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Startups investors and money lenders asked us two questions. Based on our risk assessment and management experience, is ORE capable of:
We derived a subset from ORE (Optimum Risk Estimates, ©Riskope) and deployed it on 7 companies. The companies are at different maturity stages. They are active in different spaces in three countries (Canada, Italy, Switzerland), namely:
|Startup #1||Canada||Networking hardware products|
|Startup #2||Canada||Agricultural waste recycling|
|Startup #3||Italy||Medical spa|
|Startup #4||Switzerland||Grass insulation panels|
|Startup #5||Canada||Industrial waste water treatment|
|Startup #6||Italy||Engineering analyses|
|Startup #7||Canada||Restaurant Waste Recycling|
The names of the companies and their URL are confidential.
We used the results delivered by ORE applied to Startups to analyze:
The first deployment produces a priori evaluations which we can update as the corporation develops. We can include uncertainties by running a optimistic vs. pessimistic estimate. In this blogpost we show a pessimistic estimate only.
ORE for Startups computed a priori the evolution of the probability „of failure“ through a simulated triple round of funding and development. As the development of the company proceeds, we can undertake various rounds of a posteriori re-assessments. That is as new data becomes available, some mitigations occur, etc.
Figures 1 to 4 show “summarized” information for all the companies in the tested portfolio. Data cover the three Phases of their life, i.e. three rounds of financing of unspecified total duration.
We will now use these figures displaying relative a priori probabilities to select the best companies for investment.
Note that the high values are compatible with factual rates of failure determined, for example, by the Kaufmann Foundation in their studies related to startups.
Note how different the a priori probabilities of failure profiles are for each company as they transition from Phase 1 to 3.
Startup #1, Startup #2 and Startup #3 reduce the probabilities when transitioning from Phase 1 to Phase 3. Instead Startup #4, Startup #5 and Startup #6 see their Phase 3 probabilities increase very significantly.
Startup #7 is an exception insofar all three Phases have very high probabilities.
Based on Figure 1 one would be inclined to select Startup #1, Startup #2 and Startup #3 as the best investment candidates. But is that really the best option?
Figure 2 shows the same results than Figure 1 but for Phase 1 only.
Note that Startup #1, Startup #2, Startup #3 as well as Startup #7 have the highest a priori probabilities of failure. Instead Startup #4, Startup #5 and Startup #6 have the lowest. Startup #6 is by far the less prone to failure in Phase 1. The horizontal orange line represents the average of the a priori probabilities of failure for Phase 1.
Figure 3 shows the same information, but for Phase 2. Indeed, same trio of “best” companies, with Startup #1 being at the average value.
Finally Figure 4 shows the results for Phase 3 with the “winning” companies being Startup #1, Startup #2 and Startup #3. Startup #6 is slightly above the average and Startup #4, Startup #5 and Startup #7 are the most likely to fail in the a priori estimate of Phase 3.
Finally, Startup #4, Startup #5 and Startup #6 “win” the best place in Phases 1,2 and only “lose” in Phase 3.
This discussion will continue over the next few weeks to show further details on how ORE predicts business startups 3 financing rounds success.