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New (Especially Quality) Notes Really Trailing Off?

Started by Peter, November 18, 2014, 11:00:00 PM

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avid investor

Have had to lower the APRs of the loans I am funding to get anything that passes through my (admittedly stringent) filters.  Sideline cash was piling up.  But noticing that each feeding time only sees about 100+ new loans being dropped.  Frequently, 0 to 1 of those make it through my filters.  Anybody else having trouble staying invested at good return rates?  I don't do any A's or lower interest B's.


Yep most definitely last few days there has been a drop off in decent notes, but this happens in waves periodically, so nothing alarming yet.


I do mostly Es.  Yes to the same problems as above.  I noticed my last E1 at 18.99 was  on Oct. 23, and my first E1 at 18.54 on Nov. 2.  Don't know how the other E APRs compare, but they all seem lower.  That's good news for borrowers.  Now they can always make their payments on time, and I love when that happens!


By quality, I think you may mean mispriced.   If that's the case, one should never get used to relying on mispricing to last forever.  What you label as good may not be a realistic return.


"To answer your question, you really need to have the probability of default estimate produced by the model.  You then can use this to determine whether something is priced too high or too low. --rawraw"

Thanks for reminding me.  It's easy to click on the individual loans in the PC Similar Risk box and then note the default rates, although I'm not sure if they were model produced.  I haven't paid enough attention to that.  I was letting my eyes skip over the default numbers.
Thank you.


As I have never discussed Loans with Similar Risk Profile feature, I just wanted to put more color on how PeerCube determines the 'Loans with Similar Risk Profile'. PeerCube uses a clustering algorithm to determine what loans are similar to each other. The Loans with Similar Risk Profile box displays the five closest loans similar to the loan being viewed. There are multiple ways to use this for making lending decision.

  • If your filter selected a loan, by reviewing Loans with Similar Risk Profile, you will be able to select similar loans that may not have met some of your filter criteria but are still very similar to the loan selected by filter according to the algorithm. It may just have missed a few selection criteria of your filter. This may works well when your filter is very tight and doesn't find enough loans on its own in lieu of cash buildup. The main drawback is that you will be building a portfolio of very concentrated loans that are similar to each other so they may move in tandem.
  • If you prefer a very diversified portfolio, you can use the Loans with Similar Risk Profile to avoid investing in loans similar to one you are viewing.
  • If you are after higher return, select the loan with higher interest rate among the Loans with Similar Risk Profile and the one you are viewing. For example, if you are viewing a C2 Grade loan and Loans with Similar Risk Profile displays a E4 Grade loan, you may consider selecting E4 loan that will give you higher potential return for similar risk profile.

In the past, a few PeerCube users mentioned that they leverage Loans with Similar Risk Profile feature by using a combination of method 1 and 3."> from: Lovinglifestyle on November 13, 2014, 04:22:47 PM


Is the cluster analysis done on just the credit variables or does it factor in past performance of those combination of credit variables?  If I recall, cluster analysis is often used to find similar groups of notes but generally a firm then has a cluster specific scorecard to evaluate the credit of notes within that cluster.   But I'm not very familiar with cluster analysis, so appreciate any insight on how it works


The clustering algorithm takes into consideration the probability of default from past loans as weight for distance estimates. This enables the important variables to have more influence when doing similarity analysis. And, this approximation avoids two step analysis of first identifying cluster and then evaluating credit quality of cluster. The speed is more important as calculations are performed in real-time."> from: rawraw on November 14, 2014, 06:18:14 AM

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