# FCF Prediction: A Simple Method To Generate A Fairer Free Cash Flow Estimate

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Includes: DIA, IWM, QQQ, SPY
by: Dividend Drive

## Summary

Free cash flow is, for me, a critical aspect of any analysis of a company.

Unfortunately, this means trying to predict future free cash flow generation.

Here, I outline my simple, straightforward method for producing a fair (though far from perfect) free cash flow prediction.

Cash flows are really my modus operandi. They represent the key focus for most of my analyses and are core to any investment decision I make. Yet as part of this it means that I have to deal with predicting cash flows. Cash flows are notoriously erratic. This volatility is more pronounced in some industries than others.

Some companies offer investors guidance for the year ahead which included FCF guidance. Yet this is the exception rather than the rule. We are therefore left having to try and fathom FCF predictions ourselves if we want them on a consistent basis.

I have developed my own method to do this. Those of you who read my piece on how I compare the credit ratings strength of companies know that I prefer simple methods where possible which require little or no direct input from me. This is true with regards to my FCF predictions.

So how do I do it?

The Basic Method

For some time I utilized a very basic method indeed which eventually formed the foundation of my current model. This basic method involved simply calculating the FCF/Revenue ratio for each of the last five years. I would then generate the average FCF/Revenue ratio from this period and multiply the predicted revenue figures for the company in order to generate my predicted FCF figure.

In essence, this worked. Let's see this with an example using "Company A."

Company A started with a revenue of \$1,000 in Year 1 which grew 10% each year to Year 5. Company A also increasingly generated a higher proportion of FCF from its revenue, leaping from 9% in Year 1 to 12% in Year 5.

In the future, analysts expected to see revenue continue to grow at 10% for the next two years (Year 6 and 7):

This is all well and good. Averaging the FCF/Revenue ratio for the last five years provides us with an average which offers a conservative 10.2% FCF/Revenue prediction for future revenue predictions. FCF predictions of \$164 and \$181 for the next two years come out.

Yet here is the catch. Let's take "Company B" to show why this method was just too simplistic. Company B is exactly the same as Company A. The only difference is that the FCF/Revenue ratio deteriorated as the previous five years progressed. The average still came out at 10.2%, but Year 5 saw a FCF/Revenue figure of just 9%:

With the revenue and FCF/Revenue ratio average exactly the same, needless to say the FCF predictions for the same revenue growth is the same again. Yet this is not a conservative FCF prediction but a generous one. After all, last year they were only able to manage to convert 9% of revenue into FCF not 10.2%.

This basic method, therefore, penalized companies which had improved their revenue to FCF conversion rate whilst flattering those that had done the reverse.

How to solve this?

The Enhanced Method

Some adjustments needed to be made as a result. But how? I needed to find a method which emphasized any improvement or deterioration in FCF/Revenue ratios over the course of the last five years.

I achieved this by utilizing the average FCF/Revenue from the last five years (hereafter, "5Y") but also tying it to last year's FCF/Revenue figure (hereafter, "LY"). In the process I also sought to emphasize the influence of last year's FCF/Revenue performance.

I therefore utilize this formula:

FCF/Revenue Prediction = (5Y + (LY * 2)) / 3

Last years' FCF/Revenue ratio therefore has twice the influence on the final predicted FCF/Revenue ratio than that of the last five years. Consequently, Company A sees its predictions change to this:

The new predicted FCF/Revenue figure is now 11.4% rather than 10.2%. Closer (though still modestly below) that experienced in Year 5. Yet a fairer reflection of their likely FCF conversion rate.

Consequently, \$20 has been added to the basic methods FCF prediction in Year 6 and \$21 to Year 7.

And what about Company B? Well the opposite happily occurs here:

Rather than the 10.2% FCF/Revenue prediction we have a new, lower 9.4% prediction. Consequently, FCF is predicted to be \$13 less in Year 6 and \$14 in Year 7.

Conclusion

Clearly this method remains far from perfect. The enhanced model of FCF predictions still tends to slightly flatter recent underperformers and penalize recent overperformers. Yet it does, for the most part, correct for this. What is more, it generally provides pretty fair and surprisingly accurate assessments of FCF potential. Certainly it provides enough detail for me to utilize it to gauge a company's fair value judged by its enterprise value to FCF valuation.

For me, this enhanced method of predicting future FCF figures handles the job pretty well. Yet I am very interested if anyone else uses an alternative method or has any proposals for how the method may be improved upon. I am always on the look-out for way to refine my methodologies, so please do share your thoughts. Also, do consider clicking the follow button at the top of this page if you'd like to read future articles where I put my FCF analysis into action.

Notes

Unless otherwise stated, all graphs and the calculations contained within them were created by the author. Creative Commons image reproduced from Flickr user seanfx.

Disclosure: I/we have no positions in any stocks mentioned, and no plans to initiate any positions within the next 72 hours.

I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.