Introduction to the "Rising Stars" Strategy

[ Last actualized 3 September 2020 ]

In this article we are describing theoretical background and back-testing results of our "Rising Stars" strategy.

We launched the strategy on 13 July 2020, and the strategy has shown great results since then: overall result was +28% in less than 2 months, 1 out of 5 picks went up by 193%.

The idea of the strategy
The idea of the strategy was to screen almost all the Nasdaq and S&P companies (above certain MCap threshold) and to find companies looking like Next Facebooks / Zooms / Alibabas in terms of their growth trajectory — the companies that are growing radioactively and disrupting their industries.

Our basic assumption (that was confirmed) was that growth companies are quite auto-correlated: the companies growing fast tend to grow fast, companies growing slowly tend to grow slowly. We assumed also that the last revenue growth is especially important (as sometimes companies being growth stories stop being "growth stories").

The other assumption (that also found confirmation) was that companies with the most rapid historical revenue growth (in fact disrupting their industries) on average show much better share price performance than other companies.

Finally, we assumed (and confirmed) that if we add limitation in terms of EV/Sales multiple (to control risks and avoid situations when you invest at companies valued too high), it would not spoil and a little bit further improve results.
The "Rising Stars" strategy aims to select and invest in companies looking like next Facebooks / Zooms / Alibabas in terms of their growth trajectory
By the way, growth is the leading parameter venture capital funds are looking at; for example, some Y Combinator (the most successful venture accelerator) partners say that growth is almost the only KPI one should pursue.

Unlike our US Growth Strategy, the Rising Stars strategy doesn't measure created value in terms of EBITDA / profits. Quite often companies with rapid growth rates are measured by revenue; such companies may be loss-making and expected to start being profit-positive somewhere in the future.
Data analysis
We’ve gathered financial information on ~1500 companies from 2015 to 2020 and uploaded it to statistical analysis tool.

The target of analysis was to answer following questions:
What are the most important factors influencing share price performance? Which factors influence most the fair multiples of the companies?
Would it be correct to say that companies showing the best (last and average) revenue growth rates on average overperform the other companies?
The analysis confirmed our hypothesis of significant influence of revenue growth on share price performance.

For example, if average quarterly share price performance for the whole selection on the considered timeframe was 1.7% (after the reporting date), if we apply condition of last and average historical revenue growth being > 20%, quarterly share price performance increases to 3.0% (72% higher!). If we raise the bar and modify the condition so that average historical revenue growth >30% (and last revenue growth >20%), average quarterly share price performance increases to 5.4%!

Adding the condition of EV/Sales multiple < 5.0x improves result a little bit further (from 5.4% to 5.9%) and gives more comfort in terms of risks control and avoiding "overheated stocks".

When we ran logistics regression on the EV/Sales multiple all the considered factors (revenue growth rates, EBITDA margin, the company being based in US, the company being from IT sector) had positive regression factors on overall EV/Sales multiple; still, the highest influence as expected was for last and average historical revenue growth.
Strategy formal description
The strategy automatically screens all the S&P-500 and Nasdaq companies with market cap >$500m and invests in companies showing the best revenue growth trajectory: the companies should show last and average historical revenue growth >20% and being valued by the EV/Sales multiple below average for the selection threshold (5.0x);

Out of such companies the strategy picks 5 companies with the best 3-years revenue growth rate and invests evenly among them.
Back-testing results
The strategy has shown great on the back-test overperforming the index substantially
Year Strategy S&P 500
2015 -4.8%% -0.7%
2016 35.3%% 9.5%
2017 59.7%% 19.4%
2018 -22.9% -6.2%
2019 71.7%% 28.9%
2020 96.2% 8.5%
Annualized 34.4% 9.9%
Return by year
Maximum drawdown
-44.4% (2018-12-24)
The strategy performed quite well since inception on 13 July 2020, total return up to 03 Sep 2020 was +28% (in less that 2 months).

One of the 5 strategy picks showed 193% share price increase.
We are open for further discussions/questions!