
Show HN: Retirement Calculator/Simulator - dnadler
http://lunchmodel.com
======
dnadler
Hi everyone -- I've been working on this for a while in my free time. It's not
'feature complete' yet, but I'm eager to get some feedback.

It's basically a monte carlo simulation based on market characteristics
(correlation, volatility, etc.). The 'killer feature' is that it can
accurately account for granular cash flows throughout a person's life. For
example, selling or buying a house, receiving an inheritance, a recurring
salary, social security, etc.

This page has some more info about how to use it, and how it works:
[https://lunchmodel.com/learn-more](https://lunchmodel.com/learn-more). I'm
happy to answer whatever questions you have, too.

Try it out, and let me know what you think. It's running on a small server, so
hopefully it doesn't get too popular :).

One important note is that the form's state is saved in your browser's
localstorage, which means you can leave and come back. If you want to delete
this data, you can click the 'reset' button at the bottom.

Some features I'd like to add are:

\- How would I have performed in XXXX period?

\- Save and Load state

\- Some nominal registration fee ($2? What would you pay? Anything?) to unlock
the 'advanced features'

\- What did Equity/Bond performance look like in the 99th and 1st percentile
simulations?

~~~
lonelappde
The problem with granular simulators is that the results depend on unknowable
parameters. Do you compute a robust model the accounts for the 90% confidence
interval of those parameters?

The problem with Monte Carlo is the it tells me I'll die with somewhere
between -$5M and $20M.

~~~
dnadler
It generates returns for the market based on the covariance matrix and returns
assumptions. These do fluctuate, but long-term estimates of these values are
pretty well defined. The simulation here generates several thousand random
returns based on these assumptions, and the results indicate the distribution
of possible outcomes.

The results you are seeing suggest that under these assumptions, there is a 1%
chance that you will have -$5M and a 1% change that you will have $20M at the
end of the simulation. (1st and 99th percentile outcomes).

With different parameters (less savings, lower income, etc) you may have a
much high likelihood of running out of money.

Knowing what this distribution looks like can be quite informative.

I could modify the market returns generator to allow for variance around the
historical market assumptions, or provide some sensitivity analysis. On the
whole, though, that will simply expand the distribution of the final results.
I believe it is better to target more conservative assumptions than it is to
include both conservative and aggressive assumptions. If you know that your
conservative scenario still plays out well, then you can be confident that
your plan is sound.

I guess, to sum up: the fact that monte carlo returns a distribution is a
_feature_ , not a _bug_. You can't know the future for sure, but you can put
some bounds around the likely outcomes. If you have a 10% of running out of
money (for example), you should probably save more.

