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Effect of Previous Delinquencies on Probability of Default

Started by Peter, December 14, 2015, 11:00:00 PM

Previous topic - Next topic

dompazz

I've just started digging into the historic data provided by Lending Club.  I'm currently looking at 36 month notes and only on the data prior to 2015.

I had assumed the the variable delinq_2yrs would be positively correlated with defaults.  I'm finding exactly the opposite.

DefaultnLoans
0

Fred

I think a few people in this forum has done extensive "factor analysis"-- https://en.wikipedia.org/wiki/Factor_analysis" class="bbc_link" target="_blank">https://en.wikipedia.org/wiki/Factor_analysis -- on all the attributes in LC historical data.  Not sure if delinq_2yrs really has a negative coefficient, and if it is statistically significant.

However, if you really think your analysis is correct, you should follow your numbers instead of your assumptions -- i.e., buy LC loans with delinq_2yrs > 0.

Good luck.

TravelingPennies

I've run some basic logistic regressions, sampled and not, and with other factors included, the coeficient is significantly negative.  As a modeler, anything that doesn't make economic sense, I would generally toss out.  Trying to understand if there is a world where this makes sense.

One thing I have come up with is LC's underwriting possibly rejects borrowers with higher delinquencies with a higher probability to default.  Those that make it through are better credit risks and have a lower probability to default.

jennrod12

If I read that right, you are looking at loans that defaulted and what % of them were from borrowers with delinquincies?

Did you look at all loans from borrowers with delinquincies and what % of them defaulted?

Jenn


TravelingPennies

Interesting info,

Is there a difference when looked at by the borrower's number of delinquencies or how many months since the last delinquency?

Jenn




hoggy1

As I understand it, a Delinquency is 30 or more days late and I don't count one in the last two years against the borrower. By contrast a major derogatory is more than 120 days late and I don't buy notes with any.


brycemason

Being stumped by covariance is so 2011 on this forum, but you caught me in a good mood after a pleasant 16-hour day of building loan servicing logic. Data: LC 9/30/2015 extract, matured policy code 1 36-month loans between 3 and 5 years old (out of convenience); various other exclusions I'll cite later.

Define prior_delinq to be 1 just in case delinq_2yrs is greater than 0. Useful practice because of the few data points past 1 anyway. Avoids estimation issues.

. tab delinq_2yrs

delinq_2yrs |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |     39,617       89.42       89.42
          1 |      3,493        7.88       97.31
          2 |        779        1.76       99.07
          3 |        243        0.55       99.62
          4 |         73        0.16       99.78
          5 |         47        0.11       99.89
          6 |         23        0.05       99.94
          7 |         12        0.03       99.97
          8 |          3        0.01       99.97
          9 |          4        0.01       99.98
         10 |          3        0.01       99.99
         11 |          4        0.01      100.00
         18 |          1        0.00      100.00
------------+-----------------------------------
      Total |     44,302      100.00


Observation (single-variable analysis): Having a prior delinquency increases the chance of a charge off event.


Logistic regression                               Number of obs   =      44302
                                                  LR chi2(1)      =      27.03
                                                  Prob > chi2     =     0.0000
Log likelihood = -16924.115                       Pseudo R2       =     0.0008

------------------------------------------------------------------------------
   chargeoff |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
prior_delinq |   .2302795    .043396     5.31   0.000      .145225    .3153341
       _cons |   -1.94617    .015193  -128.10   0.000    -1.975947   -1.916392
------------------------------------------------------------------------------


Observation (Multi-variate analysis): This relationship vanishes when controlling for the four horsemen of the consumer credit scoring apocalypse.

Logistic regression                               Number of obs   =      44302
                                                  LR chi2(5)      =    1575.80
                                                  Prob > chi2     =     0.0000
Log likelihood = -16149.729                       Pseudo R2       =     0.0465

--------------------------------------------------------------------------------
     chargeoff |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------+----------------------------------------------------------------
  prior_delinq |  -.0021604    .044922    -0.05   0.962    -.0902059    .0858851
fico_range_low |  -.0153585   .0005158   -29.78   0.000    -.0163695   -.0143476
inq_last_6mths |   .1900362    .013459    14.12   0.000      .163657    .2164155
emp_na_slf_ret |   .4404685   .0522608     8.43   0.000     .3380393    .5428978
      loan2inc |   2.286919   .1299917    17.59   0.000      2.03214    2.541698
         _cons |   8.213554   .3604598    22.79   0.000     7.507065    8.920042
--------------------------------------------------------------------------------






TravelingPennies

You are super welcome! Don't take my snark personally https://forum.lendacademy.com/Smileys/default/smiley.gif" alt=":)" title="Smiley" class="smiley" />.

Peter

*giggle*  Welcome, Dompazz.  Stick around, I think you're gonna fit in just fine! https://forum.lendacademy.com/Smileys/default/smiley.gif" alt=":)" title="Smiley" class="smiley" />
Publisher of the Lend Academy blog

See my returns here: http://www.lendacademy.com/returns

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