Would you say that MSM are between "less technical methods" and "real options"?
Although I've written about real options I've never used them in practice. But when it comes to any new business I tend to start off by saying it has a 5% chance of success, and then this could happen or this or this. So I see different states, but I don't usually quantify them. But I do like things that have several different routes to a payoff, or even better would be routes to several payoffs/spin offs.
I think this is a more concise explanation: https://www.maths.lancs.ac.uk/~titman/leeds_seminar.pdf
Now that I consider it, I think MSM's give you a better figure to put into a real options model. The issue with computing real options is the standard deviation usually. There is a way to compute standard deviation at some future date based off of a payoff table. The MSM gives you a more accurate perception of those probabilities. If you put a chart together of potential outcomes in Visio, you could break down something actually very complex into a relatively simple problem.
When people don't understand finance that well or come to understand it on their own, I've found that they come up interesting ways of modeling problems mathematically. In this instance, I think that is why Elon Musk came up with a MSM model. I ran into another really successful guy who was doing something with capital structure and valuation multiples. The way that Elon Musk describes it, he thinks about the relationship between the multiple states as being a function of your rate of breakthroughs in success. There is also the popular terminology surrounding burn rates
, etc. He makes no mention of this, but it is sort of inherently implied. For example, you receive some capital, and there is some future date at which you must complete your innovation otherwise you will have "burned through" all of your capital, at which point you are ready to go to market. What is the probability of hitting that target spot on or exceeding it at any point in time? In theory there is a distribution of potential outcomes revolving around that target date.
I've had to use real options before when computing parts of contracts, granted I had my own company at that point so I was able to do everything at the level I would like to for analysis. There was one acquirer who frequently liked to have put options on the sale inserted in the contracts (the right to sell remaining shares, up until a future date, at a specified multiple of EBITDA). I used a forecast of scenario's to determine the payoff table, and the payoff table to determine the standard deviation (if you have any payoff table of any kind, you can compute expected variance in addition to expected value
- There is also a manner in which you can compute standard deviation using a binomial method as well at any point in time. You can either do it the same way as with a multivariate scenario outcome, or slightly differently, but in theory you should come up with similar values.
There is also likely some sort of use when it comes to default probabilities, utilizing a technique like the Merton model
for firm credit risk: https://courses.edx.org/c4x/DelftX/TW3421x/asset/Week5_PD2_2.pdf
For example, when examining the credit default spread with fixed income securities (a Venture-Debt
- Equity risk factor - the credit (or default) spread can also be thought of as a put option. There is an inherent put option on any bond for default because they can default and give up their assets.
- Bankruptcy is just the exercise of this put option
- It’s value is = to the Max (Liabilities – Assets,0)
- This gives you your option adjusted spread