cheap companies and hoping the market will eventually see things the way we do. And sometimes it eventually does. But other times not only does it not eventually see things our way, things actually get much worse. Those types of companies are what we refer to as value traps. These are stocks that looked cheap, but it was a mirage.

Value Traps in an Academic Framework

Now that we have gotten the simple explanation of what a value trap is out of the way, I think it would also be useful to look at this using an academic framework.

Value investing works for two reasons (at least most people think so – some question the second). The first is that value is a risk factor. That means that value stocks generate an excess return because they are riskier. The second is what we call the behavioral explanation. The idea is that market participants systematically overreact to the problems with value companies, pushing their prices down more, on average, than is warranted by the fundamentals.

 I seriously doubt Eugene Fama has ever read my work, but if this happens to be his first time, he will likely stop reading now. The reason is that many who believe in market efficiently don’t buy into this second explanation. They focus purely on the first.

So why did I just go through all of that in an article about value traps?

In addition to the simple explanation of what a value trap is, I think it is also important to think about why value traps exist using that framework. The key words I used in the above paragraph were “on average”. Those words explain why value traps can exist in a world where investors get paid to take on the risk of value stocks and they are also often mispriced. Those two things occur across a wide number of stocks. Within that larger group, there will be a wide range of outcomes. Some companies will be much better than the average. Some will do much worse. That second group are the value traps.

Let’s Banish These Horrible Traps Forever

I hate to lead a discussion on the criteria that we have found can help with value traps with some bad news, but I am a value investor, so I guess bad news is kind of my thing.

One of the most important things to keep in mind when you are looking at any system to manage the problem of value traps is it will likely only help on the margin. You can’t get rid of value traps.

As value investors, we are buying stocks the market does not like. We are buying businesses that usually aren’t great. There are bound to be some problems in there. What we are trying to do with a value trap system is to make some incremental improvements without eliminating the reason we own value stocks in the first place, which is their value.

Positive vs. Negative Quality

One easy way to manage value traps is to just look for characteristics of high-quality companies among value stocks. For example, I could look for high returns on capital, consistent sales and earnings, healthy balance sheets etc. That is a perfectly reasonable way to invest. But the problem with it is that those types of companies usually aren’t cheap. So by adding those criteria I am reducing my exposure to value.

A second method to try to limit the impact of value traps without making this tradeoff is to use negative quality. With negative quality, instead of looking for the best companies, I am simply trying to get rid of the worst ones. When we look to limit value traps in our deep value models, this is the approach we use.

Six Criteria to Help Manage Value Traps

As value investors, we are relying on past fundamental data to select our investments. Regardless of which metric we use, we are comparing the price of a stock to its previously reported financial results. So when we look to limit value traps, we start by asking ourselves one simple question:

In what cases would past financial results be the least representative of the future?

All of our criteria flow from trying to answer that question.

Here are six criteria we have found helpful.

1) Earnings That Are Expected to Fall

Analyst estimates are notoriously unreliable. But despite that, they are usually directionally correct when large deviations are forecast. If something has dramatically changed between the past results we use in our valuation ratios and what is likely to happen in the future, estimates usually reflect that.

2) Cash Flows Not Keeping Up With Earnings

Cash flows are more difficult to manipulate than earnings. We filter out situations where cash flows paint a significantly worse picture of the business than earnings do.

3) High Debt

Debt magnifies everything. If a company has problems, and most value companies do, debt makes them more difficult to overcome. We screen out the companies with the highest debt using a composite of different metrics.

4) Deteriorating Fundamentals

We have a Twin Momentum strategy we run for Validea that is based on a paper by Dashan Huang. The strategy uses seven variables (earnings, return on equity, return on assets, accrual operating profitability to equity, cash operating profitability to assets, gross profit to assets, and net payout ratio) to calculate a firm’s fundamental momentum. We have found that inverting that and excluding the companies with the absolute worst fundamental momentum can help to reduce value traps.

5) Poor Economic Margin

For a company to be a viable entity, it should earn a return on capital that exceeds its cost of capital. If the reverse is true, providing a company with more capital doesn’t make sense. We filter out the companies with the biggest gap.

6) Low Relative Strength

We use this as a catch-all criterion to try to pick up companies that the other screens missed. If a company is at the absolute bottom of the market in terms of relative strength, often there is something very negative going on, even if it isn’t reflected in the other fundamental screens.

Bringing it All Together

Once we have ranked our universe using these six criteria, we then create a combined ranking that combines all of them, and the worst 10% of all stocks are eliminated. We aren’t trying to eliminate companies with slight problems with these variables. We are trying to eliminate the absolute worst offenders.

Value traps will never be eliminated, and any attempt to eliminate them will likely come with the consequence of also reducing exposure to companies that go on to perform well. Our goal with this system is to just try to achieve slight improvements at the margin. We think looking to eliminate the companies where past fundamentals are least indicative of the future is a sensible way to do that.

Jack Forehand is Co-Founder and President at Validea Capital. He is also a partner at and co-authored “The Guru Investor: How to Beat the Market Using History’s Best Investment Strategies”. Jack holds the Chartered Financial Analyst designation from the CFA Institute. Follow him on Twitter at @practicalquant.