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Thread: Harmoney

  1. #3901
    yeah, nah
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    Chart of Expected Defaults by grade (because I hate the Hazard Curve and often find annualised data misleading):

    This is calculated from historical data (unique.csv) with points on the line representing the percent of loans that are expected to default sometime in the future for a given loan age. (this is not annualised!) I've applied a window (description in title) to remove some outlier detail that plays havoc with the graph.

    To give an example of how to read the details:

    C Grade loans that are 10 months old have a ~3% chance of defaulting sometime in the future.

    expected_defaults.jpg

    The first point on the line is the expected default rate for all loans in that grade (based on historical data) - NOT annualised.

    Some interesting crossovers and some rising and some falling grades...

    Fix: Had a date constraint wrong which influenced the right hand side of the chart. Note that the right hand side is still incomplete as number of loans still to low for meaningful detail at that age.
    Last edited by myles; Yesterday at 07:17 PM. Reason: Fixed

  2. #3902
    yeah, nah
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    Quote Originally Posted by beacon View Post
    A bivariate analysis is much more useful than segregated filtration in a multivariate environment. At the very least, it proves again that risk falls with age, until you hit 60.
    If you liked that then you might like these even more - same but with grade as well pdf runs to 67 pages though...

    l2_default.csv

    l2_default.pdf

    The csv is particularly easy to use in a spreadsheet with simple column filters - lets you 'dig' into the data without having to get bogged down with pivot tables initially.

  3. #3903
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    Quote Originally Posted by beacon View Post
    Brilliant work leesal. Thanks for proving again, that loan picking ISN'T simply happening for love, and so, ISN'T a sheer waste of time.

    I'm assuming 1. is your default minimiser. That is an awful lot of filters, overridden by top level elimination/modification of filtered loans. Be interesting to know, what criteria you chose for your second default set.
    Thanks. Myles data stimulated a lot of thought and good ideas... and as a loan picker, its useful to have an overarching frame work - and one backed up with historic data all the better!

    I've progressed beyond where I was And now have a streamlined single "default minimiser" that can ideally put into an autofilter. The criteria I have is B,C,D,E grades; age band 20 -60 year; debt to income < 10%; time at residence 2+ years; all purposes except new vehicle, new boat, wedding expenses, tax, other. With those alone can boost return and half default from the average 7.0% to 3.5%. Then there are items which cannot autofilter on that have a big impact - enquiries 3 or fewer, and debt to income < 5% for E grade loans - which bring the default down to 2.2%. I have gone further then that without sacrifricing return, but it can get very complicated.

    Will start off running the default minimiser, and will be handpicking others I like from gut feel; and track how they perform.

    Quote Originally Posted by beacon View Post
    There is bound to be selection bias, since only 17 or so investors contributed to the data pool, versus the 8,800 odd investors Harmoney has on its books today. Hence the disclaimers Myles has put in the report. Still, we got input from loan pickers and index buyers to some extent, and I'm not holding my breath that Harmoney will (ever) publish default info by individual variables for its whole dataset. So, we've got the next best thing.

    What I find interesting also, is that Myles' efforts at cleaning and reporting data put Harmoney's output-to-date to shame. Four years, and they still have the type of data errors humvee recently reported. They also lack in investor education and communication, especially in the area of protect loans. But overwriting/hiding rewritten status/info of defaulting loans, I find absolutely appalling and unforgivably misleading, bordering on fraud. I hope they desist and rectify the wrong they have done - as it impacts on investment decision making.
    I wonder whether its possible that rewritten loan data can be fixed going forward. I recall a thread some time ago, someone mentioned that employment status changed part way through a loan, which means that the data we are viewing is taken from a live relational database - eg any of the "client data" links such as employment etc is subject to change at any point in time.

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