Lending Club update

Things are moving slowly on the Lending Club front. As I wrote a few posts back I had a major problem when I turned on automated investing to fill one of the test cases. It immediately purchased 151 notes and drained the cash in my account down to nothing. I initiated a process with Lending Club to undo this mistake but they indicated that it would take 6-8 weeks to do. Meanwhile I was out of cash in my IRA Lending Club account.

This has made acquiring new notes for the test cases difficult. I can only invest cash as it comes in on my previous notes. This results in acquiring only 2-3 notes per day. It also is going to lessen the value of reporting on the test cases as they will have notes of more varied age than I hoped for. Thus, any measurements will be somewhat skewed,

I have decided to keep the 151 notes acquired during th automated investing fiasco. The notes that I have produce a somewhat steady cash flow, although not a high return on investment. At my current stage in life this is a reasonable choice for me.

I’ve also started to look art selling some of the longer-term notes on the secondary market FOLIOfn. I have some terminating in 2021 and will consider selling these to be able to totally get out by 2020. I’ve had limited experience trying to sell notes and am not overly optimistic. Right now, there are 320,000 notes available on FOLIOfn. I suspect the only way to sell many notes would be with deep discounts. I’ll report on my experiences with this soon.

More test cases

New notes are added to Lending Club 4 times a day. Some of these disappear very quickly. So quickly, that it is obvious that they are being selected by some automated software. As we see in test case 3, one such advisor LCPicks, can react very quickly and has a model for rating the newly released loans. One can assume there are other such investors out there doing precisely the same thing. Further each one of these probably uses a different model for selecting the loans. So using a concept that has been successful in sports wagering, I decided to take advantage of this.

I download the new notes shortly after another release and only look at the newly added notes. I wrote a program that does some calculations and sorts. I then piggyback on those notes that are going quickly on the assumption that they are the collective wisdom of the other investor’s models.

Test case 5 is made up of notes that had over 90% of their initial amount purchased in a short period of time. While implementing this I noticed that this was mainly made up of smaller notes because investment moved up their percentage rapidly. So I also added test case 6 which included notes with funding over 50% and the amount funded over $10,000.

Test case 7 is a control group. It was intended to be made up of those selected by Automated Investing. But as indicated in a previous post, I screwed that up so elected to select notes for this group randomly based on the last 2 digits of the note number. This then leaves us with the following 7 test cases.

Test Case 1 – Buy notes on FOLIOfn finishing in 2017.

Test Case 2 – Buy notes on FOLIOfn with less than 12 months to go.

Test Case 3 – Use an advisory service, LC Picks.

Test Case 4 – Use my Zip/Grade screening process.

Test Case 5 – Acquire new notes selling fast by percent invested.

Test Case 6 – Acquire new notes with lots of earl $ invested.

Test Case 7 – Control group of randomly selected notes.

I will be developing a reporting mechanism on the progress of these tests. Follow me on Twitter, @billlanke, to be notified when my next post appears.

Test Case 4 Defined

Test case 4 uses the technique I mentioned back in a previous post on July 17^th. I built a match code that combined the first 3 digits of the potential borrower’s zip code and the grade and sub-grade of the loan. I built a table of the possible combinations and used all the previous note history to save the computed returns on these notes. I then compared the newly released notes to this table. I ignored notes that had less than 100 previous notes in this match code, and only selected those that had returned at least 110% of the initial investment.