How to calculate p2p platform risk Part 1

Measuring the risk in any investment is very important. While much material is found to measure risk in traditional investments (stocks, bonds, etf) it is not easy to find risk measures in p2p investments. In this post I will try to provide material and risk measures for this type of investment

What is an investment in a p2p platform

The p2p investment we have 3 actors a borrower who needs a loan a lender who lends his money and a web platform through which the deal takes place. The most obvious risk is that the borrower does not pay or the lender. To eliminate this type of risk, many platforms offer a “buy back” formality so that if the borrower does not pay, the p2p platform will pay the lender.
Apparently it might seem like a zero risk investment but it is not.
The platform could fail or could implement a ponzi scheme. The p2p platforms are very young and it is often difficult to quantify the platform risk.

Some examples of platform defaults

The most recent default that unfortunately also saw me involved is the failure of Eurocent. Eurocent, a Polish lender that issued loans on Mintos failed, and is being liquidated. It appears that this was mainly a result of poor lending quality, which led to funding difficulties. Collateral UK, a British lender that operated its own P2P site, was closed down by British regulators earlier this year. The circumstances behind this closure have still not been made fully public, but it has led to a substantial risk that investors in this platform will not be able to fully recover the amounts they invested in this site.

How to measure the platform risk

I have found on the internet many times phrases like “p2p lending is too young to have reliable data”. True
But if the data is not available we can still propose a model through which to measure the platform risk. To do this simulation, we would have assumed that we could invest in n p2p platforms all capable of providing “buy back guarantee”. We would also suppose that all platforms are stochastically independent from each other, ie the probability of failure of one is independent of the other.

From my point of view, a p2p platform is a sort of black box in which I invest money and which will then give me a return or it will fail without giving any return. So the return of a p2p platform is given by the product of a bernoulli random variable  which is 0 or 1multiplied by a normal random variable. The random variable of bernoulli is worth 1 if the platform is alive at the end of the year, 0 if during the year it has failed. So my return if the platform fails year (Bernoulli = 0) will be 0 * interest rate * invested capital = 0.

if instead the platform survives (Bernoulli 1) my return will be 1 * interest rate * invested capital.

At the end of each year I will invest a new additional sum, the savings made during the year that will be distributed equally among the p2p platforms. I will carry out the analysis with 4 platforms and with 100 platforms to measure how diversification can change the risk of an investment. After that I used a Montecarlo simulation

How Monte Carlo Simulation Works

Monte Carlo simulation performs risk analysis by building models of possible results by substituting a range of values—a probability distribution—for any factor that has inherent uncertainty. It then calculates results over and over, each time using a different set of random values from the probability functions. Depending upon the number of uncertainties and the ranges specified for them, a Monte Carlo simulation could involve thousands or tens of thousands of recalculations before it is complete. Monte Carlo simulation produces distributions of possible outcome values. By using probability distributions, variables can have different probabilities of different outcomes occurring. Probability distributions are a much more realistic way of describing uncertainty in variables of a risk analysis.

The tools used

To perform the simulation I used two tools: Excel and an addin en addin risk amp
This add in easily allows you to perform a simulation montecarlo on an excel model. Excellent tool unfortunately paid. I then used the trial version

The simulation lasts 5 years, starting with a value of 100,000 distributed evenly across the different platforms. the platforms have a normal distribution and the probability of retura  a platform to remain alive during the year is 90%.

Number of year 5
Starting Balance  $ 100.000,00
Expected return for each platform 10,00%
Standard deviation of return 1,00%
Added Balance every year  $   12.000,00
probability of survival for each platform 90%

curious to know the results? you will know a little patience in the next post


Leave a Reply

Your email address will not be published. Required fields are marked *