Disclosure – I’m long Ubiquiti Networks, Inc. (UBNT).

Closing price May 22, 2015: $30.46

**The company and the business model**

From the company’s 10-K – “Ubiquiti Networks develops high performance networking technology for service providers and enterprises.” The service providers are typically Wireless Internet Service Providers (WISPs). “Our technology platforms deliver highly-advanced and easily deployable solutions that appeal to a global customer base, particularly in under-networked markets.” Manufacturing is outsourced, resulting in low capex. Sales costs are low as the company relies on an internet forum/community to evangelise the products, which allows disruptive pricing, while the pricing encourages the evangelism. Headcount is low and Ubiquiti are able to concentrate on R&D.

**About this piece**

This is an update to my previous piece, “Ubiquiti Networks – charting volatile revenue” February 18, 2015 (wordpress.com). I’ve written less in this update, making it easier to refer to the charts while accessing other material. I recommend reading this – “Ubiquiti Networks’ (UBNT) CEO Robert Pera on Q3 2015 Results – Earnings Call Transcript” May. 7, 2015 (seekingalpha.com). The CFO had left and only the VP of Investor Relations accompanied the CEO.

**Source**

I’ve used 10-Qs and 10-Ks from the company’s SEC filings page.

**Revenue and earnings**

In the next chart I’ve fitted trend lines, and the chart includes an explanation of why I don’t trust them for making projections. That tends to be my general attitude regarding trend lines fitted to financial results. If you want to get results such as “Standard Error of Slope” online, you can use the Linear Regression Analysis page on The Chinese University of Hong Kong. You’ll need to navigate via “Data modelling” and “Linear Regression”. Although the chart’s scale is in millions, the formulas are based on thousands, and that’s true for all the trend lines in this piece. There’s more about statistics in the blog post I linked to at the top.

**Cash flow**

“Cash from operations” includes changes in working capital which can sometimes be volatile. The investing cash flow accurately represents capex, consisting of only “Purchase of property and equipment and other long-term assets”.

**Revenue by regions**

The R-squared value of 0.753 means the trend line explains 75.3% of the variation in total revenue. The next chart uses the same data, but drops the total and puts trend lines on the regions.

**Product types**

My data for the current two product types starts from Q1 2014, and growth since then has been slower. Adding the coefficients in the trend line formulas (below) gets 3056.000-39.607 = 3016.393, which implies about $3 million growth in sales per quarter, which is about 2.0% based on Q3 2015 sales of $147,456 thousand, or 8.4% annually (although projecting an annual growth rate would contradict the assumption of linear growth implied by the trend line). The growth between Q1 2014 sales and Q3 2015 sales works out to $2,962 thousand per quarter, which is very close to the growth implied by the trend line formula.

**Correlations between regions and between product types**

The charts in this section help to answer the questions, how closely do sales in the regions move together, and how closely do the product types move together.

The data for regions runs from Q1 2012 to Q3 2015, and the general direction in the charts is from the bottom left to the top right. The slope of each trend line is given by the number in front of “x” in the trend line formulas, for example “0.524x” in the next chart means that for every dollar sales increase by in EMEA (Europe, Middle East and Africa), sales in North America increase by $0.524, on average, according to the line fitted.

The slope does not depend on the units (whether the data is in thousands or millions of dollars). The constant 6927.608 in the next chart implies that sales in North America would be $6,927.608 thousand (i.e. about $6.9 million) if sales in EMEA were zero. I do not regard that figure as accurate or meaningful, however I decided against forcing the lines to pass through the origin, which is sometimes done when there’s a good reason to believe that variables have an underlying relationship which is strictly proportional.

The red R-squared value of 0.206 in the chart above means only 20.6% of the variation in revenue from South America is explained by variation in revenue from EMEA in conjunction with the line fitted (the red one). Sales from South America are mostly independent of sales from EMEA, or in simple terms, even though both regions have trend lines that slope up, sales from South America can easily go down while EMEA goes up (which has happened in five of the last fourteen quarters).

The two regions where sales are the most in step with each other are EMEA and Asia Pacific, where 69.3% of the variation in one is explained by the movement of the other in conjunction with the line fitted.

If you want the correlation, taking the square root of R-squared gets R, the correlation coefficient. R-squared is probably more intuitive, as it measures the proportion of variation explained by a fitted line.

In the next chart I’ve zoomed in on the area that matters, but I also show a view which includes the origin. The line fitted has a negative slope, which implies that more sales in “Enterprise” mean less sales in “Service provider”, and vice versa. The R-squared value is only 0.360, and in my opinion the relation implied by the negative slope is likely to be spurious.

**The spreadsheets**

The formula view below shows that in the spreadsheet above, most columns are a mixture of data and formulas, and in some columns the formulas vary. The reason is that cash flows for a single quarter are only in the SEC filings for Q1. The result is the kind of complication that makes spreadsheet errors more likely, even though each formula is only simple arithmetic.

Thank you for reading this.

DISCLAIMER: Your investment is your responsibility. It is your responsibility to check all material facts before making an investment decision. All investments involve different degrees of risk. You should be aware of your risk tolerance level and financial situations at all times. Furthermore, you should read all transaction confirmations, monthly, and year-end statements. Read any and all prospectuses carefully before making any investment decisions. You are free at all times to accept or reject all investment recommendations made by the author of this blog. All Advice on this blog is subject to market risk and may result in the entire loss of the reader’s investment. Please understand that any losses are attributed to market forces beyond the control or prediction of the author. As you know, a recommendation, which you are free to accept or reject, is not a guarantee for the successful performance of an investment.