In the last post I argued (mainly with myself) on how Mattermark scores could be used as a gauge for a NI startup’s performance. No response, not that I was really expecting one but no one complaining either. I did say that I would delve into the numbers a little deeper, so here it is.

Got a drink ready? Let’s go.

marilyn

“So How’s [Startup] Doing?”

It’s a question I’m often asked but one I have now stopped asking myself. The main reason: location, location, location. I’m nowhere near the action.

With the best will in the world it’s either be in Belfast or pretend you are in Belfast. The thing is when someone asks that question, “How’s so and so doing” the answer is usually based on hearsay, rumour and the 99% confirmation bias of the founders, regardless of how nice they are everything will be going fine. They’ve got to keep the positive mindset going, I won’t knock them for doing what they have to do, it’s business.

One of the reasons I’ve relied on the Mattermark scores more and more, they are a good gauge as to how a startup is performing from a social and investor sentiment profile standpoint. It also means I can compare against others in the same sector.

So how is seesense.cc doing? Easy, inspect the growth score numbers.

core>seesense
(160 173 175 170 174 172 170 169 176 178 185 196 196 196 188 196 196 198 199 206 205 204 207 197 187 187 187 182 173 173 170 167 160 154 153 146 150 149 147 145 144 140 138 136 135 134 129 126 129)

That’s all I care about, the previous 52 week Mattermark scores.

Annual Return

Taking the end and start figures I can return a percentage of theoretical returns (what did it make), if I were to treat the Mattermark score as stock price for example then how much am I making over the annual period.

The annual return is easy to calcuate, it’s a percentage.

end / start - 1

Seesense started at 160 and ended at 129, which gives me -0.19375 or -19.3%.

Daily Returns

Exactly the same calculation as annual returns, just done on an each reading basis over the period.

(inv/daily-returns seesense)
(0.08125000000000004 0.011560693641617936 -0.02857142857142858 0.02352941176470602 -0.011494252873563204 -0.011627906976744207 -0.00588235294117645 0.041420118343195034 0.01136363636363602 0.039325842696628976 0.059459459459459074 0.0 0.0 -0.04081632653061218 0.042553191489361986 0.0 0.010204081632652962 0.005050505050504972 0.035175879396984966 -0.004854368932038833 -0.004878048780487809 0.014705882352940902 -0.048309178743961345 -0.050761421319796995 0.0 0.0 -0.0267379679144385 -0.0494505494505495 0.0 -0.01734104046242768 -0.01764705882352935 -0.041916167664670656 -0.03749999999999998 -0.006493506493506551 -0.04575163398692805 0.027397260273972934 -0.00666666666666671 -0.01342281879194629 -0.013605442176870652 -0.006896551724137945 -0.02777777777777779 -0.014285714285714346 -0.01449275362318836 -0.007352941176470562 -0.007407407407407418 -0.03731343283582089 -0.023255813953488413 0.023809523809523947)

Remember these are based as percentages.

Measuring Risk

Risk is measured as the standard deviation of daily returns.

(defn risk-measure [coll]
 (stats/standard-deviation (daily-returns coll)))

In the above figures we get:

:risk-measure 0.02866805600574157

A 2% standard deviation from the mean, not bad going at all. It’s consistent.

Using The Sharpe Ratio

A common measurement used in finance, the Sharpe Ratio is a reward vs risk measurement. I’m basically taking the average of daily returns and dividing it by the standard deviation, the average is then multiplied against the square root of the number of trading days.

There are variations on the Sharpe Ratio but this version serves me well as a reward/risk measurement.

(defn sharpe-ratio [coll trading-days]
 (let [k-num (Math/sqrt trading-days)
 dr (daily-returns coll)
 risk (risk-measure coll)]
 (* k-num (/ (stats/mean dr) risk))))

Let’s have a look with Seesense’s readings.

(inv/sharpe-ratio seesense 52)
-1.0255674077731773

I’m looking for a ratio of 1 as a general good return with low risk, if it were 2 then my eyes would be wide open looking to invest if the company had IPO’d. As Seesense’s downward curve (why? they’re a good company in NI) is pretty consistent I personally wouldn’t be looking to invest, the -1 Sharpe ratio confirms it.

Take six companies

Let’s take this a little further. At the start of November I looked at the data from a number of companies from the mainland and Northern Ireland. Good companies doing great things, what’s important is that there’s a consolidated number within Mattermark for all of them.

I ran the investor metrics against the Mattermark company data and got the following data back.  Disclaimer: The idea is for me to remove myself from the biases of the startup and get a response purely from the numbers, the companies below are all great I just picked them for the purpose of this blog.

Company Annual Ret Risk Sharpe
Adoreboard -0.0645 0.0171 -0.4950
Airpos 0.9833 0.0408 2.5306
App Attic 0.5625 0.2118 0.8072
Brewbot -0.25 0.0182 -2.1838
Get Invited 0.1428 0.1084 0.5443
Taggled TV -0.0064 0.0252 0.0520

Interesting results, there’s one standout “investment” and that’s Airpos. With a Sharpe ratio of 2.53 and only a 4% risk factor an investor would be putting money on it if it were IPO’d.

Talking of risk, is App Attic the riskiest company? From the numbers it says so, at 21% but the annual return was good too at 56%. There was plenty of volatility in the raw data to back it up, especially at the early stage of the year.

From an investment standpoint the numbers, while live, are purely theoretical. The question we need to ask ourselves is this: is a VC, Angel or other investor going to use these scores as part of their due diligence process. Taken even further could something like the gamblers fallacy take over?

The Binary Decision

Would data driven decision making work for long term returns? If an investor algorithm for example worked on two basic rules:

if risk < 10% and sharpe > 1 then invest

Out of the seven companies we’ve looked at all together only one would be positive as a result of the algorithm, that’s Airpos.

Concluding

It’s been an interesting exercise and one I’ll be keeping an eye on. The question that remains unanswered though, do actual investors use the Mattermark scores as part of their due diligence and use the metrics available to steer the basic decision of to invest or decline?

 

 

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