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12-10-2018, 01:49 PM
#3841
yeah, nah
Originally Posted by beacon
And I'd do the same for the separate PP dataset beginning from the date of the first PP loan.
Have done the PP one - see note on side of graph.
I'm tempted to just go with all loans from start to current, the watering down effect is likely to be minimal and one would expect it would effect everything by the same proportion (assuming similar loan characteristics throughout the year)?
Happy to go with the flow with this, just don't want to over complicate things too early
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12-10-2018, 01:57 PM
#3842
yeah, nah
Originally Posted by RMJH
When you quote default rates, are these %'s by loan numbers or $ values ?
Numbers, can't do value on a merged data set. (everyone invests different amounts in individual loans - the merged unique dataset is made up of values from different lenders)
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12-10-2018, 02:14 PM
#3843
Member
Originally Posted by myles
Numbers, can't do value on a merged data set. (everyone invests different amounts in individual loans - the merged unique dataset is made up of values from different lenders)
Thanks, I'm clearly a bit behind some of you guys! I guess to an extent using numbers compensates for the young loans included though the figures should be viewed as comparative (for setting filters) rather than absolute (for calculating returns) and are not directly comparable with HM's.
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12-10-2018, 02:38 PM
#3844
Member
Hi Myles
How many loans exist in the combined data set with the each of the following problems
Negative principal outstanding
Paid Off/Cancelled - but still owe money
Active/Arrears but $0.00 principal owing
As I assume others have the same problems as I have had with data quality - Or maybe even more concerning - if this data is correct
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12-10-2018, 03:59 PM
#3845
yeah, nah
Originally Posted by humvee
How many loans exist in the combined data set with the each of the following problems
Negative principal outstanding: 49 (-$152.13)
Paid Off/Cancelled - but still owe money: 248 ($1350.66)
Active/Arrears but $0.00 principal owing: 8
Some of this could be due to timing, small rounding type errors, and there will be duplicates? Doesn't look excessive for the size of the data set?
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12-10-2018, 04:07 PM
#3846
Member
Originally Posted by Cool Bear
I just had a quick look at my defaults in HM reports on the website. Of the last 10 charge-offs, 6 had indicated that they were re-writes but had 0 successful payments and 0 remaining payments. Maybe HM just "zeroize" the re-writes when loans are charge-off. So maybe the conclusion that re-writes are safer investments is not correct after all.
Yes all Harmoney charged-off loan that were a rewrite seem to have had all their rewrite data corrupted or changed. All mine have been reset to '0' in the charged-off loan details and N/A in the reports...ScreenHunt.jpg
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12-10-2018, 11:38 PM
#3847
yeah, nah
Thoughts on this style:
Attachment 10063
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13-10-2018, 12:48 AM
#3848
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13-10-2018, 01:03 AM
#3849
Member
Originally Posted by myles
Really good. Thanks Myles.
Appears that HM had trouble grading C and D grades early on in '14. Otherwise looks to be running consistent. Wonder whether there's early signs of improvement in DEF grade defaults out of platform 1.5
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13-10-2018, 02:08 AM
#3850
yeah, nah
Originally Posted by leesal
Will however comment that many of the graph's lack predictive validity, as you haven't controlled for confounders
It was never my intent to run numbers for all combinations, nor to validate the grading system...
By putting together some high level detail, individuals can drill down to 'discover' what they think are key predictors and perhaps share what they find if it is of value. As I said before, a pivot table can be used to do much of that type of work. There is plenty of research available that has covered much of this ground before - whether it applies is the question.
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