Modelling venture startups like defaults

If you were in the position to choose among a few startup projects to invest, what data and what criteria would you use in order to assess the likelihood of a project to turn more successful than the rest, should there be available also the history of performance of other similar projects from the past?

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  • Well we are going through a venture capital journey now and I have spent plenty of years in banking building risk systems, under Basel 2 and so on,  so can shed some real world experience on this!

    Its very qualitative - given the entities  numbers are estimates at best, how reliable are they? Since traditional Debt lenders wont fund start ups you can assume the "forecasts" are not worth the paper they are written on.

    So it comes down to key subjective variables - perhaps overlay something like Altman's original Z-Score. But you need a bunch of observations from realt world successes and failures (by the way you will need to actually first define what success and failure are to you) to use for building your model then test it.

    Pick a bunch of predictive variables, such as some of the example already detailed here like management experience, industry, etc; give them a weighting then give them a score.

    Use 80% to build your base model then 20% to test and tweak it.

    If your model only shows roughly 5 % of start-ups being "successful" its pretty close.............!

    good luck

     

  • I will consider the following:

    - Feasibility of Project (Financial, Technological, Economical, Operational)

    - Existing Market

    - What are the Opportunities for Differentiation & Innovation?

    - Most Importantly What's is my confidence level & will power to do this?

    - In case I have to, How easy is to quit?

    Then Burn the Boats & Go in :) Of course having Strategy B, C & D also


  • I agree that the team factor along with personalities of project owners are the most crucial for the assessment. While having a history record by project owners simplifies the analysis of a project, it also triggers the possibility of misjudgement 'cause the identity of project leader is easily substituted for PR purpose from someone well-reputed, but looking to exchange own success for quick-money. For ease of the execise lets however assume that the investors are dealing with someone new and unknown to the public. What other things should they look at in order to facilitate the selection of projects?

  • The traditional method for a project by one's own company or a trusted contractor was to conduct both Tangible and Intangible benefits analysis:

    1. Tangible - a Cost-benefit Analysis giving NPV, IRR, MIRR, Break-even

    2. Intangible - What are the benefits that cannot be costed

    And I would add a formal Risk Assessment.

    If the project is to be undertaken by people without a clear track record, or there may be some doubt about the viability of the project (market, technology, resourcing, etc), then definately conduct a full Risk Assessment as per ISO 31000, including some of the considerations mentioned above.

  • Yes. Definitely. 'Default' should be the Default Position. Preferably there should be available "the history of performance of other similar projects from the past". See 'Planning Fallacy' eg. Daniel Kahneman: 'Thinking Fast and Slow'. Many new projects fail due to a Fallacy that they will somehow be different or better than similar past projects but inevitably they 'regress to the mean' of similar past projects. This is coupled with the Appalling Record of Start-Ups generally. So Please Please, lets Applaud the Entrepreneur, but go in with eyes wide open.

  • Does the project solve a recognized need/pain? 

    Are there people or organizations willing to pay to solve the pain?

    Does the project team have relationships with customer?

    Are there enough people to make a business case?

    Does the project team know what it is doing technically?  Do they know how to run a business?

    What is the competitive environment?

  • Having led several GRC initiatives as a customer as well as various vendor initiatives, I always start with defining and normalizing result output from insurance actuarial models – that’s the foundation.  Typically, actuaries have the most granular and vetted data by constant regression testing using asset and liabilities models.  Many companies struggle in 2 key areas - the massive amounts of output modeling data and robust data quality input processes. This is where my current employer SunGard provides that gap nicely.  

    For a venture startup, I’d say it’s very difficult to help establish that foundation requiring deep intimate knowledge and understand of what constitutes a ‘normalized model’ and the sheer amount of data reaching into the 100’s of terabytes. 

    The hot-spot for startups could be on the back-end business intelligence area, providing a set of commonly accepted formats supporting Solvency II and for the US the emerging ORSA standards.  If considering enterprise risk management dimensions and risk KPI’s, you need measurements across volatility or CAT scenarios you need to calculate mortality, lapse, reinsurance, investment returns and operational cost to provide the full risk picture. 

    For startups, having spent 2 of my 6 years at Microsoft driving startup adoption, I can say with a high degree of confidence Microsoft provides startups with a great program called BizPark helping smaller partners (or VC’s) get their applications up and running faster with lower cost entry points.

  • Let's start with the assumption that the start-up projects belong to different sectors, hence they have different yard-sticks for measurement.

    The data to be collected for testing feasibility will depend on the "scalability" of the project. The more scalable the project the broader should be the selection of variables. For example; A telecom venture will need longer periodic data and macro economic analysis than that required for a software development project. I would then rank the combinations according to the following parameters:

    1. Payback period;

    2. Profitable payback period;

    3. NPV and sensitivity to four largest key risk factors, for e.g. government policy, interest rate, productivity-linkages, counter-party risk; and

    4. Exit valuation strategy at different stages of the project.

    This approach facilitates a time-scale comparability of risks and returns among the options for start-up projects.

    Hope to hear your further comments.

    Regards

    Samuel  George

    casammy@gmail.com

  • Benchmarking the projects against existing models is definitely a fair approach. This being said several issues have to be factored in this method :

    - Are market environments and consumer behaviours comparable ? 

    - If both are available or even identical, do you see a comparable potential for a second actor ? Without impact on pricing/competition ? What are differntiating factors ?

    Other criteria to be considered :

    - Quality and track record of project owners

    - Market potential evaluation - third party field study if available

    - Product/service offfer already met demand ?

    - Sustainibility of cash-flow planning

    - Identification of risks (market, credit, strategic, operationnal ...)

    And obviously a lot more. Several well researched books are available on business plan challenging. It's worth using them prior to any decision.

  • Normally:

    1) team;

    2) market;

    3) scalability of the products;

    4) possibility to defend own market share in future;

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