Dreams to Nightmares ratio
What this blog post is NOT:
1. Official view on any sector
2. Investment advice
What this blog post IS:
1. Reminder to not get blindly carried away by good-time or stress-time narratives
2. Remember that every decision can result in a dream outcome or a nightmare outcome. One key role as managers / investors / leaders is to know what this ratio is for every decision and bend the ratio in our favor
Software will eat the world. Asset light. <Whatever>-as-a-service on cloud.
Phrases like these get discussed all the time.
Got me thinking…
· What is the probability of these phrases being secularly correct paradigms for resource allocation?
· What about other business models?
· How do the odds look for different business models over time?
My colleagues Aniket Kulkarni and Sanchit Seth helped do some quick and ready analysis in Indian context.
They looked at all the publicly listed companies in India from February 2003 to December 2019. Only companies that were listed during this period and did not delist during this period were considered for the analysis. This gave us a universe of 2336 companies across 11 sectors.
We analyzed how many companies lost money. Meaning, they were trading at lower than their listing price. This was a proxy for saying, if you had allocated capital to this business on day-0, you would have lost money. This is catastrophic loss. Investor loses capital.
Similarly, we analyzed how many companies protected or generated a multiple on the capital.
The below chart shows by sector, what proportion of companies lost money, what proportion returned 1–2x of IPO price and what proportion returned more than 2x. Granted the time periods are different for each company so this is not a TSR analysis. It is a simple way of looking at where the capital was lost (in very real terms), where it was at least protected (in nominal terms) and where the capital generated returns (in nominal terms).
Just this simple analysis shows that old boring sectors are not that bad at preserving capital or generating good returns.
The most practical implication is — if you are today thinking of capital allocation, its not obvious that all old sector investments are bad and all new digital / data science sector investments are great. It boils down to who you are and whether you can manage the dreams to nightmare ratio in your favor.
That brings us to the Dreams — To —Nightmare ratio. The next chart shows for each sector, when you make money (i.e. make more than 1x returns) how much money do you make and when you lose money, how much money do you lose. It is then weighted by the probability of making vs. losing money. Eventually probability adjusted gain and loss numbers are calculated. The ratio of the two is the Dreams to Nightmare ratio. The energy bar is surprising but remember that this is pre-covid, pre-war. So its still interesting…
Again, while admittedly this analysis is an over simplification, it clearly shows that founders, investors, managers, leaders, employees should be very alert to asking themselves five questions unemotionally:
How likely am i to succeed in this business / venture?
How likely am i to fail?
When i succeed, how good can success be?
When i fail, how bad can things get? Will i lose my shirt?
How do i ensure that both absolute success and probability of success increases while the probability of disaster is eliminated / minimised?
Last but not the least, all the buzz-terms … they are not wrong…but they don't mean anything till we evaluate the dreams to nightmare ratio with a clear head. Not all businesses succeed. Not all businesses have salvage value. It boils down to ones personal risk tolerance.
PS: views are personal.
PPS: Many thanks to the two gentlemen who let me a hand in the heavy lifting Aniket Kulkarni and Sanchit Seth
