Thursday, May 10, 2012

Crowdfunding and Lending Club

Overview

There are lot of different type of crowdfunding sites online where people come together to support non-profits (Kiva), projects (Kickstarter), and borrow and lend money (Lending Club, Prosper). With the crowdfunding portion of JOBS act, I believe crowdfunding will become more popular in a year or two.

Last year, while discussing the negligible interest rate on savings, an EMBA classmate brought my attention to Lending Club where lenders can earn attractive returns on their notes while borrowers can get unsecured loans, similar to credit cards, at attractive interest rates. In fact, LC loan statistics show that 68.50% of borrowers use the loan to consolidate debt or pay off their credit cards and 91.73% of lenders with 800+ notes earn returns between 6% and 18%.

Lending Club

As I looked more into Lending Club, it appeared to be a very attractive alternative investment option. Having participated in equity market for almost two decades, it was my first opportunity to participate directly in debt market specially in consumer loans. LC also provides the export of their historical loan database and I saw the opportunity to use it to refine my knowledge of statistical and data mining tools and techniques.

One of the main risk from lenders' perspective is the risk borrower may not repay their loans. As there is no collateral for the loan (unsecured), there is very little recourse in such events other than relying on LC's recovery process. Though LC claims the overall annualized default rate is below 3%, objective of most lenders is to identify parameters that influence higher default rate or minimize default rate.

Goals

Recently, I thought why not share with my blog readers my analysis of the LC historical loan data, as analysis progresses.  This may enable instant feedback and improvement in analysis and techniques.

The specific goals of historical loan data analysis are:

  1. Identify parameters that influence higher default rate or minimize default rate.
  2. Identify lending strategies that result in greater return compared to a randomly selected lending portfolio.
  3. Compute excess return versus excess risks in comparison to a benchmark.
Can you think of any other goals that should be included for such analysis?

I plan to use Microsoft Excel, Python, R, Google Fusion Tables, and Tableau Public as the basic tools for analysis and visualization. Any tips and tricks in using these tools are welcome.

In the next post, I will review the data contained in the loan database and define the default rate.

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3 comments:

  1. I am certainly interested in finding out your results. The bottom line that most investors want to know is what loans will produce the best returns. I am willing to take more risk on the higher interest loans even with a high default rate if this results in higher returns. I believe the analysis confirms this. I will be interested to see if you come up with the same conclusion.

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  2. I'd be very interested in seeing your results, as well. Peter is right on that a portfolio that focuses strictly on minimizing the rate of default would likely underperform one whose goal is to maximize returns. So, a benchmark I'd be interested in seeing is - when looking for loans what interest rate range should I be targeting (low to mid-twenties)? What is an acceptable rate of default for those loans, which would allow me to see above average returns (5%)? Lastly, how many loans fitting that criteria make up the entire portfolio of loans issued by Lending Club?

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    Replies
    1. Brady and Peter, thanks for comments from both of you.

      For every variable including credit grade and interest rate, I plan to segment default rates. My goal is the segment each variable and determine how I can minimize the default rate which in turn will increase the net return.

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