P2P Lending / NFT Lending Forum

So I've been playing with LC data these past few days.  I've never liked the fact that LC doesn't provide the whole monthly history of the notes.  So I wanted to try to create a simplified annualized ROI based on the cumulative data given.  I basically want to use the NPV and IRR methods classically used in finance.

Metrics used to calculate IRR

Loan Amount
Payment  = Total Payment to Months On Book Ratio =  Total Payment /  parallel minimum of Term and Months to Last Payment
Periods = Term

The idea behind the Total Payment to Months On Book Ratio is to estimated an loan annuity payment.  The higher the payment the better the note return.  Obviously chargeoffs will lower this payment amount.

Once IRR is calculated I annualize by (1 + r)^12 - 1

This is obviously a simplified method but allows for quicker analysis of notes.  Open to suggestions on improvement.

Attached is a chart which matches somewhat closely to the LC Net Ann Returns.

There are a lot of opportunities to get started in auto finance if you have the right skillsets.  The typical skillsets that auto finance companies look for are STEM degrees and business degrees.  You can easily look up on a job aggregator to see the job descriptions.  Most auto finance companies are like every other company in they want to be able to make data driven decisions about loan applicants ability to repay on loans.  If you don't have previous financial or lending experience I believe you can still get in the door at an analyst or IT developer level and build your experience.  Even if you can't find an auto finance job you can find an analyst job at a bank and learn about credit and lending in that position.  The important things to know in auto finance is credit bureau data and loan portfolio management.

Getting started in predictive modeling is more broad.  There is a huge swath of companies and industries looking for that skill.  In fact even if you current job doesn't require you could probably take it on as as side project and show how your model would help your current organization.  Here is the secret untold story about predictive modeling that most academics do not tell you.  Predictive modeling is 80% data acquisition and management and 20% modeling.  So make sure you are a data skill hawk meaning that you can download, pull, connect, manipulate, slice, dice, warehouse, store, and distribute data.  That means having skills in SQL, Python, R or other data programming tool.   Trust me your bosses would like it even if you can just manage multiple data sources and provide meaningful data analysis.  Chances are the business decision makers in an organization doesn't know how and doesn't know the data exists.   

Getting your chops up in the statistical and applied math of predictive modeling can be done on your own via online learning or in a more structured classroom setting.  Do it in baby steps if you have no applied math background.  Start with basic statistics 101 and move on to more advanced. 

I've built a loan default prediction model with Lending Club about 2 years ago and I've been investing modestly with it since then.  I'm getting about 5.5 to 6% adj. return on my loans.  So I think its working fairly well.  I'm trying to improve the model hopefully one day achieve 10% returns.  I'm wondering if anyone else has built similar models and have come up with creative variable transformations on the historical loan data?  Here are some that I've come up with

loan_to_income = loan amount / income
payment_to_income = installment / income
time_since_earliest_credit_line = earliest credit line date - issue date
open_acc_ratio = open_acc / total_acc
curr_bal_ratio = tot_cur_bal / total_bal_ex_mort

some of these are more or less predictive.  Anyone have any other interesting transforms?

My inspiration for developing a Lending Club model came from LendingRobot  http://blog.lendingrobot.com/research/predicting-the-number-of-payments-in-peer-lending/" class="bbc_link" target="_blank">http://blog.lendingrobot.com/research/predicting-the-number-of-payments-in-peer-lending/

I only started investing in March 2017 but my returns got as low as 2%.  Now it is at 8%.  So yes I believe there has been an improvement.  I'm guessing the improved economy has helped some folks out on making payments.

My adj annualized returns is hovering around 1.8%.  Very low but it was lower than that last month.  Typically end of year is a bad time for loans as a lot of folks don't have as much funds to make payments.  Could be also that collection centers are being staffed up again now that vacations are over.

Here's a thought but I haven't been able to look at the data yet.  Most student loans are installments and there is a lot of them.  Yet typically student loans are deferred until graduation or some other time period after graduation.  So I would imagine a borrower with a high number of installments (num_il_tl) that are not openly active (open_act_il).  Perhaps take the ratio of open_act_il to num_il_tl and look for low ratios.

Anyone else having issues downloading the 2015 lending club loan data?  It seems to be giving me errors.  All of the other csv files download fine.

https://www.lendingclub.com/info/download-data.action" class="bbc_link" target="_blank">https://www.lendingclub.com/info/download-data.action