The Cold Foot Investor
By: Kosta Tzioumis
Tags: Finance, Investing, Pandas, Data Visualization, Lending Club

Photo by Michael Longmire on Unsplash

I’m not bad with money but I’m not particularly good either. Like most people that live in New York City I have enough trouble just paying my rent so the whole concept of investing has an aspirational quality to it. That I could meet my immediate needs and somehow have extra money?! and that money could somehow become even more extra money?! would be fantastical if I weren’t so sure that, somehow, other people are doing it. But if I were to indulge that fantasy for a moment and become and investor! how would I even go about it?

The investing world is intimidating, there are a lot of ways to invest my imaginary extra money and like all novices I am terrified of making the wrong decisions but I’m also excited (and just a little bit greedy). But stock markets are volatile and property is unrealistic even for my imaginary extra money so, going slowly and carefully, I am going to use data science (with just add a dash of realism to wash the dollar signs from eyes) to take a hard look at LendingClub.

LendingClub is a peer-to-peer lending exchange. I wouldn’t be investing my imaginary money in faceless corporations that destroy the planet; I’d be lending it to real people that will use it to improve their lives (hopefully not by destroying the planet). More succinctly:

LendingClub enables borrowers to create unsecured personal loans between $1,000 and $40,000. The standard loan period is three years. Investors can search and browse the loan listings on LendingClub website and select loans that they want to invest in based on the information supplied about the borrower, amount of loan, loan grade, and loan purpose. Investors make money from interest. LendingClub makes money by charging borrowers an origination fee and investors a service fee.

-Wikipedia

Maybe a loan shark but a nice kind of loan shark.

LendingClub makes a large quantity of data available on past and current loans and repository of loan data spanning 2007 to 2018 is available from kaggle.com here. So let me so what I’m imagining myself getting into.

The dataset contains 2.26 million loans with 145 features. The full feature list is expansive and goes into detail of the borrower’s financial situation as well as conditions of the loan and its repayment. I just care about how much imaginary money I‘m going to pretend to make, how can I figure that out?

The basic premise is simple enough I lend out my imaginary money, the pay it back with interest, the interest is my profit. The devil is ALWAYS in the details. Loans come in 3 or 5 year terms and are graded that A-F with interest rate ranges that increase as the letter increases.

More interest means more profit and the expected total repayment on a loan can be calculated with the following formula:

courtesy of 1728.com

All the above components are known for each loan from the dataset features ‘int_rate’, ‘term’ and ‘loan_amnt’, so calculating my imaginary return is simple but is it realistic? Can I really just loan my imaginary money out and then 3 years later I have more imaginary money? Is that what has happened on LendingClub’s loans? Many of the loans represented in this dataset are still current, payments are still being made and the final return on the loan cannot be determined so I will only consider the loans with a finalized ‘loan_status’ feature of either “Charged Off” or “Fully Paid”

Completed Loans ONLY

Half of all are paid back with more than an 11% profit to the investor (Good!). On average the return is less impressive with just 3.36% profit to the investor from the loan. What is scary though is that some of the loans are not being paid back in full, some are not being paid back at all! Risk! Aaaaaaaaahhh!

Presuming that the loan is going to be paid back perfectly on time and with the interest is unfortunately idealistic and naive. Things happen, life is complicated, investing ALWAYS carries risk. But risk can be measured and minimized. So which loans are not all they’re cracked up to be?

There is no perfect group of borrowers but loans are graded by their risk and shorter term loans perform better than longer term loans (less chance for things to go wrong?). Interestingly some of these loans return at a HIGHER rate than expected through our simple calculation. This could be due to late fees , loan extensions or temporary underpayment that eventually lead to the loan costing the borrower more than expected.

Generally though, A and B graded loans at 36 month terms are the closest performers to expectation. So lets look at the actual investment return for all of these loans and see what I can actually expect from my imaginary investment

LendingClub promoted return from their website

The high interest rate loans of grade G that could be so enticing are (unsurprisingly) a bad investment. Every loan grade and term has some loans that have a -100% return on the investment. Nothing was repaid and the investor is just left empty handed. This appears to be more rare on A and B grade loans where the interquartile range of the box and the mean and medians are nice and far from that 0% return line where I start losing my imaginary money. This is where it is important to note the LendingClub does not require you to fund all of an individual loan. They actually discourage it. LendingClub allows you to diversify, funding small fractions of many loans, called notes, spreading out the risk and offsetting any losses from loans not repaid in full.

This is all imaginary so I’m still on board and I’m willing to try it out! Even with my imaginary money I am a conservative investor, I am only going to take notes from A or B graded loans and I am going to diversify like crazy. 1 imaginary dollar (i$) each in to 1,000 loans. ( LendingClub requires a minimum of $25 (real dollars) per note but whatever, this is imaginary). I’m also going to try to be just slightly realistic. I am going to limit myself to loans originated in the year 2013 with a term of 36 months. These are relatively recent loans that all should have fully completed their repayment or been written off as a loss by the time this dataset was compiled at the end of 2018. There are 54,107 leans that fit my stringent criteria but I am only going to randomly select 1,000 and determine the total payment divided by the loan amount for each loan and sum the total.

df_sim=df.loc[(df[‘issue_d’]>’2013')&(df[‘issue_d’]<’2014')].loc[(df[‘grade’]==’A’)|(df[‘grade’]==’B’)].loc[df[‘term’]==36][[‘loan_amnt’,’total_pymnt’]].sample(n=1000,random_state=44) ((df_sim[‘total_pymnt’])/df_sim[‘loan_amnt’]).sum()

i$1104.76!! An impressive return of over 10% from my initial i$1000 investment. My diversification strategy and high graded loans appears to have worked but also maybe this was just a fluke. So I’m going to try again, and again, and again. 1,000 times (just to be sure). Yes, I am performing a Monte Carlo simulation.

My investment scenarios return a range of 8.5% to 11.4% return approximately normally distributed around a mean of 10.1%. It is important to note that this is not an annual return but a return over the course of the loan which in the above scenarios is 36 months. However, LendingClub do not make you wait that long and pay returns on notes purchased by investors on a monthly basis.

So, I think I’ve identified an investment strategy with a modest return rate and low risk that also includes a warm fuzzy feeling from helping people get loans that they need to improve their lives, start businesses, get married, consolidate debt and do all kinds of wonderful things. Now all I need to do is replace my imaginary money with real money!

Full python notebook linked below: