## How to calculate p2p platform risk Part 2

In the previous post I told you how it is possible through a model to describe the platform risk of a p2p investment.  I modeled the return of a p2p platform as the product of a Poisson variable that is 0 or 1 times the value of a normal variable that represents the real performance of the platform.  The return was modeled as the result of a normal random variable with an average value of 10% and a 2% variance.  Why a normal distribution? because Central Limit Theorem. Mean and variance have been calculated empirically.

An important value is the probability that a platform fails in one year. I did two simulations the first with a probability of failure  equal to 10% and one with a probability of failure of 5%.

I created the model with excel and generated 5000 simulations using 4 platforms or 100 platforms. Her the results

## First scenario: default probability 10%

4 p2p platform

Min Return -79%
Max Return 11%
Average -3%

100 p2p platform

Min Return -8,451%
Max Return 3,782%
Average -1,074%

The first thing that initially surprised me is that increasing the number of platforms reduces both the minimum and maximum returns. Intuitively the explanation derives from the fact that increasing the number of platforms increases the probability that at least one goes into default. If the probability of default is 10% with 100 platforms almost every year, one of these will default.
With 4 platforms it could happen that in five years none goes into default (maximum return). With 100 platforms this has never happened. Of course in the case of a defalt the damage is low if we have 100 p2p platform.

##### Probability distribution with 4 p2p platform and default probability 10%

In this simulation the probability distribution is as follows. In 83% of the cases the return was greater than -10%  and only  46% had a positive return. I would say a very risky investment

##### Probability distribution with 100 p2p platform and default probability 10%

Surprising the result with 100 p2p platforms

All the simulations had a return higher than -10% (great!) and 99% greater than -5% (wow). However, the probability of having a positive return is less. WHAT?  Adding p2p platforms reduces variance and returns are all close to the average value. Which in this case is negative.

This result were shocking for me. I always thought that increasing the number of platforms decreased the risk of a negative return. This is not true. Let’s see what happens if we reduce the probability of default to 5%

## Second Scenario: default probability 5%

4 p2p platform

Min Return -66%
Max Return 11%
Average 4%

In this case things went much better. The worst performance has improved and the average has also improved. Maximum performance has not improved.

100 p2p platform

Min Return -0,215%
Max Return 8,370%
Average 4,477%

In this case things are much better. I have a slightly negative minimum return Even in this case positive average

## Comparing the result

As I expected the performance in the case of 100 platforms is very close to the average. In the case of 4 platforms it is distributed in a less uniform way both downwards and upwards.

Conclusions
Increasing the number of p2p platforms brings the results closer to the average return. There are fewer deviations and the yield is more easily predictable. So diversification is important but it is an insufficient weapon to defend ourselves from all risks. It is very important that the platform risk is low and that loan originators are reliable. This is why I believe that p2p platform like Mintos (reliable and market leader) should have a significant percentage in all portfolios

## 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

## E-Fire model: a new model for financial independence

The fire movement suggests a very simple model to free oneself from the slavery of work. Multiply your annual expenses between 300 and 400 and you will have the amount that, if invested in low-cost etf will allow you to live a lifetime. In my case the annual expenses are 24k euros so I need a figure between 600k and 800k  euto to be financial independent. So, as shown in the following figure, you must first create your nest egg and then from this, withdraw the money you need.

There are two main criticisms of this model, the first which is a model for the rich. Indeed 600k-800k euros are a huge sum for me.

Secondly in this  model you could be a victim  of “Capital Depletion”. Not knowing the performance of the stock or bond markets you could remain without money. It is certainly not necessary to be a “wolf of wall street”  to understand that if you  you are so unlucky to start your journey at the wrong time you could remain without money.

There are many solutions that can make this model less rigid but I would like to introduce a new variable.

Today the Fintech world offers us important new opportunities such as p2p lending. In this model, the investment takes place both in low-cost etf but also in p2p platform that do not suffer from the fluctuating trend of the stock exchanges but have a constant return (around 10% at the time of writing).

In this way it is possible to balance the fluctuating trend of the stock exchanges by investing in the p2p platforms, withdrawing the capital from the ETFs when the stock exchanges are high and vice versa by investing in the stock exchanges when they are low withdrawing the capital from the p2p platforms.
In practice, a flow is generated as shown in the following figure.

I called this model e-fire model where it stands for European as this model will be described in the next posts only with tools easily available for a European citizen.
Stay tuned

## What’s P2P Lending?

Peer-to-peer (P2P) lending is a method of debt financing that enables individuals to borrow and lend money without the use of an official financial institution as an intermediary. Peer-to-peer lending removes the middleman from the process, but it also involves more time, effort and risk than the general brick-and-mortar lending scenarios. P2P lending is also known as social lending or crowdlending

Traditionally, individuals and small businesses who want a loan usually apply for one through the bank. The bank would run extensive financial checks on the applicant’s credit history to determine if the entity would qualify for a loan and if yes, determines the interest rate that will be charged on the loan. Individuals that want to avoid being charged high interest rates or that would otherwise be rejected for a loan application due to poor credit history, may opt for an alternative way of borrowing funds – peer-to-peer lending.

With peer-to-peer lending, borrowers take loans from individual investors who are willing to lend their own money for an agreed interest rate. The profile of a borrower is usually displayed on a peer-to-peer online platform where investors can assess these profiles to determine whether they would want to risk lending money to a borrower. A borrower might receive the full loan amount or only a portion of what he asked for from an investor. In the case of the latter, the remaining portion of the loan may be funded by one or more investors in the peer lending marketplace. In peer-to-peer lending, a loan may have multiple sources and monthly repayment has to be made to each of the individual sources.

Today there are many platforms that allow you to get profits around 10%

## The meaning of “passive income”

A few days ago I wrote in a forum that a real estate investment (rent a house for example) is a passive investment.
This statement has triggered a series of unpleasant comments to me.
Many argued that a real estate investment was not entirely passive
What does “passive income” mean?
I work 40 hours a week.  If I count travel, I spend 30 minutes to go to work and another 30 to go home. At least two days a week I have to go to customers so instead of 60 minutes I have about 180 minutes. So travel is about 9 hours a week

Often I have to do unpaid overtime say on average 3 hours a week.
This is the time I spend at work:

• Ordinary work    40 per week
• Trave 9 hours per week
• Extraordinary 3 hours

Total 52 hours per week
I do not think my situation is different from that of many of you.

But if you have for example 4 houses with a rent of 500 euros per month.

How long will it take for managing your business? 8 hours a month for each house? 16 hours a month for each house?
So  from my point of view a real estate investment is passive.because the time I waste is very small
Probably things would improve if instead of real estate I had an investment of ETF and apply a strategy “Buy and hold”.

In this case, probably the wasted time ta would be a few hours a month.

From my point of view, financial independence means making money work for you in order to free up enough time to be happier so a real estate investment could be a choice

## What I will do when…

These days I’m on vacation at the beach at Caorle a nice place near Venice. The sea is always a source of inspiration. So I started thinking about what I will do when I’ll be financially independent. I would like to take care of start-ups and dedicate my time and my skills to help create new companies. It ‘what I would have liked to do but I did not because I always needed money to carry on with my family and to support me. Working for a startup means the uncertainty of gain and this is a luxury that until now I could not afford

## The mith of 4% Safe withdrawal rate

The safe withdrawal rate (SWR) method is one that retirees use to determine how much they can withdraw from their accounts each year without running out of money before reaching the end of their lives. The safe withdrawal rate method is a conservative approach that tries to balance having enough money to live comfortably with not depleting retirement savings prematurely.

The first study come from financial planner William Bengen. Essentially, Bengen tested a variety of withdrawal rates using historical rates of returns for stocks and bonds. He found that 4% was the highest withdrawal rate retirees could use if they wanted their money to last at least 30 years, assuming they invested at least 50% of their savings in stocks. The 4% rule quickly became the default withdrawal rate for retirees who wanted to make sure that their retirement nest egg would be around as long as they were.

But today what withdrawal rate can be reasonable?   I tried to give an answer looking at this site

You can simulate the expected real return of different asset in next 10 years

As you can see there isn’t an asset  with expected return above 6%. But if you change from expected to historical data expected return are much higher

So with  4% today probably is unrealistic

## My vision of Financial Independence

In my opinion there are many different visions of the concept of “financial independece”. You will find many blogs that talk about becoming millionaires in a few years others who will give you tips on how to save and live with little money. In my opinion both these visions are not correct.
Becoming millionaires in a few years is very unlikely, there are no magic recipes.
We live only once. the object of “financial independece” is to improve the quality of life. Today, compared to yesterday we have available new tools such as Peer to peer lending and crypto currencies that can help us integrate with classical instruments (bonds and shares). In the next posts I will tell you my experience