Who doesn’t love getting a deal? I know in my family we use coupons, shop at wholesale clubs and use Amazon’s ‘Subscribe and Save’ feature all to reduce costs. We know the items we want and we want them at prices below what most can find them. Why shouldn’t this same sentiment follow in the investment area of our lives? This very question that I want to discuss and noodle through. It is my hope that we will be able to find companies (stocks) that are on sale. I know there is no substitute for doing your homework (like reading annual company reports), but I would like to be able to have a data-driven way of identifying companies on sale.

Before we can know if a company is on sale, we must first know what it is worth. Valuation, in and of itself, has many different answers and methods. Tons of books are written on this subject like McKinsey or Damodaran. In the spirit of keeping things simple, because we are looking for glaring errors and not minute differences, I am using a Discounted Cash Flow model. The Discount Cash Flow model is V = FCF / (r-g) where:

V = the value of the company

FCF = the Free Cash Flow or ‘dividend’ you get from the company

r = the discount rate or interest rate required by the investor

g = the growth rate of the company

I have chosen Free Cash Flow because it is the theoretical amount of money that you, the business owner, could withdraw from the company every year. Still, it is an assumption and one that I am going to discuss during the rest of this article to see if using FCF is appropriate.

This model is effectively valuing a company as a perpetuity and is almost identical to the Dividend Discount model . Given that this model is built upon the assumption of long-term payout, we want to use on companies whose long-term prospects look good. This form of valuation is not meant for speculation.

With this formula, we now have a starting point to value a company. For example, let us look at Disney’s (DIS) 2016 Annual Report. Looking at their Cash Flow statement we can see that their Free Cash Flow is $8.4B (Cash from Operating Activities — Capital Expenditures). Now if we were to make up some numbers for our variables r and g of 10% and 3%, our answer would be V = 8.4 / (0.1–0.03) = $120.5B. In this scenario, we are valuing Disney at $120B. Granted, we should validate the interest rate we require (r) and the growth of the company (g) before we consider using this number. That discussion is outside the bounds of this article.

Now that we have walked through an example of how valuation might be derived, I want to discuss the how to find companies that would work well with this methodology. First, let’s look at how Free Cash Flow tracks to Market Capitalization (the price the company is being sold in the market) for all the public companies in the US since 2001.

While there appears to be some trend in the data, there are also many data points that do not follow any trend. We color code everything based on Return on Capital Employed (ROCE). This allows us to see that this metric appears to be a discriminator for those companies whom we will attempt to predict its value.

At this point, let us look at ROCE to understand what it is and how it can be used. ROCE is calculated as Assets — Current Liabilities. We leave out Current Liabilities because the company needs to pay its bills and would not be available to be deployed elsewhere. This metric is attempting to figuring out how much money the company makes based on how many resources they have at their disposal. Think of the interest rate you have on your bank account. In your savings, your money makes money based on the going interest rate. We can use ROCE just like shopping around savings interest rates when we choose a bank. ROCE will help us understand how well the company is using the resources the owners have given them.

Getting back to the analysis, we filter the companies to find based on those who have:

Residence in the US Positive Cash Flow ROCE >= 10%

Note We chose a ROCE greater than or equal to 10% to focus on higher performing companies.

Our data now looks like:

We now have a predictive model that shows FCF is a good predictor of Market Capitalization. For those who care, the stats look like:

Model — Market Capitalization = 13.3604 * FCF

R2 = 0.86

P-Value < 0.0001

The main takeaway about the stats is that the model is statistically significant and does a good job of fitting the data.

We have shown that FCF is the right metric to use for determining a company’s value since it tracks well with Market Capitalization. We can now combine FCF with our valuation method mentioned above to begin the process of determining if a company is over/under valued. Thus, we can ‘stock’ up on companies we like when they go on sale just as we would with any of our favorite drinks, snacks or toothpastes. The only difference is that this kind of shopping, if done right, will pay us back for years to come.