For a single loan, how are the ROI and Loss calculated?
Run this LC filter:
Loan Status = Charged Off
Annual Income = 500000 to 500000
Grade = C
This results in exactly 1 loan:
a $35,000 loan that was charged off.
How is the Loss = 183.38%?
How is the ROI = -168.03%
[EDIT: I figured it out using NSR Returns Formula, though I still don't understand it. If someone can explain it in plain English, that'd be great.]Also, why is Completed = 0.00%? (Isn't a charged off loan completed?)
The loss rate is a combination of forward looking math to support loss estimates and displaying results as a yearly ROI, not fixed. The simplest example is a loan that makes 10% in the first 6 months. This puts you on track to make a yearly ROI of 20%. The same forwarding looking logic gets applied to losses. So if you had a loan that defaults at 6 months which causes you to lose 90% of your investment, that puts you on track to lose 180% for the year.
The idea in both scenarios is that you are multiplying up your losses as well your gains in order to extrapolate an estimated ROI at a yearly rate. Similarly if you had a 24 month loan that returned you 40% on your investment, it only has a yearly ROI of 20%. This is an oversimplification, but at a high level it is what is occurring.
When looking at a single loan it might not make a lot of sense to do this, but that isn't what we are trying to do. We want to come up with an estimated yearly ROI for a pool of loans, and this is when it starts to make more sense. When a pool of loans are put together you dollar weight and time weight the yearly ROIs in order to come up with an estimated return for the entire portfolio. This is when the loss rates of over 100% help. In this pool of loans if you have a couple loans that charge off real early, that implies a high charge of rate and that you will have other loans charge off early and you need to capture that in your estimate. The forward looking loss rate of over 100% gets averaged in and takes this into account creating a higher rate of loss for the entire portfolio from these early charge offs.
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The completed flag in our system doesn't mean what you think it means. It is an indication that a loan is older then its term. So a 36 month loan originated over 36 months ago is completed. It doesn't take into account anything else. The use case for this feature is different then trying to find all loans that are in a certain state. You should be able to filter on status to get all loans that are charged off and finished making payments if that is what you are trying to do.
I happen to have a Ph.D. in Engineering, an MBA in Finance, and have been in quantitative finance for many years. I have seen all kinds of financial equations and assumptions, but not the one you mentioned -- getting > 100% loss for what's considered long-only securities.
Can you tell me more about this forward looking math to support loss estimates? And what is the path from this "math" to the final loss estimates?
I think the detail that is missing is that I can't fit exact definitions of what our formula generates into the header column. The column headers "Loss" and "ROI" are the best descriptions that fit in the small space we have for the header, and they are pretty good descriptions for what those fields are.
The numbers that show up there do not always equate to the text book definition of the one word header. I tried to explain what they mean in some weird scenarios, such as when a number over 100% shows up in the Loss column. It seems like you are ignoring the explanation of the data and saying that the one word header must exactly describe the data, even after I post a message saying it doesn't in all scenarios.
It is a real loss estimate for most usage scenarios, so I am ok leaving the word Loss there. But in some scenarios it is over 100% because the loss occurs extremely quickly and skews the calculation at which point the number means there is large loss in a very short time frame (with the larger the number being the faster the loss occurred), and that needs to be captured in order to balance out other young loans that are performing well. Extrapolating to yearly returns from young loans causes stuff like this to happen.
But again, I have to reiterate, the formula is not meant to be used on extremely small set of loans. The number for any single loan is not meant to be used by itself, that is not what our tool is designed for. Any estimated numbers that are generated can have weird results if there is not enough meaningful data to normalize the results.
Hello,
I'm also attempting to understand how NSR calculates ROI. In the formula given at
https://www.nickelsteamroller.com/#!/stats/roi , what do L
0 and L
n represent?
I appreciate any help, thanks!