Thanks for the comments Ben! This paper is the canonical response to the poorly researched âAll great CEOs do these 10 thingsâ sorts of articles that Harvard Business Review etc. loves to publish, which is why I included it.
Fair enough. If you look at table 4, you can see that the basic trend of âindustry matters more than segment specific effectsâ holds for everything except, confusingly, for manufacturing. So while it may not be robust across different company sizes, itâs at least robust across different industries. (And my intuition is that small companies are even more affected by industry downturns.)
If Iâm understanding you correctly, you think they should be using the Rumelt model described on pages 17 and 18? They ran that model on the data in table 4 with basically the same result (17% of variance explained by industry, 29% by company versus 19% and 32% with their model).
Interesting, good point. I skimmed some citing articles and couldnât find anyone talking about this, so Iâm not sure.
So while it may not be robust across different company sizes, itâs at least robust across different industries.
This is irrelevant to your point if it doesnât hold across sizes, though. Compustatâs data is entirely on public companies. I really donât see any reason to expect this to generalize to tech startups.
In fact, one would expect that most of the influence of founder/âIP/âteam/âwhatever would be concentrated in navigating from startup to IPO, rather than in post-IPO profits. If the fixed effect is already on the order of 30% of variance post-IPO, I think itâs hard to defend the primarily-lottery-ticket interpretation in the pre-IPO regime.
Overall, I agree with the point that this data set may not be valid, and I sincerely appreciate you pointing that out. I will have to do more research and figure out what can be done with that section.
However, I disagree with the statement:
In fact, one would expect that most of the influence of founder/âIP/âteam/âwhatever would be concentrated in navigating from startup to IPO, rather than in post-IPO profits.
Startups pivot all the time, and are constantly changing their products. Therefore we should expect that a startupâs profitability in year T doesnât predict very well their profitability in year T+1.
Big companies almost never change, and my experience is that a large part of the reason they make sales this year is because of the sales, products etc. they made last year. Therefore we should expect their revenue stream to be highly correlated over time.
So I think the big company numbers place an upper bound on the results you would see from small companies, not a lower one.
(One way of coming to this conclusion that I assume is uncontroversial is just to note that the valuations of startups fluctuate a lot more rapidly than the valuations of public companies.)
Let me know what you think about this, and your thoughts around including some language about this in that section.
Startups pivot all the time, and are constantly changing their products. Therefore we should expect that a startupâs profitability in year T doesnât predict very well their profitability in year T+1.
Yearly profitability isnât the right measure before IPO; most startups arenât optimizing for profitability (and indeed never become profitable until after IPO). High losses can indicate failure, or they can indicate rapid scaling of a successful model. So unpredictable profits arenât really evidence of eventual success being unpredictable. Even if eventual success was relatively predictable we would expect profits to fluctuate along the way depending on the companyâs specific growth trajectory.
Sure. For better or for worse thatâs what the paper I cited measured, so thatâs why I was using that metric.
If you prefer though: startups have significantly higher fluctuations in valuation than public companies, and presumably valuation captures whatever metrics are relevant.
Thanks for the comments Ben! This paper is the canonical response to the poorly researched âAll great CEOs do these 10 thingsâ sorts of articles that Harvard Business Review etc. loves to publish, which is why I included it.
Fair enough. If you look at table 4, you can see that the basic trend of âindustry matters more than segment specific effectsâ holds for everything except, confusingly, for manufacturing. So while it may not be robust across different company sizes, itâs at least robust across different industries. (And my intuition is that small companies are even more affected by industry downturns.)
If Iâm understanding you correctly, you think they should be using the Rumelt model described on pages 17 and 18? They ran that model on the data in table 4 with basically the same result (17% of variance explained by industry, 29% by company versus 19% and 32% with their model).
Interesting, good point. I skimmed some citing articles and couldnât find anyone talking about this, so Iâm not sure.
This is irrelevant to your point if it doesnât hold across sizes, though. Compustatâs data is entirely on public companies. I really donât see any reason to expect this to generalize to tech startups.
In fact, one would expect that most of the influence of founder/âIP/âteam/âwhatever would be concentrated in navigating from startup to IPO, rather than in post-IPO profits. If the fixed effect is already on the order of 30% of variance post-IPO, I think itâs hard to defend the primarily-lottery-ticket interpretation in the pre-IPO regime.
Overall, I agree with the point that this data set may not be valid, and I sincerely appreciate you pointing that out. I will have to do more research and figure out what can be done with that section.
However, I disagree with the statement:
Startups pivot all the time, and are constantly changing their products. Therefore we should expect that a startupâs profitability in year T doesnât predict very well their profitability in year T+1.
Big companies almost never change, and my experience is that a large part of the reason they make sales this year is because of the sales, products etc. they made last year. Therefore we should expect their revenue stream to be highly correlated over time.
So I think the big company numbers place an upper bound on the results you would see from small companies, not a lower one.
(One way of coming to this conclusion that I assume is uncontroversial is just to note that the valuations of startups fluctuate a lot more rapidly than the valuations of public companies.)
Let me know what you think about this, and your thoughts around including some language about this in that section.
Yearly profitability isnât the right measure before IPO; most startups arenât optimizing for profitability (and indeed never become profitable until after IPO). High losses can indicate failure, or they can indicate rapid scaling of a successful model. So unpredictable profits arenât really evidence of eventual success being unpredictable. Even if eventual success was relatively predictable we would expect profits to fluctuate along the way depending on the companyâs specific growth trajectory.
Sure. For better or for worse thatâs what the paper I cited measured, so thatâs why I was using that metric.
If you prefer though: startups have significantly higher fluctuations in valuation than public companies, and presumably valuation captures whatever metrics are relevant.