Thursday, July 18, 2013

Lending Club: Adverse Selection

In my last post I claimed that competitiveness in P2P lending is probably not an issue. I now am of the opinion that it is. Let's have a look at this post. We can see that institutional investors are indeed using API investing to grab loans quickly. It of course makes no sense that they would have set up investing bots to grab notes unless they were interested in patiently cherry-picking on a daily basis.

On my end, I was initially surprised to see that while there were many examples of well-performing loans in historical data, there were none to be found to be found still in funding.

Therefore it becomes essential to create a bot to automatically invest in loans. However, API access is sadly (and rather unfairly) limited to institutional investors and bloggers.
My workaround for this unfortunate situation was to use Twitter's Stream API, pointed here. PeerCube's blogger has API access, and has set up some sort of script that provides pretty much instant notifications about the appearance of new loans. This is great since without this I would probably have to resort to constantly querying the Lending Club website during release times and having to worry about a IP address ban. So I just tell my bot to download the new loan data and analyze it whenever the twitter account updates. I'm sure the API investors still have a huge advantage in speed, but it's the best I can do.

I do wonder what kind of models the institutional investors use, and what the extent of adverse selection I am being exposed to. I can say for sure that the uninformed investor is suffering a great deal of adverse selection, as all the higher grade loans that remain have poor scores according to my model. This means they have two strikes against them: my model says they're bad, and that they're still unfunded means that the institutional investor's models say they're bad. As a result, investors will tend to see substantially lower returns than what the Lending Club website says is an average return.

My guess is that high risk/return loans are either great or terrible, and low risk/low return loans are mediocre, in which case the uninformed investor would be better off sticking with the safer loans, so as to avoid the selection effect. I haven't and don't intend to try to gather data to test this guess. I'll explain theoretical justifications for it in a future post, though.

1 comment:

  1. Thanks for the mention of PeerCube twitter stream. I set this up to help LC lenders find out when new loans are posted as well collect data on new loans and total loans available to check the validity of claims of desirable loans being snapped up soon after new loans posting.

    Personally, based on the data I have collected I don't see a lot of new loans getting funded quickly. On average new loans are taking about 6 hours to fully fund. One of these days, I need to sit down and analyze this data in detail and post my findings.

    BTW, to reduce the noise level in twitter stream I have a minimum threshold for new loans, only above which tweet goes out. I am sure followers wouldn't want to receive tweet when only 1 or 2 new loans are posted.

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